Published by Psi Chi, The International Honor Society in Psychology ®
PSI CHI JOURNAL OF PSYCHOLOGICAL RESEARCH FALL 2025 | VOLUME 30, NUMBER 3
EDITOR
STEVEN V. ROUSE, PhD
Pepperdine University
Telephone: (310) 506-7959
Email: steve.rouse@psichi.org
ASSOCIATE EDITORS
JENNIFER L. HUGHES, PhD Agnes Scott College
STELLA LOPEZ, PhD University of Texas at San Antonio
TAMMY LOWERY ZACCHILLI, PhD Saint Leo University
ALBEE MENDOZA, PhD Delaware State University
KIMBERLI R. H. TREADWELL, PhD University of Connecticut
ROBERT R. WRIGHT, PhD Brigham Young University-Idaho
EDITOR EMERITUS
DEBI BRANNAN, PhD Western Oregon University
MANAGING EDITOR BRADLEY CANNON
DESIGNER JANET REISS
EDITORIAL ASSISTANT EMMA SULLIVAN
ADVISORY EDITORIAL BOARD
GLENA ANDREWS, PhD RAF Lakenheath USAF Medical Center
AZENETT A. GARZA CABALLERO, PhD Weber State University
MARTIN DOWNING, PhD Lehman College
HEATHER HAAS, PhD University of Montana Western
ALLEN H. KENISTON, PhD University of Wisconsin–Eau Claire
MARIANNE E. LLOYD, PhD Seton Hall University
DONELLE C. POSEY, PhD Washington State University
LISA ROSEN, PhD Texas Women's University
CHRISTINA SINISI, PhD Charleston Southern University
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222
EDITORIAL: Reframing and Reevaluating the First-Generation Variable for Research on Academic Success
Stella Lopez1, Mary McNaughton-Cassill1, Darryll Owens1, Robert R. Wright2, Albee Mendoza3, and Leslie Martinez4
1 Department of Psychology, University of Texas at San Antonio
2 Department of Psychology, Brigham Young University–Idaho
3 Department of Psychology, Delaware State University
4 Department of Psychology, University of The Incarnate Word
228 Emotion Science Video System: Clips With Categorical and Dimensional Ratings
Tyre N. Johnson and Jared J. McGinley* Department of Psychology, Towson University
240 Promoting Equitable Advising Practices: Examining the Impact of Growth Mindset and Appreciative Advising Micro-Messages on Disclosure and Student Success
Zaire I. Preston, Elizabeth C. Yanes, and Tonya M. Buchanan* Department of Psychology, Central Washington University
251 She Wears It How? Perceived Racism and Colorism
Relate to Hair Texture Dissatisfaction in Black Women
Tori A. Elliott, Denise M. Martz*, Doris G. Bazzini*, and Shraddha M. Selani Department of Psychology, Appalachian State University
263 When Feeling Positive Brings Out the Negative: The Impact of Nurturant Love on Perceptions of Ambiguous Situations
Valeria Panameno, Taylor S. Perea, Daniel M. Vasilyev, Samantha Mae C. Flores, Kelly T. Cazinha, and Makenzie J. O’Neil* Department of Psychology, Saint Mary’s College of California
273 COVID-19 and Current Mental Health of College Students
Morgan R. Bodker and Megan M. M. St. Peters* Department of Psychology, Murray State University
(Continued on Next page)
278 “Our Friends and How We Make Them”: How Disordered Eating Tendencies Could Contribute to Friendships Based in Either Competition or Mutual Success
Abigale G. Hartwig
Department of Psychology, University of Wisconsin–La Crosse
288 Mindful Strides: A Brief Associative Attention Intervention to Improve Running Performance Among College Students
Natalie K. Johnson, Madeline E. Adair, Lindsay Johnson, Robert R. Wright*, and Samuel L. Clay* Department of Psychology, Brigham Young University–Idaho
301 Examining the Associations Among Relationship Obsessive-Compulsive Tendencies, Extreme Love Beliefs, and Big Five Personality Traits in a Nonclinical American Sample
Grace A. Thompson1, Indica R. Machost1, Jeremy Tost*1, Clay King*2, and Alexander C. Olson1
1Department of Social and Behavioral Sciences, Colorado Mesa University
2Department of Mathematics, Stonehill College
Reframing and Reevaluating the First-Generation Variable for Research on Academic Success
Stella Lopez1, Mary McNaughton-Cassill1, Darryll Owens1, Robert R. Wright2, Albee Mendoza3, and Leslie Martinez4
1 Department of Psychology, University of Texas at San Antonio
2 Department of Psychology, Brigham Young University–Idaho
3 Department of Psychology, Delaware State University
4 Department of Psychology, University of The Incarnate Word
Research suggests that, similar to previous eras, earning a college degree is a ticket to success by greater job satisfaction and financial security. Unfortunately, approximately 33% of the people who start college do not end up earning a degree (https:// educationdata.org/college dropout rates ; Hanson, 2024), and recent events such as the COVID 19 pandemic have seemed to compound this further, especially regarding students’ health and wellness (Birmingham et al., 2021; Wright et al., 2025). To enhance retention rates, universities have spent a substantial amount of effort to identify the factors that put students at risk for failing to graduate. One factor that is often cited is availability of financial resources to support students’ educational efforts. Although financial means certainly help, other factors that assist in this endeavor include knowledge of university policies, practices, and jargon, which must be experienced to clearly understand and capitalize on available opportunities and resources. These are otherwise known as cultural capital (CresswellYeager, 2021). Although students can utilize these opportunities, it is easier to rely on the advice of people who have experienced and understand the system of higher education. Therefore, it is presumed that parents who attended and earned a degree in college are better positioned to help their children navigate the college experience than those parents who have no firsthand knowledge to share. Collectively, students whose parents have not attended college are referred to as “firstgeneration students” (1G) and, by these implications, face more difficulties in completing college than those who are not 1G students. In its simplest form, students are asked one question about 1G status. The Higher Education Act of 1965 and 1998 defined a 1G student as “a student both of whose parents did not complete a bachelor’s degree, or in the case of students who live with and are supported by only one parent, a student whose only such parent
did not complete a bachelor’s degree.” Thus, the single question to assess 1G status is “Are you the first in your family to go to college?” This item suggests that, if one is the first to attend college, then there is no one in the family who could/would be able to provide guidance to the individual as he or she navigates through college or university life. This guidance is generally assumed as necessary for success in college (Roscigno, et al., 2025). For example, 1G students may not have access to cultural capital if their parents do not know how to apply for college or how to make a college schedule (CresswellYeager, 2021). Furthermore, there would be no parental guidance for growing skills for high academic achievement (e.g., time management, course expectations, test preparation, writing an essay), for building connections on campus (e.g., professors, peers, university staff), for knowing about campus life and social interactions (e.g., dormitory life, student organizations, wellness center), and for finding student resources (e.g., counselors, advisors, tutors, student success centers). Guidance for all these could be provided by the family member who had completed college previously. Presumably, a parent who successfully attended college can offer this needed guidance, while students who are the first person in their family to go to college will be at a disadvantage with reduced access to this firsthand knowledge about college success (Kim et al., 2023; Kim, Armstrong, Freeman, et al., 2024).
Although identifying the 1G student is represented by one simple question, determining who would be given this designation is complicated. To identify students who might be at risk of failing to graduate, most colleges have adopted versions of a 1980s federal law specifying that students should be considered 1G if neither of their parents had completed a college degree. This definition plays a crucial role in eligibility for various federal programs and support initiatives aimed at assisting 1G college students in their educational
journeys. However, attempts to deal with variations in family structure and college attendance have resulted in over 100 definitions of 1G status, which means that the same student may be labeled differently depending on where they are applying to school (referred to as “CAP 2”). The widely used College Common App system uses the 1998 Higher Education Act Amendments’ definition of 1G status, which considers parental attainment of a bachelor’s degree but does not consider nondegree earning school attendance or associate degrees (referred to as “CAP1”).
A 2021 poll indicated that 26% of adults who were considered 1G because their parents were not degree earners complete a bachelor’s degree themselves, whereas approximately 70% complete a similar degree among those who have at least one parent who completed college (Pew Research Center, 2021). Although this is a substantial difference, many have suggested that parental education alone is an incomplete or inadequate criterion. For example, a student whose father completed a college degree would not be considered 1G, even if they were raised exclusively by their noncollegeeducated mother. In contrast, a student whose stepparent played a central role in their decision to go to college but did not actually raise them would still be considered 1G even though they had access to informed support (Kim, 2024). The same might be true of students whose grandparents, aunts, uncles, or older siblings attended college and provide support or advice during their college journey. Moreover, there is evidence that relying on multiple people for varying kinds of support is beneficial, indicating that even students with collegeeducated parents might need more than parental support alone (Hagler, 2023). Noncollegeeducated parents who provide emotional and instrumental support to their children play an essential role in their success, whether they understand the world of higher education or not (Capannola & Johnson, 2020). For instance, many parents who attended college for a few semesters did not finish their degree for myriad reasons. People can learn from failing to reach a goal and still use that experience to help others, and parents in such a position might still have valuable insights to share with their student child. Perhaps, it would be more helpful to ask students who they turned to for advice, what sort of advice that person provided, and how relevant or helpful it was, regardless of family education level. There is also a need to assess the overlap between a student’s college situation and the experiences of the parent who completed a degree (Kim, Armstrong, Freeman, et al., 2024). For example, a parent could have attended college in another country and obtained a degree that is very different from the one the student is pursuing, or they might have attended school before
online availability of college courses. As such, the parent may not be familiar with the specific challenges a student is facing, and if the knowledge gap is large enough, their advice could be counterproductive or harmful. A parent who expects their student to work to pay for college because they did so several decades ago may not realize the difficulty of doing so now, as minimum wage jobs have not kept pace with college costs.
A family’s socioeconomic status (SES) is another important factor to consider in the classification of 1G. 1G status is often associated with the lack of financial resources, which is in itself an impediment to college success (FlanaganBorquez & SorianoSoriano, 2024; Renn, 2024; Wilbur & Roscigno, 2016). However, the lack of a collegeeducated parent does not necessarily mean that the family lives in poverty (Goward, 2018). Families with 1G students are not exclusively low SES. They may have the financial means to purchase professional services including tutoring, assistance with college applications, test preparation, and career guidance tailored to the students’ needs, compensating for the lack of family experience (Wilbur & Roscigno, 2016). On the other side of the continuum, when parents of lowincome students provide them with consistent emotional support, it is associated with positive academic outcomes (Roksa & Kinsley, 2018). Moreover, those enrolling in college following the onset of the COVID19 pandemic may be of a higher SES than before (Wright et al., 2025), suggesting that families are strongly considering the financial benefits and costs of attending college, which could impact 1G student enrollment.
1G status is closely linked to being a member of a minority group who is coping with systemic social inequities, including access to safe housing, a quality K12 education, and reliable employment (Saenz et al, 2007). Parents who have experienced racially based discrimination may not have had the opportunity to attend college, which reduces their overall earning potential and affects decisions of location of residence. Neighborhoods predominantly comprised of minority groups often experience high rates of violence and crime, reduced access to health care, and more poorly funded schools, all of which can influence a student’s preparation, readiness for, and success in college (Georgetown, 2019). As a result, regardless of parental education level, a student who attends an underfunded high school may be less prepared for college than their more affluent peers. Consider, for example, a collegeeducated public school teacher who chooses to live and work in a neighborhood, comprised of minority groups, because of their ties to the community. Although their child is not considered a 1G student, by virtue of where they go to school, they may have fewer enrichment opportunities than a nonminority, 1G student who lives in a wealthier school district.
FALL 2025
Lopez,
McNaughton-Cassill, Owens, Wright, Mendoza, and Martinez | First-Generation
Further confounding the situation is the proliferation of programming on college campuses designed to create a sense of identity and belonging, and to provide guidance and tangible support to 1G students. The development of mentoring programs, increased advising through student success centers, and emphasizing role models such as 1G professors and alumni have provided students with additional access to useful information. But these focused programs may also be changing the way 1G students view themselves with this designation. Many students on today’s campuses have moved from feeling as though their 1G status labels them as an outsider to seeing the designation as a badge of honor signifying their resilience, resourcefulness, ability to overcome obstacles, and a successful representative of the university (Martinez, 2018).
Furthermore, it is important to consider 1G status at the graduate level as well (Ledesma, 2022; Lunceford, 2011). According to the US Census Bureau, only 14.4% of Americans have completed a professional or graduate degree (US Census Bureau, 2022). This means that a student may not be a 1G undergraduate, but their family is new to the graduate or professional school experience. Therefore, it may be important to distinguish between students whose parents had no college experience, those who had undergraduate but not graduate experience, and those who completed studies at both levels. Indeed, the qualitative differences inherent between the undergraduate and graduate experience are great enough that parental experience may be a critical factor to take into consideration.
In short, determining the factors that influence student success at the collegiate level is incredibly difficult. Although the lack of consistent, informed guidance makes it more difficult for a student to achieve their goals, it is not clear that consideration of a collegeeducated parent is the only or best way to conceptualize this need for student success. Although some may call for ceasing to use the 1G descriptor, that seems a bit premature and likely misses a continuing important demographic of students that could be assisted in the college success journey. Indeed, we recommend that we engage in the difficult task of exploring the interconnected factors that contribute to student success, including parental education, so we are better prepared to meet the needs of these and all students.
As such, we propose the rephrasing of survey items and some new items that would measure “first generation student status.” A combination of these items would redefine the variable depending on the educational institution’s definition or the researcher’s operational definition of the variable. In addition, questions could be added for respondents to rate the guidance (e.g., from
none to frequent) and the quality of assistance received about the college experience (e.g., from not helpful to very helpful) for each person. See Figures 1–14b.
Conclusion
This editorial is in response to the call to reevaluate and reframe the assessment of the 1G variable. In general, the list of items to include regarding 1G student status need to change in accordance with how student experiences have evolved and adapted over the years. These revised items also attempt to account for the complex set of factors related to enrollment and matriculation (Roscigno et al., 2025; Wilbur & Roscigno, 2016). Although we have offered some suggestions here to assess the different dimensions of 1G status, this is not a final list to use but provides meaningful suggestions on how to rephrase and expand on the 1G student status categorization. We acknowledge that these suggested items can be rephrased to meet individual research project and theoretical needs, and as additional revisions may be necessary in the future. For instance, if students’ parents are two mothers or two fathers, then the stem of these questions would have to reflect these variants of the family structure. In addition, the suggested items listed above combine the experience of attending “college” to encompass attending a community college or a 4year college. It is possible that experiences from these different structured university settings affect the quality of guidance and advice given to students.
In sum, the reframing and reevaluation of the 1G variable is an ongoing issue in the research about academic success. As our society continues to evolve and develop, the examination of the 1G variable will also need to be modified and revised to more accurately reflect and capture this important demographic in college student success.
Are you the first person in your family to attend college?
a. Yes, I am the first person in my family to attend college.
b. No, I am NOT the first person in my family to attend college.
c. I am not sure.
Did your father attend college (e.g., community college, 4-year university) at any point in his life?
a. Yes, my father attended college at some point in his life.
b. No, my father did NOT attend college at all.
c. I am not sure.
FIGURE 1
FIGURE 2
FIGURE 3
Did your father attend college (e.g., community college, 4-year university) and finish college with a degree?
a. Yes, my father attended college and finished college with a degree
b. My father attended college but did NOT finish college with a degree
c. My father did NOT attend college.
d. I am not sure.
4
If your father attended college, was it in the U.S.?
a. Yes, my father attended college in the U.S.
b. No, my father did NOT attend college in the U.S.
c. My father attended college in both the U.S. and outside of the U.S.
d. My father did NOT attend college at all.
e. I am not sure.
5
Did your mother attend college (e.g., community college, 4-year university) at any point in her life?
a. Yes, my mother attended college at some point in her life.
b. No, my mother did NOT attend college at all.
c. I am not sure.
FIGURE 9a
As you attend college, which individual or entity in the list below provides you guidance about living in college (e.g., dormitory, apartment while in college)? (Check all that apply)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid, counseling services)
j. Other
k. No one
9b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
6
Did your mother attend college (e.g., community college, 4-year university) and finish college with a degree?
a. Yes, my mother attended college and finished college with a degree
b. My mother attended college but did NOT finish college with a degree
c. My mother did NOT attend college.
d. I am not sure.
7
If your mother attended college, was it in the U.S.?
a. Yes, my mother attended college in the U.S.
b. No, my mother did NOT attend college in the U.S.
c. My mother attended college in both the U.S. and outside of the U.S.
d. My mother did NOT attend college at all.
e. I am not sure.
10a
As you attend college, which individual or entity in the list below provides you guidance about academic success (e.g., how to study in college, what courses to take)? (Check all that apply.)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid, counseling services)
j. Other
k. No one
8
Do you have any siblings who attended college (e.g., community college, 4-year university)?
a. Yes, I have some siblings who attended college.
b. No, I do NOT have siblings who attended college.
c. I do NOT have siblings.
d. I am not sure.
10b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
FIGURE
FIGURE
FIGURE
FIGURE
FIGURE
FIGURE
FIGURE
FIGURE
FIGURE 11a
As you attend college, which individual or entity in the list below provides you guidance about financial management (e.g., tuition payment, part-time employment)? (Check all that apply.)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid, counseling services)
j. Other
k. No one
FIGURE 11b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
FIGURE 12a
As you attend college, which individual or entity in the list below provides you guidance about how to deal with loneliness, isolation, and depression? (Check all that apply.)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid, counseling services)
j. Other
k. No one
12b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
13a
As you attend college, which individual or entity in the list below provides you guidance about mental health issues? (Check all that apply.)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid, counseling services)
j. Other
k. No one
13b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
FIGURE 14a
As you attend college, which individual or entity in the list below provides you guidance about extracurricular activities (e.g., student organizations)? (Check all that apply.)
a. Mother
b. Father
c. Sibling
d. Grandparent or grandparents
e. Other relative (e.g., aunt, uncle)
f. High school teacher
g. High school counselor
h. The university’s student success program
i. The university’s other services (e.g., financial aid)
j. Other
k. No one
14b
How helpful was/were he/she/they in guiding you?
a. No one helped me
b. Not at all helpful
c. Slightly helpful
d. Moderately helpful
e. Very helpful
FIGURE
FIGURE
FIGURE
FIGURE
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Saenz, V. B., Hurtado, S., Barrera, D., Wolf, D., & Yeung, F. (2007). First in my family: A profile of first-generation college students at four-year institutions since 1971. Higher Education Research Institute. https://www.heri.ucla.edu/PDFs/ pubs/TFS/Special/Monographs/FirstInMyFamily.pdf
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Wilbur, T. G., & Roscigno, V. J. (2016). First-generation disadvantage and college enrollment/completion. Socius: Sociological Research for a Dynamic World, 2, 1–11. https://doi.org/10.1177/2378023116664351
Wright, R. R., Brough, S., Castro, J., Osborne, M., Johnson, L., & Johnson, S. (2025). State of the first semester freshman: Health and wellness through the COVID-19 pandemic, years 2018-2023. Psi Chi Journal of Psychological Research, 30(1), 65–83. https://doi.org/10.24839/2325-7342.JN30.1.65
Correspondence concerning this editorial should be addressed to Stella G. Lopez, One UTSA Circle, Department of Psychology, University of Texas at San Antonio, San Antonio, Texas 78249. Email: stella.lopez@utsa.edu
Emotion Science Video System: Clips With Categorical and Dimensional Ratings
Tyre N. Johnson and Jared J. McGinley* Department of Psychology, Towson University
ABSTRACT. The use of stimulus sets to study emotion has been a staple of emotion science research. Although many methods of emotion elicitation exist, the use of film (or video) clips has been a popular and effective choice. Even so, validated clips vary on many characteristics and quickly become outdated. Another drawback of current sets is rooted in the terms selected to rate the participants’ emotional responses. Due to varying theoretical viewpoints used to study emotion, stimulus sets are often only rated with categorical or dimensional emotion labels. As a result, the stimuli have limited versatility across research frameworks. To address some of these limitations, the current study recruited 401 undergraduates to complete an online video clip viewing study. Each participant viewed 10 clips (randomly selected from a 41clip set) and rated them on both categorical and dimensional emotion items. Selfreport ratings averaged across the emotion items indicate this stimulus set to be both effective in eliciting emotion, and versatile for both categorical and dimensional research paradigms. The present study produced the Emotion Science Video System, a robust, contemporary, and versatile set of video clips useful for research questions from varying theoretical viewpoints.
Keywords: emotion, film clips, video clips, emotion elicitation
Emotion drives human experience. It mobilizes resources in the body to adaptively respond to challenges in the environment (e.g., during fear, blood flow is redirected to the large skeletal muscles that are required to run). It also can amplify, constrain, or recruit elements of cognition; emotion states have been shown to reliably influence what humans attend to (Yiend, 2010), what they perceive from the environment (Zadra & Clore, 2011), how they make judgments based on this perceived information (Clore & Huntsinger, 2007), and which information will be stored in and retrieved from memory (Kensinger & Schacter, 2008; Tyng et al., 2017). In short, emotion plays an arguably essential role as an integrative force for most aspects of human psychology (Damasio, 1998). The role of emotion in human experience has stimulated broad interest in many research disciplines and facilitated application in a variety of commercial domains. Even though the appreciation of, and empirical investigation into, emotion has seen a pronounced increase over the last several decades, it has remained a polarizing topic for researchers. Conceptual discord
arising from differ ing theoretical approaches has divided researchers on the best strategies for how to elicit and assess emotion. To effectively elicit emotion for study in research settings, researchers have relied on a variety of stimulus types (Siedlecka & Denson, 2019). The most common method of emotion elicitation has been the use of video (or film) clips (Lench et al., 2011). Although these clips have been reliable in eliciting emotion, they quickly become dated, and the design decisions made by researchers can render the clips as less useful for subsequent researchers with incompatible theoretical viewpoints. The current study was designed to produce a contemporary set of video clips, each rated on categorical and dimensional scales, to produce greater versatility for future researchers.
Emotion Elicitation
Across disciplines as varied as psychology, engineering, economics, and neuroscience, the study of emotion has emerged as a prominent research topic over the last several decades (e.g., Gündem et al., 2022; Siddharth et al., 2019; Szymkowiak et al., 2021). Considering the
amorphous and often ephemeral qualities of emotions, there is an everpresent need to generate and improve upon ways to reliably elicit and measure them. Although pursuit of the latter has benefited from the standardization of self report, behavioral, and physiological recording tools, the former remains plagued by obstacles such as theoretical inconsistencies among researchers, cultural and language differences for participants to relate to the stimuli, variability in stimulus quality (e.g., image resolution), and generational differences in participants’ familiarity or connection with the themes or content of the stimuli. Despite these myriad and persistent obstacles, the standardization of emotion elicitation methods remains valuable for many reasons. Two notable ones are: (a) to confidently expect that the participants will generate comparable responses, and (b) to synthesize findings across disparate disciplines with the confidence that differences in findings are not simply due to methodological variability. In the brief history of studying emotions, many different elicitation methods have been used, each with its own strengths and weaknesses. The composite body of elicitation methods functions as a toolkit for researchers interested in the continued study of emotionrelated research questions.
The Role of Theory in Emotion Elicitation
A fundamental obstacle to the standardization of elicitation methods and the subsequent synthesis of findings across emotion literature is the conceptual discord resulting from the many theoretical approaches designed to explain the structure and functional organization of emotion (Adolphs, 2017; Barrett, 2006; 2017; Friedman, 2010). The history of emotion research, signposted by important empirical and paradigmatic shifts—from Charles Darwin’s writings until today—has been loosely characterized by “categorical” and “dimensional” conceptualizations of the structure of emotion (e.g., Cannon, 1927; Darwin, 1872/1998; James, 1884; Schacter & Singer, 1962). The categorical approaches often present emotions as discrete and evolutionarily hardwired responses to environmental challenges similar to thirst and hunger (Ekman, 1992). It is believed that the brain and the body are composed of differentiable neural and hormonal circuitry that each can produce emotion responses (Kragel & Labar, 2013, 2015). In contrast, the dimensional models of emotion purport that emotions arise from separable neural systems that largely control emergent elements of phenomenological experiences such as valence (e.g., positive vs. negative), arousal (e.g., high vs. low), or motivation (e.g., approach vs. avoid; Barrett, 2006; Eliot et al., 2013; Russell, 2003). These simplistic contrasts have largely been subsumed by more expansive functionalist versus
constructionist debates, which focus on the functional value of emotions or assert that the experience of discrete (i.e., categorical) emotions is largely constructed at the individual level by forces such as learning, socialization, adaptation, and linguistic labels, respectively (Kragel & Labar, 2013, 2015). Nonetheless, even modern theories conceptualize emotions in categorical and dimensional terms, and the presence and persistence of incompatible perspectives present issues for researchers, specifically regarding how the elicitation and measurement of emotion are operationalized. Researchers often develop their questions and select or construct experimental designs based upon their theoretical viewpoint; this can manifest with the creation of standardized stimuli that are not applicable to other researchers (e.g., using stimuli with only dimensional affect ratings to answer research questions about categorical emotion states). Stimulus sets that are not universally applicable to researchers posing questions from different theoretical viewpoints can foster challenges for being able to directly compare findings. For example, when comparing findings between studies, variability in findings could be due to either (a) different variance explained by statistical models, or (b) the use of different stimuli. Therefore, having widely available emotion elicitation stimulus sets that have standardized ratings on both discrete and dimensional terms can remove option “b” as a source of differing findings between studies.
Emotion Elicitation Methods
In conjunction with decisions on following a theoretical viewpoint to guide the design of a research study, researchers also are tasked with identifying the most appropriate stimuli for their research question. The selection of emotion stimuli is often one of the more challenging aspects of experimental design (Diconne et al., 2022); consequently, many elicitation methods have been used throughout the history of emotion research (Coan & Allen, 2007; Quigley et al., 2014; Siedlecka & Denson, 2019). These can range from more static stimuli such as images or brief sounds to more dynamic and sensorially rich stimuli such as music, videos , and social interaction. Each stimulus type offers researchers the opportunity to target or control for specific aspects of emotional elicitation that they deem relevant to their study. For example, textbased stimulus sets offer the participant the opportunity to reread portions of a text during the moment of presentation, whereas confederate interactions may not provide this opportunity. Static images permit fastacting and shortduration elicitations, and music clips traditionally require longer time courses for building to emotional crescendos. Collectively, the variety of stimulus types
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allows researchers to have more control over the specific aspects of emotion targeted by the research question.
Limitations of Different Elicitation Methods
With the existence of multiple types of stimuli for emotion elicitation, there will inherently be limitations that differ based on which stimulus type is employed. For example, for autobiographical recall manipulations (i.e., giving participants the flexibility to recall memories of intensely experienced emotions), participants must willingly submit to what could be an unpleasant experience; some may choose not to. Also, considering the variability in characteristics of memories (e.g., duration, vividness, complexity), autobiographical recall cannot be standardized across participants like other methods can (Siedlecka & Denson, 2019). Manipulations that require internally generated visualization (such as guided visual imagery) often require training to elicit potent emotion responses (Lang, 1979; Milshtein & Henik, 2020). Another elicitation method—viewing emotionally valenced pictures—has been reliably effective in eliciting emotions, but the static nature of pictures usually leads to shortduration emotion experiences, which can prevent collection of biological measures that require longer duration windows to capture signal changes (Laborde et al., 2017). Additionally, most standardized picture sets only have standardized dimensional (and not discrete) ratings of emotion (Lench et al., 2011). Many of these manipulation types are also hampered by the demand characteristics created by the inherent or necessary transparency of their instructions. It has been found that, after controlling for demand characteristics, the magnitude of the emotion responses from a variety of elicitation types is weakened (Lench et al., 2011; Polivy & Doyle, 1980).
Reviews of the literature have also revealed that different categorical emotion states are better elicited by some methods than others (Behnke et al., 2022; Kreibig, 2010; Siedlecka & Denson, 2019). For example, autobiographical recall is not an effective means of eliciting the emotion of surprise, and disgust and anger have been less reliably elicited by music (Siedlecka & Denson, 2019). Veltentype manipulations (i.e., selfreferential statements designed to elicit emotion) have generally been effective for eliciting dimensionally targeted states but not for discrete emotions (Lench et al., 2011). Although it is clear that many of these manipulation techniques have strengths that can be tailored to different research designs (e.g., pictures better allow for the repeated presentation and number of trials necessary for studies of memory or attentional control), many can be hampered by the ease, the magnitude, the duration, the ability to elicit all emotions, etc. (Kreibig, 2010; Rottenberg et al., 2007; Siedlecka & Denson, 2019).
Video Clips in Emotion Elicitation
The use of video clips has been a popular and effective method for eliciting emotion for many decades (Gross & Levenson, 1995; Samson et al., 2016; Scott, 1930). Video clips possess many strengths over other methods of elicitation. One is the ease with which they can be implemented within research settings. Another benefit is the ease in standardizing video clips, which makes replication of effects possible (Gross & Levenson, 1995; Rottenburg et al., 2007). Video clips also can elicit higher intensity emotion states as demonstrated by higher ceilings for selfreport ratings and physiological reactivity than alternative methods such as images and music (Rottenburg et al., 2007; Schaefer et al., 2010). This potential for high intensity can also be attributed to the high ceiling of complexity associated with video clips as well; the combination of a dynamic multimodal stimulus set, varying visual and auditory elements, and the potential for complex narrative, all work together to increase immersion (Quigley et al., 2014). It has been argued that video clips are also generally high in ecological validity, making them a preferred option among other stimulus types when the goal is to understand how emotions impact individuals in a realworld setting (Gross & Levenson, 1995; Rottenberg et al., 2007). This is especially important when comparing video clips to confederate interactions, which are more prone to ethical issues such as using deception. Emotions have also been shown to be just as if not more effective for eliciting a wide range of discrete amotion states (Lench et al., 2011; Siedlecka & Denson, 2019).
Video clips do have some limitations, many of which are mitigated by the consistent generation and validation of more contemporary sets of videos. One of the issues that attenuates the potency of emotion responses is participants’ familiarity with the clips (Quigley et al., 2014). This can impact the validity of participant responses, because prior familiarity with a video clip has been shown to influence potency (Rottenberg et al., 2007). As mentioned, video clips also quickly become dated—as evidenced by reduced relatability and comparatively poorer image resolution and frame rate (LaRocco, 2023)—which results in few contemporary sets appropriate for new and younger generations of participants (Diconne et al., 2022; Zupan & Eskritt, 2020). Relatedly, video clips can be expected to provide greater heterogeneity in responses in older populations (Charles, 2005), and themes contained in the videos (e.g., vampires or nurses) are rated differently in younger and older populations (Grandy et al., 2020). Limitations aside, video clips are still frequently employed in emotion studies and have clear and continued utility for future investigations, but researchers call for Johnson and McGinley
the continued generation of standardized video clips sets to provide a wide pool of contemporary options to select from (Diconne et al., 2022).
The Present Study
Although still frequently used in contemporary emotion research, many e xisting video clip sets contain validity issues that can impede emotional elicitation. Recent literature has also encouraged the consistent and persistent formation of new emotion stimulus sets to provide researchers with more options for the myriad characteristics that might be important when selecting clips (e.g., diversity of sample, visible gender of protagonist in a clip; Diconne et al., 2022). Also, most existing video stimulus sets are less versatile for researchers subscribing to differing theoretical viewpoints, because few include both categorical and dimensional ratings. In response to the limitations listed above, the current study was designed to serve several goals: (a) to generate a new set of standardized video clips that (b) are more contemporary than past video sets; (c) contain both categorical and dimensional emotional ratings, which (d) vary in valence and arousal and across seven categorical states; (e) contribute to the broader body of video clips for future researchers to select from based on nonemotiondriven characteristics (e.g., visible ethnicity and gender of protagonists), and (f) are freely available to future researchers to provide flexibility in selecting clips based on their research questions.
To do so, we presented participants with 10 (of 41) randomly selected emotionally evocative video clips and had them rate their emotion responses on both dimensional (via the SelfAssessment Manikin; Bradley & Lang, 1994) and categorical (via the Discrete Emotions Questionnaire; HarmonJones et al., 2016) selfreport items.
Method
Participants
Undergraduates (Mage = 19.1, SD = 2.6) from a large midAtlantic U.S. university were recruited through an online research pool and received course extra credit for participation. A total of 401 participants’ responses were collected; however, 36 were not included due to either failure to complete the study or completing the study more than one time. As a result, 365 participant responses were analyzed and reported. Of these 89 identified as male, 270 identified as female, and 6 identified as nonbinary. One participant did not wish to specify their racial/ethnic identity, and 26 participants identified with more than one racial/ethnic identity. Of the remaining ( n = 338) participants, 46% identified as White/ European American, 32% as Black/African American,
7% as Hispanic/Latinx, 7% as Asian American/Pacific Islander, <1% as Middle Eastern/North African, and <1% as Native American.
Materials
Video Clips
One hundred four clips were located through popular video platforms (e.g., YouTube, DailyMotion) and then rated on categorical and dimensional items by three research members of the research team. Clips that received a mean rating of at least 5.0/7.0 on at least one of the categorical emotion states (i.e., fear, anxiety, sadness, anger, disgust, happiness, and surprise) were deemed acceptable for the study. Fortytwo clips met this criterion. Due to unforeseen access restrictions, one video clip used in this study was removed (leaving 41 video clips in total). The remaining video clips ranged from 15 seconds to 5 minutes. Descriptions of, and information on, accessing the video clips can be found in the Appendix.
Measures
Discrete Emotions Questionnaire. Seven items from the Discrete Emotion Questionnaire (DEQ; HarmonJones, 2016) were presented to assess self reported emotional responses for discrete items to each video clip. Participants were asked to use a 7point scale ranging from 1 (not at all) to 7 (an extreme amount) to assess how strongly they felt seven discrete emotions (i.e., happiness, sadness, anger, disgust, fear, surprise, and anxiety).
Self-Assessment Manikin. The (SAM; Bradley & Lang, 1994) was used to assess both subjective arousal and valence after viewing each video clip. The two imageassisted response items were scored on 5point semanticdifferential scales. Valence was anchored with “extremely negative” and “extremely positive” whereas arousal was anchored with “ low energy ” and “ high energy. ” The anchors were accompanied by an image representing the response choice (e.g., a happy face was used to represent the choice “extremely happy”; an explosion image in the chest of a character indicated “high energy”).
Procedure
The entire study took place on an online survey platform that contained surveys and embedded the videos. Following the completion of the consent form, participants completed the demographics survey and an alexithymia survey not discussed further in this manuscript. Prior to the first video clip being shown, participants were instructed to (a) maximize the screen of all videos shown in the survey, (b) watch each clip in its entirety, (c) answer each question to the best of their ability, and (d) refrain from skipping any videos. Each
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TABLE 1
Descriptive Statistics for Video Clips’ Emotion Ratings
Note. The values represented in each cell show (1) the mean, and (2) the standard deviation (in parentheses), for each video clip's rating. Values presented in bold reflect categorical emotion ratings above a mean of 5.00.
participant was then shown a video clip, after which they were asked to rate their emotional responses to the clip via seven DEQ and two SAM items. Each participant repeated this process (clip viewing and emotion rating) for 10 clips. The 10 video clips were randomly selected from the list of 41.
Results
Data Processing
Accumulated across viewing 10 videos, each participant had 10 ratings for each emotion item. Average emotion scores were calculated by averaging the 10 ratings for each of these items (e.g., the 10 anger scores were averaged into a single anger score). ShapiroWilk tests were then run for each emotion score to test for normality. All categorical and dimensional scores violated the assumption of normality (all ps < .05). The interquartile range method was then used to identify potential outliers. With the exception of anxiety, each emotion item had between one and four outliers at the 1.5 interquartile range. Because none of the outliers were identified as
2
“extreme” (i.e., scored outside 3 interquartile ranges), each emotion score was logtransformed to correct for normality. Followup ShapiroWilk tests confirmed that the logtransformed variables were no longer skewed. All descriptive analyses listed below used nontransformed scores, and all inferential analyses listed below used logtransformed scores.
Descriptive Analyses Across Emotion Ratings
The primary research objective of the study was to produce a set of clips with standardized ratings for categorical and dimensional emotion items. We calculated mean scores for each emotion item for each video clip (see Table 1), and then tabulated the number (i.e., frequency) of clips whose mean rating exceeded numerical thresholds for each item (see Table 2). On the 7point rating scale for the categorical emotion items, only one item (disgust) had a mean average above 6.00. Sixteen mean ratings exceeded the 5.00 threshold, and 66 mean ratings exceeded the 4.00 average. For translation, mean categorical ratings above 4.00 indicated that participants reported experiencing the respective emotional state “moderately,” ratings above 5.00 indicated experiencing it “quite a bit,” and ratings above 6.00 indicated experiencing it “very much.” For the dimensional ratings, five mean ratings were below the 2.0 (indicating high positive) valence. Fourteen mean ratings were above the 4.0 (indicating high negative) valence.
Assessing “Categorical-Ness” of Items
Note. The values in each cell are the number of film clips that had average ratings above the specified values for each emotion category
TABLE 3
Correlations Between Emotion Ratings
9.
Note. The values used in the calculations are averages of each emotion response item across all 10 video clips viewed. ** p < .001.
As stated above, mean responses were calculated across the items within each participant; for example, the mean for the 10 sadness ratings provided for 10 separate video clips was calculated to provide a “mean sadness” score for each participant. Pearson’s bivariate correlations were then run between mean ratings for all emotion items (see Table 3). Higher correlations between categorical items were interpreted as indicating higher similarity (i.e., less differentiation or categoricalness). The correlations between most negative emotion items were high (all rs were .66 or higher). Surprise, which is often viewed as a mildly negative or valence neutral emotion (Noordewier & Breugelmans, 2013), was highly correlated with all negative emotions (r = .58 or higher) but only r = .20 with happiness. Anxiety and fear were close to indistinguishable, r(355) = 0.87, p < .001. Also notable were some of the correlations between categorical and dimensional items. Most categorical items were moderately to strongly correlated with valence (range: .32–.50), while showing significant but weaker correlations with arousal (range: .24–.42). Most notably, happiness showed no significant correlations with negative emotion items
Discussion
Video clips are widely acknowledged as effective tools for eliciting emotions in experimental contexts (Schmidt et al., 2019); yet, although several standardized video clip sets exist, many lack the versatility for researchers to test questions based on both categorical and dimensional frameworks. Existing video clip sets also present limitations for future researchers due to being outdated, inaccessible, and having lowquality resolution (Zupan & Eskritt, 2020). To address these shortcomings, we attempted to create a large, contemporary, and freely accessible set of video clips that were able to elicit: (a) several categorical emotion states, (b) a wide range of positive and negative states, and (c) a wide range of arousal states. The clips also add diversity to the larger pool of video clip sets in dimensions such as the visible gender and racial identity of the protagonists. The resulting set of video clips—the Emotion Science Video System (ESVS)—was effective in meeting the first two aims, but only effective in eliciting a somewhat restricted range of arousal states.
Categorical Emotion Ratings
In service of future research designed to target categorical emotion states, the ESVS was effective in eliciting multiple emotions. In contrast to past research, which has used video clips to only elicit a small number of categorical emotion states (e.g., Gilman et al., 2017) or measured reactivity solely on valence or arousal (e.g., Sato et al., 2020), the current system included a large number (41) of video clips, which were effective in eliciting seven categorical emotion states (i.e., happiness, sadness, anger, disgust, fear, surprise, and anxiety) based on participant selfreport. On a selfreport rating scale, all but two clips averaged ratings greater than “somewhat” on at least one emotion item. Thirtythree of the clips averaged greater than “moderately” for one of the categorical emotion ratings. Five of the clips were highly evocative, averaging ratings greater than “quite a bit,” and one clip exceeded “very much” for its average rating These findings are similar to other large video databases, which have also found greater than 80% of their clips to be rated at least “moderate” in their emotional evocativeness (e.g., Schaefer et al., 2010; Zupan & Eskritt, 2020). In contrast to smaller video clip stimulus sets (e.g., Gross & Levenson, 1995), the ESVS provides greater versatility to researchers who might want to induce a large number of categorical emotion states.
With categorical emotions, there remains a storied and continuing debate on the “discreteness”—or biologically hardwired nature—of emotions (Gundem et al., 2022; Kragel et al., 2019). Also, humans vary in their ability to successfully differentiate emotions
(Seah & Coifman, 2022), which can obfuscate whether self report emotions are accurately reflective of the emotions actually being experienced at that moment. However, we believe that inferences can still be drawn from the samplelevel interitem correlations between the categorical emotion items to at least partially inform which specific emotions can be reliably elicited by the ESVS. Notably, the ratings of anxiety and fear were so strongly correlated that they should not be considered as separable emotion states. This finding was expected, because most basic or discrete emotion theories do not separate them (e.g., Ekman, 1992; Izard, 1977; Panksepp, 1982), and most empirical emotion literatures establish neuroanatomical and physiological links between anxiety and fear (Dbiec & LeDoux, 2009; Tovote et al., 2015). So, after dismissing anxiety from the categorical emotion list, eight clips showed differentiation in participant ratings for five selfreport emotions. We crudely operationalized differentiation as having an average rating above “quite a bit” for only a single emotion item. Two clips had ratings above “quite a bit” for more than one item: Domestic for disgust and anger, and Fire Sky for disgust and fear. It is arguable that Domestic tapped into contempt, which was not assessed but has been purported to be an emotion state constructed from both disgust and anger (Prinz, 2007). Considering the dynamic nature by which a video clip unfolds, it makes sense that Fire Sky was rated high on two emotion states, which might not have been elicited concurrently, but nonetheless, both emotions were experienced at times during the clip’s presentation. Similar to past research on emotion elicitation, many of the negative emotion ratings were highly correlated, which might speak to the (a) aforementioned dynamic unfolding of a minuteslong clip, (b) difficulty in emotion differentiation by participants, or (c) actual dimensional underpinnings of the discrete emotion states according to some theoretical frameworks (Christie & Friedman, 2004).
Dimensional Emotion Ratings
Because many research studies that elicit emotion are not designed to target categorical emotion states, the ESVS is also useful for those selecting clips based on ratings of valence and arousal. In this set, 26 clips averaged ratings on the negative side of the valence continuum and 15 on the positive side. On a 5point semanticdifferential scale, 14 of the 26 negative clips exceeded an average rating of 4 out of 5, and 5 of the 11 positive clips received averaged ratings of less than 2 out of 5. Although there were close to three times more clips rated as highly evocative on the negative side of the valence continuum, the complete ESVS library still contains clips that were effective at eliciting states on the full continuum of Johnson and McGinley
positive to negative valence. The greater representation of negatively valenced clips was expected, because the clip selection process was structured to reflect the more broadly accepted discrete emotions (e.g.. fear, sadness, anger), of which there are more negative than positive ones (Ekman, 1992). Similar to past video clip stimulus sets, it is common to have more negative than positive emotion clips (i.e., Schaeffer et al., 2010; Zupan & Eskritt, 2020).
The range of average intensity (i.e., arousal) elicited by the clips was restricted, with fewer clips scoring on either end of the scale. On a 5point scale, all clips had average ratings between 2 and 4. It is likely that this is partly due to the inherent limitations of the video clip medium for eliciting truly intense emotion states in an ecologically valid or naturalistic sense (Rottenberg et al., 2007). Research has shown that laboratory stressors are not as intense as realworld events (Whilhelm & Grossman, 2010). Additionally, the clips were intentionally selected to target categorical emotion states, so neutral clips, which are, by definition, lower in intensity, were not included. Close to half the clips were scored as moderately arousing (operationalized as averaging greater than 3 out of 5), which is comparatively less than the proportion of clips in other video libraries (e.g., Schaeffer et al., 2010; Zupan & Eskritt, 2020).
Although this study was not intended for theory testing, it is notable that valence and arousal were not significantly correlated, which fits with their often orthogonal presentation in theoretical models (Russell, 2003) and aligns with other studies that did not find a directional relationship between the two dimensions (e.g., Koelstra & Patras, 2013; Zupan & Eskritt, 2020). Also notable for theory construction, happiness did not significantly correlate with any of the negative emotion items, which precludes it from serving as an oppositional anchor on a single positive to negative valence continuum. Emotion theories vary in whether positive and negative valence are present on one continuum, multiple continua, or have a nonlinear relationship (Norris et al., 2010; Russell, 2003; Watson et al., 1988).
Versatility
Although other comparable video libraries exist for targeting multiple emotion states, there is a continuous need to generate new and contemporary sets to provide researchers with not only more options of clips to select from, but also to permit the consideration of other criteria that could be of relevance for research designs (e.g., clip duration, diverse raters; cf. Diconne et al., 2022). The ESVS also provides additional versatility by permitting researchers to select from clips even if they vary in terms of how they intend to operationalize emotion in their
design, such as selecting clips based on dimensional or categorical ascriptions of emotion. Although some studies have collected ratings for both categorical and dimensional items (e.g., O’Reilly et al., 2016), other studies conducted within the same realm of research interest have not (e.g., Ma et al., 2015). This becomes particularly apparent when evaluating research on existing video clip libraries , in which researchers have only assessed either categorical or dimensional items (e.g., Bartolini, 2011; Howard, 2014). The ESVS circumvents these potential constraints by not only providing both categorical and dimensional emotion ratings but also by contributing a set of video clips containing several varying levels of evocation. For example, if a moderately arousing, positive, highhappiness (with a low chance of experiencing a negative emotion) video clip is desired by a researcher, the ESVS can offer a few different options (e.g., Kids, Kitten). Additionally, the clips were mostly selected from media published in the previous 15 years, with a higher concentration of clips from the previous five. Compared to past video clip libraries, the ESVS offers the most contemporary collection of video clips published (to date). Considering how prolific the topic of emotion has become across different research disciplines, the variety and versatility of validated stimuli to select from remains important.
Limitations and Future Directions
There are a few clear limitations from this study that we think are important to address. First, although the sample w as quite diverse in its demographic composition, it was composed of an educated college student sample with a restricted age range, and close to three quarters identified as female Therefore, followup studies should test whether the pattern of emotion responses to the videos hold in samples that differ on meaningful demographic and psychological characteristics. Second, although the media selections themselves were clearly evocative for the tar geted emotion states, they are mostly proprietary, so we are unable to make them freely accessible for the public. Even though we provide information to assist others in locating these clips, the additional barrier created by not making each clip readily available slows science and can disincentivize their use when compared to widely accessible noncommercial libraries (e.g., Kreibig et al., 2013). Third, in spite of the clear targeting of categorical emotion states by the selection of clips, we did not attempt to quantitatively differentiate emotion states for each clip. If we incorporated a multivariate or multisystem battery of measures (e.g., behavioral, autonomic), then we could more reliably present with confidence each clip’s ability to target a differentiable emotion state (e.g., McGinley &
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Friedman, 2017; Stephens et al., 2010). Extending from this point, the purely selfreport response modality has many known limitations such as participants’ variable capacity for emotion differentiation. Lastly, although the creation of this set of clips serves the important role of enabling future researchers to conduct more applied studies on areas involving emotion, the study was not designed to ask or answer any applied questions, itself. Therefore, future research should most clearly use these emotion clips in more applied research paradigms and assess the predictive validity of participant responses to these clips.
Conclusion
This study contributes to the large and growing repository of stimulus sets curated and rated to broaden the options for future researchers interested in the topic of emotion. Although the generation of stimuli like this do not directly inform the competing theories about the structure of emotions (e.g., functionalist versus constructionist approaches), there is still value in the utility of stimuli prerated on discrete and dimensional scales to afford flexible use by as many researchers as possible. Additionally, the diverse sample and contemporary nature of these clips permit broader criteria for stimulus selection for future studies. We encourage others to use, validate, and share the ESVS for future research endeavors. We believe these videos have utility in research domains spanning from basic to applied settings across a variety of topical domains. Considering the continued interest in emotion across psychological and behavioral research, we think the current stimulus set can be used to answer many research questions with broader importance. Emotion has been shown to influence cognitive and decisionmaking processes, affect wellbeing, impact social interactions, and play an important role in many psychopathologies. Both basic and applied research aimed at understanding these phenomena often rely on validated and versatile stimulus sets, and we anticipate that the current set will be valuable for such purposes.
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Author Note
Jared J. McGinley https://orcid.org/0000000191407081
We have no conflicts of interest to disclose for this manuscript. We want to thank our research assistants Tanisha Bhattacharya and Brenda Villanueva who helped select and preliminarily rate the clips used in this set. Additionally, we would like to thank all our intellectual predecessors who have paved the way to enable this study to be conceptualized and executed.
Any correspondence should be directed to Jared J. McGinley, Department of Psychology, Towson University, MD 21252. Email: jmcginley@towson.edu
A slave owner finds out he is being lied to by potential business partners, leading to a hostile exchange
A lady is tormented by an evil spirit spawning from a doll in her home
Promoting Equitable Advising Practices: Examining the Impact of Growth Mindset and
Appreciative Advising Micro-Messages on Disclosure and Student Success
Zaire I. Preston, Elizabeth C. Yanes, and Tonya M. Buchanan* Department of Psychology, Central Washington University
ABSTRACT. Given high rates of attrition among firstyear college students (e.g., Walker et al., 2017), increasing student retention while reducing educational inequality is imperative for institutions of higher education. Academic advisors play a critical role in these efforts, as advising communications can have powerful effects on important student outcomes (e.g., Kyte et al., 2020). Across two experimental studies, we examined how small changes in language used in advising communications influence students’ thoughts and behavioral intentions (e.g., support, persistence, disclosure of learning disability/need for accommodation). Building on recent work supporting the effectiveness of advisor communication based in growth mindset and appreciative advising frameworks (Buchanan et al., 2022), we investigated the utility of advising micromessages congruent with these frameworks on students. In Study 1, undergraduate students imagined receiving a message from an advisor regarding a challenging class that contained growth mindset micromessages, appreciative advising micromessages, or policy/ resource information only. Participants then completed measures of student outcomes. Overall, when students saw either of the advising micromessages (vs. only information), they experienced more positive student outcomes and reported being more willing/ comfortable to disclose a learning disability to their advisor. In Study 2, we replicated the positive effects on student outcomes of Study 1 using a combined growth mindset and appreciative advising message. Messaging matters, especially as college campuses seek to foster and support their increasingly diverse student bodies. Our research suggests that subtle language used in advising can have a powerful influence on student affect and behavior.
Academic advisors are often the first people that incoming college students interact with. As such, advisors have prominent roles in guiding student decision making and monitoring program
Diversity badge earned for conducting research focusing on aspects of diversity. Open Materials badge earned for transparent research practices. Materials are available at https://osf.io/ub7n4/
progress throughout their undergraduate careers. Due to high rates of attrition in firstyear college students, there is a heightened need for quality communication in the advisor–student relationship (Alexitch, 2002; Walker
et al., 2017). Advisors function as the building block towards student success; promoting and encouraging academic achievement, while helping students learn to navigate the institution’s rules, norms, and culture (Almanzar, 2021). As a result of this pivotal role in student success, academic advisors must exceed the prescriptivebased communication methods to better support students, especially those who have historically been underrepresented and underserved (e.g., students of color, students with disabilities, first generation students; Buchanan et al., 2022).
According to previous studies, the language used by academic advisors has a significant impact on student outcomes such as student feelings of support and persistence (Buchanan et al., 2022; Maddineshat et al., 2019; Powell et al., 2013). Micromessages are subtle cues in behavior and language that can be conscious or semiconscious and may have a significant impact on how others feel (Kyte et al., 2020; Rowe, 2008). Micromessaging has been shown in experimental studies to be an effective tool in academic contexts, allowing for students to feel supported, and promoting persistence (Buchanan et al., 2022). It is of vital importance that advisors move beyond a superficial and a solely informationbased rhetoric that fails to prepare and support marginalized students in their academic journey. In the current set of studies, we further investigate the impact of utilizing advising micromessages in advisor–student language on positive student outcomes across diverse student identities. We extend previous research to examine the combined utility of these messages and their ability to create a safe space for students to be open about their challenges by measuring willingness to disclose ability/disability status in addition to persistence, and feelings of support.
Advising
Firstyear college students often experience a newfound freedom in decisionmaking, yet with that freedom comes the responsibility to make sometimes lifealtering choices. This is where the role of the academic advisor becomes essential in guiding the decision making process. Academic advisors are often the first point of contact for incoming students, making them responsible for students’ first impressions of what college will be like. Research by Castleman et al. (2020) that used a multicohort randomized control trial suggested that students are more likely to graduate within five years of completing high school when they receive highquality, individualized advising. However, when students feel lack of concern from advisors, they are less likely to view their advisor with warm regard, which may lead to higher rates of student dissatisfaction and consequently higher rates of attrition (Holland et al.,
2020; Soria, 2012). Academic advisors, by integrating growth mindset into their interactions with students, may contribute not only to academic success but also to students’ overall resilience and persistence in the face of challenges. However, it is important to bear in mind that advising may differ across diverse university types. According to the National Academic Advising Association (NACADA), advising models can vary significantly between small and large institutions, as well as between private and public universities (Pardee, 2004). Another benefit of healthy student–advisor relationships is that students may be more comfortable selfdisclosing learning disabilities. There are several observed and empirical benefits to disclosing learning disabilities with advisors. Research has found that, when students disclose their learning disabilities with advisors and receive appropriate accommodation, they tend to perform better academically than those who do not disclose or receive support (Denhart, 2008; Mamiseishvili & Koch, 2011; Newman et al., 2019). Building a rapport with students is imperative as it may open a doorway for students to feel safe and cared for by advisors, leading to selfdisclosure of disabilities. Disclosing a learning disability can help students become more aware of their strengths and weaknesses and further develop their skills to advocate for themselves and communicate their needs to others (Henderson & Mapp, 2002). Lastly, disclosing a learning disability can help students build a network of social support and reduce feelings of isolation and stigmatization (Henderson & Mapp, 2002).
Micro-Messaging
The academic advisor’s role is to help direct students towards a degree through formal interactions that have the potential to inform and guide students’ decisionmaking, enhance their sense of belonging, and illuminate beliefs that students may have about themselves and their capabilities. Studies have shown that the advisor–student interaction can have a significant impact on student outcomes. Micromessaging includes subtle cues or actions that deliver an interpretive meaning to another person. These subtle messages or cues can be verbal or nonverbal, and they can be intentional or unintentional. Micro messaging can be used to communicate expectations or principles that are either positive (e.g., supportive micro affirmations) or negative (e.g., microinvalidations; Harrison & Tanner, 2018). For instance, microaffirmation such as those used in appreciative advising can include small acknowledgments of a student’s worth or achievement. As a result, micromessaging can have powerful effects on students’ perceptions of themselves, their attitude towards their education, and their behavior in the decisionmaking
FALL 2025
Preston, Yanes, and Buchanan
process. This is precisely what Buchanan et al. (2022) found in their study, which focused on advisor micromessaging using small language changes to advising communications that channeled a growth mindset or appreciative advising.1
Research has found micro messaging to have several empirical benefits for students including an increased sense of motivation and academic engagement. The foundation of micromessaging emphasizes and recognizes individuals in such a way that they are motivated to thrive in an environment where they may otherwise feel marginalized, hopeless, or lost (Rowe, 2008). As a result, advisors who use micromessages can empower the student by highlighting the value of education and importance of effort, creating the potential for growth in their academic studies.
Additionally, micro messaging communicates that advisors care about students, resulting in an enhanced sense of belonging and a notion that they have resources available to them (Soria, 2012). As such, micromessaging establishes students’ likelihood to seek out help and persist within their studies or challenges. Studies have also found that micromessaging supports students’ mental health in several ways including emphasizing the importance of selfcare, the value of seeking help, and the potential for personal growth (Powell et al., 2013). When students interpret these subtle cues as encouraging their own selfcare, they may feel validated and supported to achieve their goals, which may lead to improved mental health.
Building on a qualitative study by Kyte (2020), which suggested the utility of growth mindset micromessages from advisors, Buchanan et al. (2022) experimentally compared the impact of several advisor micromessages on student success outcomes and how they differed based on student identity. They discovered that, although students benefited from the micromessaging compared to an informationonly control communication, firstgeneration and students of color experienced more pronounced increases in student success outcomes (e.g., persistence, feelings of support) when exposed to growth mindset or appreciative advisingbased language. The study highlighted the impact of small language changes and reinforces the notion that the advisor to student relationship is a critical factor, especially for historically underrepresented and underserved students in encouraging persistence and fostering a feeling of support during the college experience (Buchanan et al., 2022). However, researchers examining the impact of micromessages on positive outcomes have yet to 1Given the scope of our research, we focus primarily on positive and/or supportive language and will henceforth use the term micromessaging to refer specifically to short, subtle communications that are positive in nature.
turn their attention to those students struggling with academic and social disability.
Growth Mindset and Appreciative Advising Growth mindset refers to an individual’s perspective on challenges and difficulties in their academic or work lives. When people have a growth mindset, they believe that they can learn through setbacks and that their ability or talents are neither innate nor permanent (Dweck, 2006). A growth mindset encompasses the idea that intelligence is malleable, and that academic achievement or success comes from hard work, strategy, and help seeking behavior. Someone who adopts a growth mindset views their failures or struggles as a learning opportunity rather than a reflection of who they are. In contrast, someone with a fixed mindset focuses on the result and views ability as set in stone. Researchers have identified empirical benefits for college students that adopt a growth mindset, including improved academic performance (Dweck, 2006; Pekrun et al., 2009). When someone with a fixed mindset fails, they believe their intellectual ability is limited, thus they only focus on what they could not achieve, rather than engaging in problemsolving strategies. Fortunately, a growth mindset can be taught and conveyed through instructors’ own mindset (Canning et al., 2021), workshops/lab experiences (Yeager & Dweck, 2012), and academic advising micromessages (Buchanan et al., 2022). When growth mindset is utilized, students experience a range of positive outcomes including improved academic performance, greater persistence, and more helpseeking behavior (e.g., Yeager & Dweck, 2012).
Appreciative advising is an advisor approach that focuses on advisors interacting with students in a positive and inviting manner (Truschel, 2015). The framework of appreciative advising is based on several stages that include building rapport and connection by asking positive open ended questions that draw out students’ strengths and dreams and working with students to design a plan that is supportive to the students’ passions and goals (Bloom & Hutson, 2007). The traditional prescriptive advising focuses on telling the student what to do such as courses to take and requirements to fulfill (Barbuto et al., 2011). Unlike prescriptive methods, the appreciative advising model allows for warm connection between advisors and students to help reveal students’ strengths and help direct them towards goals that will support their academic and personal success. Through the appreciative advising ideology, advisors can help to create a safe space where students feel comfortable enough to discover their interests and to disclose challenges while learning how to build skills to overcome them.
Additionally, appreciative advising has been found to increase retention rates for students who may be facing academic challenges, such as academic probation, and in helping students develop skills in resiliency and persistence (Butler et al., 2016; Virtue et al., 2021). For example, Butler and colleagues implemented the appreciative advising framework through a series of mandatory meetings for students who were on academic probation, tracking the students’ progress throughout the academic year. They found a significant decrease in the number of undergraduates who were involuntarily withdrawn (academically disqualified) from the university after the implementation of the novel advising practice. For students who have historically been underrepresented and underserved, the support gained from appreciative advising might be vital to academic achievement, persistence, and to their sense of belonging at the institution.
Research has indicated that academic advisors can be a key component in a student’s development during their undergraduate experience (e.g., Powell et al., 2013; Virtue et al., 2021). This is especially true for students who turn towards their academic advisors for support in the decisionmaking process during their college endeavors (e.g., Tinto, 1993). Although past studies have examined the benefits of using either growth mindset or appreciative advising (Harrison & Tanner, 2018; Kyte et al., 2020; Powell, 2013; Rowe, 2008), little is known about the potential positive outcome for combining these two approaches. Incorporating both methods into the advisor–student relationship could potentially have significant positive impacts.
Appreciative advising is a relationalbased practice and therefore creates a strong and meaningful relationship between advisors and students, laying a foundation for students to feel supported in their academic aspirations. The growth mindset approach emphasizes the belief that all challenges can be turned into learning opportunities, encouraging students to acknowledge that their ability and intelligence can be developed through effort. By combining these two approaches, advisors can help atrisk students to develop a sense of selfefficacy, motivation, and purpose, which can improve their academic performance and mental health (Yeager & Dweck, 2012).
Moreover, the advisor–student relationship has been shown to be critical for firstgeneration and historically underserved student populations, in part, because advisors connect students with resources, provide knowledge on appropriate courses, and have influence to foster student success and motivation (Museus, 2021). As such, when advisors focus on the strength and goals of their students, including marginalized or atrisk students, they have a unique ability to increase retention and
graduation rates while helping to build students’ sense of belonging (Almanzar, 2021; Creighton, 2006; Tamakloe et al., 2021). As indicated, there is a pressing need for research that combines both growth mindset and appreciative advising language in the advisor–student relationship given increasingly diverse student populations. Such an investigation could provide valuable insight to improving advising practices, especially for students who are more likely to face academic and social challenges.
Current Work
Expanding upon the groundwork laid by Buchanan et al. (2022) on the benefits of growth mindset and appreciative advising micromessages on diverse student bodies, our current series of studies further examined the impact of integrating growth mindset and appreciative advising micromessages within language on student success outcomes. To improve the academic college experience for all students and to create a more equitable advising approach, there is an urgent need to investigate factors that may promote positive student outcomes and provide more insight into effective advising practices.
We extended prior research by exploring the potential of these messages to foster a safe environment for students to openly discuss their challenges. We investigate this by measuring the willingness of students to disclose their abilities/disabilities, alongside measurements such as academic persistence, and feelings of support. In addition, we explore the utility of employing both growth mindset and appreciative advising through micromessaging in a single advisor communication. We anticipate that combining these two approaches within the advisor–student dynamic will be a promising communication strategy for increasingly diverse college student populations.
Hypotheses
H1: For both studies, we expected to replicate Buchanan et al. (2022) such that micro messaging conditions would lead to more positive student outcomes when compared to the informationonly control condition.
H2a: We expected that micromessaging conditions would result in a higher likelihood of disability selfdisclosure when compared to the information only control condition (Study 1).
H2b: We expected that the messaging conditions would result in a higher likelihood of disability selfdisclosure to one’s advisor rather than an outside entity (i.e., Disability Services; Study 1).
H3: We expected that the combined micromessages condition would result in more positive student outcomes when compared to the informationonly control condition (Study 2).
Study 1
Building upon prior research highlighting the positive impacts of advisor micromessaging on student success outcomes (Buchanan et al., 2022; Kyte et al., 2020), in Study 1, we aimed to replicate and extend this work. Specifically, we examined the impact of such advising micromessages on student success outcomes, including perceptions of support and persistence. Further, given the empirical benefits (e.g., increased selfadvocacy) of students disclosing their learning disabilities (Henderson & Mapp, 2002), we tested whether our micromessages might also impact likelihood of disclosure to two potential sources (i.e., advisors, disability services).
In line with prior research, we expected students to benefit from the advising micromessages. Specifically, we expected that, compared to the informationonly communication, students would exhibit more positive responses to the growth mindset and appreciative mindset advisor communications (e.g., feel more supported by the advisor, report greater likelihood to persist in a challenging class). Further, we anticipated that these micro messages (vs. information only) would also encourage students to discuss learning challenges and disorders. Finally, we expected the positive effect of advisor micromessaging on disclosure of learning disabilities to be observed when students think about disclosing to their advisor, rather than to Disability Services.
Method
Participants
The recommended sample size for our studies was determined by a priori power analyses (Gpower: Faul et al., 2007). Results indicated that 111 participants would achieve a power of .80 with an alpha of .05 and an estimated effect size of f = .30. As such, we recruited 137 undergraduate students (Mage = 20.00, SD = 5.80) in psychology courses at a university in the Pacific Northwest who identified primarily as White (62.5%) and female (65.0%) to take part individually in this 10–15 minute online study. See Table 1 for detailed demographic information.
Materials
The methodology of the study mirrors Buchanan et al. (2022) with the addition of a measurement of willingness to disclose learning challenges/disabilities. Specifically, after providing informed consent, participants imagined receiving a message from an advisor regarding a challenging class that, based on random assignment, contained growth mindset micromessages, appreciative advising micromessages, or information only (see Online Supplemental Materials at https://osf.io/ub7n4/) for advising messages and student
measures).
Procedure
Participants were given as much time as needed to read and respond to the provided communication. We utilized the same micromessages from Buchanan et al. (2022), which along with policy information, contained words and small phrases congruent with growth mindset (e.g., challenges as learning opportunities, developing learning strategies) or appreciative advising (e.g., connections, reminders of past successes) approaches. The control group received an advisor message that included
TABLE 1 Demographic Information for Participants in Study 1 and Study 2
TABLE 2
Statistics
Note. All variables were measured on a scale of 1–7, with higher scores reflecting more positive outcomes (e.g., higher levels of support/confidence).
only the policy information (i.e., no micromessaging related to growth mindset or appreciative advising).
After receiving the message, participants responded to a series of five questions employed to measure their affective and (anticipated) behavioral responses (Buchanan et al., 2022). For instance, participants responded to questions such as, “How supported would you feel by your advisor?” and “How likely would you
Effect of Messaging on Student Success Outcomes
be to give up on the course after this interaction?” using a 7point scale. We averaged the items, reverse scoring items when appropriate, into a single measure of student success outcomes (ranging from 1–7), with higher scores indicating more positive student outcomes (i.e., greater feelings of support and confidence, more persistence, resource seeking, and following up). Buchanan et al. (2022) reported a Cronbach’s alpha of .78 for their measure of student outcomes, and our first study yielded a comparable alpha of .79.
We also included two items related to students’ likelihood of disclosing learning challenges or disabilities to different sources. Specifically, we asked participants (order randomized), “How likely would you be to disclose your learning ability/disability status to your Advisor (Disability Services)?” using a scale ranging from 1 (extremely unlikely) to 7 (extremely likely). Prior to debriefing, participants completed attention check and demographic questions (e.g., age, race/ethnicity, gender, firstgeneration college student status).
Results
To examine the impact of messaging conditions on student success outcomes and likelihood of disclosing learning disabilities, we conducted ANCOVAs, controlling for GPA, age, and firstgeneration student status.
H1: Messaging and Student Success Outcomes
The effect of messaging condition approached significance, F(2, 96) = 2.82, p = .06, η2 = .05. Importantly, in line with prior research and our hypotheses, planned contrasts revealed the predicted pattern of data between groups who received micromessages vs. those who received information only. Given that there were no differences between the two groups ( p < .58), we combined students who received either growth mindset or appreciative advising micromessages into a single group for comparison with those who received no micromessages. Compared to those who did not receive advising communications containing micromessages ( M = 5.02, SD = 1.15), students who saw either growth mindset or appreciative advising themed micro messages experienced more positive student outcomes (M = 5.38, SD = 1.02), F(1, 97) = 4.09, p = .046, η2 = .04. Participants' GPA, age, and firstgeneration student status were not significant predictors of student success (all ps > .12). These results suggest that advising micromessages may have a positive impact on student success outcomes, partially supporting H1. See Figure 1 for aggregate student success outcomes broken down by our three messaging conditions and Table 2 for means and standard deviations for each student success outcome measure separately.
FIGURE 2
Effect of Messaging on Disclosure of Learning Disability to Advisor
Note. Overall (omnibus) differences among the three groups approached significance (p = .051). The difference between the micro-messaging groups (growth/appreciative) and the control group was significantly different (p < .04).
FIGURE 1
Note. Overall (omnibus) differences among the three groups approached significance (p < .07). The difference between the micro-messaging groups (growth/appreciative) and the control group was significantly different (p < .05).
H2a: Messaging and Likelihood of Disability Self-Disclosure
Parallel analyses were run for our added variables related to disclosure of learning disabilities. The ANCOVA predicting likelihood of disclosure from our three messaging conditions approached statistical significance, F(2, 96) = 3.06, p = .05, η2 = .06. See Figure 2 for advisor disclosure likelihood for each of our three messaging conditions. In line with our hypotheses and contributing to the literature, participant responses differed significantly based on whether they received advising communications containing micromessages. Students reported being significantly more likely to disclose a learning disability to their advisor when they were exposed to either growth mindset or appreciative advising micromessages (M = 5.67, SD = 1.67), compared to informationonly communications (M = 4.94, SD = 1.79), F(1, 97) = 4.54, p = .036, η2 = .04. Neither GPA, age, nor firstgeneration student status emerged as a significant predictor of selfdisclosure (ps > .18). These results suggest that advising micromessages may encourage students to disclose learning disabilities to their advisors, supporting H2a.
H2b: Messaging and Likelihood of Disclosure to Disability Services
As anticipated, our messages did not have a significant impact on students’ likelihood to disclose to a separate entity (i.e., Disability Services), all ps >.24.
Finally, given the findings of Buchanan et al. (2022) and research on the impact of intensive advising practices on underserved or historically underrepresented student outcomes, we examined whether the positive effect of the micro messages might benefit some students more than others (i.e., racial minority/ first generation vs. White/continuing generation). Interestingly, we found no significant interactions between micro messaging and underrepresented student identity on our student success or disclosure variables (all ps > .24), suggesting that the effectiveness of our micromessages did not differ systematically based on these student identities.
Overall, Study 1 found that students who received advising communications containing micromessages related to growth mindset or appreciative advising reported more positive student outcomes and were more likely to disclose a learning disability to their advisor (but not a separate entity). These findings partially support H1, H2a and H2b, highlighting the potential of micromessaging as a tool for promoting a supportive advising and learning environment.
Discussion
Replicating the pattern of effects found in Buchanan et al. (2022), we found that, when students received
communication from their advisor that included micromessages related to either growth mindset or appreciative advising approaches, they reported more positive outcomes ranging from greater feelings of support to intentions to persist when experiencing challenge when compared to students who received information only communications. We also found some initial, albeit tentative, evidence that students may be more likely to disclose a learning disability to their advisor (but not Disability Services) when receiving advising communications containing these micromessages. This consistent pattern of results highlights the potential of micromessaging as a tool for promoting a supportive advising environment.
Given these findings, we sought to extend this research by investigating whether combining growth mindset and appreciative advising approaches would produce the aforementioned benefits for students while allowing advisors more flexibility to combine approaches to suit their styles and needs. Both micro messages have been shown to increase positive student outcomes albeit in potentially different ways. For example, growth mindset messaging fosters resilience through willingness to embrace challenges, while appreciative advising works through highlighting advisor’s support and belief in the student’s strengths. Integrating these two approaches into a single advising message may help to provide students with a sense of both capability and belonging.
Study 2
Building on the findings from Study 1, Study 2 explored whether combined growth mindset and appreciative advising language within advisor communications would benefit students. Our primary aim was to test whether this integrated approach could support students’ success and offer a more practical and flexible framework for enhancing advisor communications to meet students’ needs. Thus, in Study 2, we examined whether students who received a combined micromessage incorporating both growth mindset and appreciative advising language would report higher levels of student success compared to those who received an informationonly advising message.
Method Participants
We first ran an a priori power analysis using G*Power (Faul et al., 2007) to determine the necessary sample size for the study. Based on a medium anticipated effect size, an alpha level of .05, and a desired power of .80, the analysis indicated that a total sample size of 158 participants would be required. Therefore, we collected data from 166 participants (Mage = 21.90, SD = 5.63) who were students at a University in the Pacific Northwest
currently enrolled in a psychology course and received extra or class credit per their instructor. Most participants identified as White (62.0%) and female (65.0%). See Table 1 for additional demographic information.
Materials and Procedure
We once again replicated the procedure used in Buchanan et al. (2022) with one exception. Rather than present the advising micromessages separately, we create a combined growth mindset and appreciative advising message by including key words and phrases from both original micromessages (see Supplemental Materials at https://osf.io/ub7n4/). Specifically, as part of this short (10–15 minutes) individually completed online study, participants were asked to imagine that they had reached out to their advisor because they were struggling with a challenging class and received a reply message from their advisor. Participants were randomly assigned to receive a message that did or did not include combined growth mindset and appreciative advising micromessages (e.g., emphasis on effort/strategy and warmth/connection). Following the message, participants were asked to complete the same 5item measure of student success (e.g., support, persistence, confidence) using 7point scales. Responses for the items were averaged (α = .84) with higher scores reflecting higher level of student success outcomes (e.g., greater persistence, feeling more supported). Finally, participants answered demographic questions, were debriefed, and thanked for their participation.
Results
To test whether our combined micromessage incorporating both growth mindset and appreciative advising language would benefit students, we conducted an independentsamples t test. Specifically, our analysis was conducted to determine if students who received
TABLE 3
Descriptive Statistics for Student Success Outcome Measures in Study 2
Note. All variables were measured on a scale of 1–7, with higher scores reflecting more positive outcomes (e.g., higher levels of support/confidence).
the combined micromessage reported higher levels of student success compared to those who received an informationonly advising message.
H3: Combined Messaging and Student Success Outcomes
In line with our hypotheses, we replicated the positive effects of Study 1 on student outcomes using a combined growth mindset and appreciative advising message. Students who received communications from their advisor that included both growth mindset and appreciative advising micro messages exhibited higher levels of our student success outcomes (M = 5.06, SD = 1.37) compared to those who received messages with information on relevant policy, but without micromessages (M = 4.64, SD = 1.18), t(164) = 2.13, p = .018, d = .33. These results support H3, indicating that the combined micromessage condition resulted in more positive student outcomes compared to the informationonly control condition. See Table 3 for the means and standard deviations of each student success outcome measure separately. These findings suggest that combining growth mindset and appreciative advising in advisor communications can be a promising strategy for improving student success
General Discussion
In the last several decades, U.S. colleges and universities have become increasingly diverse, yet historical and institutional disparities persist (ChicasMosier et al., 2023). According to a recent report released by the U.S. Government Accountability Office (2024), the percentage of students with disabilities enrolled in a college institution increased to 21% in the 2019–20 year. Despite increases, most students do not inform their college of their disability (National Center for Education Statistics [NCES], 2022). Potential reasons for nondisclosure by students may include a lack in knowledge of services offered at their institution as well as possible negative experiences with faculty or staff. Furthermore, the rate of students with a disability to graduate within a 4year program is 48.5% compared to 68% for students without a disability (NCES, 2019). Given equity gaps in academic achievement and rates of graduation (Haeger et al., 2024), it is vital that universities consider adopting appreciative advising methods that emphasize growth mindset for diverse populations of students.
As such, this study sought to build upon prior knowledge in research by Buchanan et al. (2022) that found the positive impact of appreciative advising and growth mindset in micromessaging on student success outcomes (e.g., feelings of support, selfconfidence). Expanding upon this previous research, we found
Preston, Yanes, and Buchanan |
that both the appreciative advising approach or the inclusions of growth mindset language into advising communications leads to students being more comfortable disclosing learning difficulties with their academic advisor. This effect was not observed for disclosure to another entity on campus, likely due to the relevance of the messaging to the student–advisor relationship and the extra effort necessary to disclose to another source.
Further, the research conducted by Buchanan et al. (2022) found that firstgeneration students (and students of color) exhibited more pronounced increases in student success outcomes because of advisor micromessages. Although our research did not detect an effect of firstgeneration student identity on our outcomes of interest, this might have been due to a lack of adequate power to detect the effect. Given a more diverse and larger sample size, we might also anticipate that firstgeneration students with disabilities may benefit from appreciative advising and fostering growth mindset. Utilizing key aspects of appreciative advising and growth mindset, advisors may actively help students to recognize their unique strengths, skills, and successes, which can help them to build persistence in their challenges and potentially the disclosure of their disability. Additionally, developing a growth mindset can impact students’ attitudes towards difficulty and decisionmaking, which may increase retention and graduation rates. We encourage future studies to further investigate the collaborative nature of appreciative advising with a more targeted population to concentrate on these possible effects.
Indeed, our study helps to demonstrate that, when advisors use the appreciative approach, students tend to feel more empowered to overcome their challenges and achieve their goals. Because appreciative advising and growth mindset messages can help students to gain a sense of belonging, they can feel more comfortable in seeking out academic advisors for supports and encouragement, leading to a greater awareness of potential resources available to them. In this aspect future studies may explore potential factors in addition to appreciative advising and growth mindset such as an equitycentered approach, traumainformed practice, and advocacybased advising, all of which may help to address systemic barriers and cultivate holistic student wellbeing.
Implications
The current studies contributed to a broad body of literature in both appreciative advising (e.g., Butler et al., 2016; Virtue et al., 2021) and growth mindset (e.g., Dweck, 2006; Pekrun et al., 2009) by replicating previous results (e.g., growth mindset contributing to student success). Additionally, these studies helped us to determine one way in which we can encourage students
to disclose learning disabilities to their advisors, which is of importance because disclosure of disabilities is associated with better student outcomes (e.g., higher retention rates, lower rates of academic probation; Mamiseishvili & Koch, 2011; Newman et al., 2019). Interestingly, students were not more likely to disclose disabilities to other campus officials (e.g., professors, disability services), suggesting that some effects of the micromessages may be limited to the student–advisor relationship. Although implementing micromessaging related to appreciative advising and growth mindset was shown to result in positive student outcomes here and in recent research (e.g., Buchanan et al., 2022; Kyte et al., 2020), it is important to note that recent work suggests that growth mindset might be most beneficial to students of high socioeconomic status (King & Trinidad, 2021) and can backfire, leading to feelings of shame when students are not able to overcome structural barriers with their mindset (Moreau et al., 2019).
Given previous literature and our current findings, it is clear that how academic advisors interact with their students is important and impactful. Utilizing appreciative advising and growth mindset messaging can assist advisors in building healthy relationships with their students, thus increasing rapport and the likelihood of student success. Advisors may utilize micromessages in a variety of ways, including sending short messages that focus on student strengths, accomplishments, and growth. These messages might include asking positive openended questions that help students reflect on recent achievements, even small ones, and consider possible strategies for improvement. As such, micromessages that involve growth mindset language should be tailored to the individual students’ strengths and needs, referencing specific goals that have been identified by the student. Appreciative advising messages that foster growth mindset not only have the potential to motivate students to persist through their challenges but build trust and meaningful connections to develop a strong student–advisor relationship. Additionally, having a good rapport with students makes difficult conversations (e.g., failing a class, switching a major) easier to navigate for both the advisor and the student. Although all staff and faculty at universities could implement these strategies, it is of particular importance that advisors consider adopting this type of micromessaging, as they are often the first university employee that students interact with.
Important to note, for appreciative advising and growth mindset to be effective through micromessaging, college institutions may consider providing specific trainings for advisors. Academic advisors can learn to utilize Bloom and Hutson’s (2007) sixstep method to appreciative advising as well as develop their own practice
in growth mindset towards their careers. In this model, advisors can integrate the studentcentered approach to assist them in identifying and focusing on student strengths that help them to emphasize positive aspects of a variety of educational situations and inspire growth.
Strengths, Limitations, and Future Directions
One important strength of our research is that it both replicated and extended the work of Buchanan et al. (2022). We not only replicated previous findings on how advisor communications have the potential to positively impact student success but expanded our understanding of its broader impacts (i.e., disclosure of disability).
Another strength of this work is in the combining of growth mindset and appreciative advising language into advisor communications with students experiencing challenges. This method provides insights for improving academic advising practices while showing how advisors might more flexibly combine approaches in a way that feels genuine for them and their relationship with the student. However, our study methods did not allow us to directly compare the effects of combined growth mindset and appreciative advising messages to the individual effects of each approach on its own. Future research could explore whether combining these strategies produces even greater improvements in student success than either growth mindset or appreciative advising messaging alone, offering a more powerful and flexible tool for supporting students.
The hypothetical nature of our advising scenario does limit our ability to generalize our findings. Had we used communications from students’ actual advisors, we might have had different results based upon previous interactions. Given that positive academic outcomes increase when students develop rapport with advisors (Butler et al., 2016; Virtue et al., 2021), we may see that micromessaging from established advisors have an even greater positive outcome on student success outcomes. Additionally, a longitudinal study tracking student communications/experiences with advisors and student progress through their undergraduate career could help establish the cumulative impact of advising communications and how that is impacted by factors such as length of student–advisor relationship. Further, although we relied primarily on measures used in previously published research, we generated our own disabilitydisclosure, singleitem measures. Future research would benefit from focusing more on this variable, and using a longer, established scale to assess disclosure.
Additionally, we utilized a Western, Educated, Industrialized, Rich, and Democratic (WEIRD) sample, which may impact generalizability across cultures. In general, there is evidence that intelligence is
conceptualized differently across different cultures (Clegg et al., 2017), which may lead to changes in the efficacy of growth mindset communications in the academic setting. For example, there is some evidence that adopting a growth mindset in academic settings may have a negative impact on academic outcomes in Chinese classrooms compared to U.S. classrooms (Sun et al., 2021). Further, given the differences in advising practices across institution types even within the United States (Pardee, 2004), there is good reason to explore how these micromessages may differentially impact students at smaller vs. larger or public vs private institutions. Finally, although attrition tends to be a more pressing issue for incoming students as they acclimate to the college experience, our studies did not limit participation to firstyear students. It is reasonable to expect that we may have seen larger effects (or an interaction involving marginalized student status) if we had focused on how these messaging types impact firstyear students rather than college students in general.
Future research in this area would benefit from engaging in field studies to ensure that these results replicate in reallife scenarios. For example, a longitudinal study that follows a cohort across their undergraduate career and tracks changes in positive student outcomes could be beneficial. Also, conducting multiple studies across different university types (e.g. public/private, large/ small) could give a better idea of how effective micromessaging is when incorporated into varied advising models. Additionally, testing appreciative advising and growth mindset messaging could be helpful in other areas (e.g., workplace, high school), but further study is needed to see if results replicate in these scenarios. Lastly, cultural differences should be explored to determine if these findings are able to be applied across cultures, or if they are specific to WEIRD cultures.
Although we did not find any effects or interactions involving student identity across our studies, many researchers have suggested that effective advising could be a tool for increasing equity in education. For example, Buchanan et al. (2022) found such advising micromessages to work well for students generally, but to be especially impactful in increasing positive student outcomes for firstgeneration students and students of color. The results of our studies suggest that the benefits of incorporating appreciative advising and/or growth mindset into advising communications has the potential to benefit increasingly diverse student bodies more broadly. As such, we encourage researchers to continue to explore how various forms of advising may uniquely impact diverse student populations, particularly underrepresented groups, and to further refine these strategies to foster inclusivity and improve student success in higher education.
FALL 2025
Preston, Yanes, and Buchanan
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Author Note
Tonya M. Buchanan https://orcid.org/0000000286523405 View supplemental materials at https://osf.io/ub7n4/ Correspondence regarding this article should be addressed to Tonya M. Buchanan, Central Washington University, 400 E University Way, Ellensburg, WA 98926, United States.
Email: tonya buchanan@cwu.edu
She Wears It How? Perceived Racism and Colorism Relate to Hair Texture Dissatisfaction in Black Women
Tori A. Elliott, Denise M. Martz*, Doris G. Bazzini*, and Shraddha M. Selani Department of Psychology, Appalachian State University
ABSTRACT. Black women in America are affected by racism and colorism—social influences which can impact their unique bodyimage features such as hair texture dissatisfaction and their use of hazardous chemical hair relaxants. A Qualtrics survey of Black women who had used hair relaxants (N = 316), conducted via Prolific, explored the relationship between perceived racism and colorism and hair texture dissatisfaction, assessed using the Hair Texture Dissatisfaction Scale (HTDSS) and a newly developed Hair Texture Ideal and Discrepancy (HTD) scale. Racism was associated with both measures of hair dissatisfaction (HTD: r = .12, p = .03; HTDSS: r = .13, p = .04), but not hair relaxant use (r = .03). Similarly, colorism was also associated with both measures of hair dissatisfaction (HTD: r = .14, p = .02; HTDSS: r = .19, p = .01), but not hair relaxant use (r = .09). A straighter ideal hair texture was most related to hair dissatisfaction (HTDSS: r = .49) and frequency of hazardous hair relaxant use (r = .19, p < .001). Social identity theory (SIT) helps to explain how the prejudicial social forces of racism and colorism can be internalized for Black women and expressed in body image dissatisfaction visàvis White beauty ideals. These findings suggest that hair texture dissatisfaction warrants further research as hair texture dissatisfaction relates to Black women’s body image.
Keywords: hair texture discrepancy, dissatisfaction, relaxants, racism, colorism, women
Across many parts of the world, beauty ideals are deeply racialized, often prioritizing traits typically associated with White individuals— such as lighter skin and straighter hair—as the standards of beauty for women of color (Harper & Choma, 2019). These White beauty standards have been extensively analyzed within the frameworks of racism and colonialism within the United States (Harper & Choma, 2019; Mathews & Johnson, 2015; Patton, 2006; Price, 2020). Social identity theory (SIT) explains how individuals’ selfidentification as members of social groups shapes their intergroup behaviors and influences intragroup processes (Hughes et al., 2015; Tajfel & Turner, 2004). This theory posits that comparisons with other groups are used to maintain or enhance positive
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social identities. Such comparisons can have positive effects when, for instance, women of color align with African American beauty standards that emphasize individuality (Webb et al., 2013). The Black Pride movement, which emerged during the Civil Rights era, played a pivotal role in challenging Eurocentric beauty norms and promoting natural hairstyles as symbols of resistance, empowerment, and cultural identity (Byrd & Tharps, 2014). However, when these comparisons yield negative outcomes (e.g., Black women who feel they are not meeting White beauty standards), individuals may experience a diminished social identity. To restore a more positive social identity, individuals often employ various processes and strategies (Scheifele et al., 2020). As such, women of color may use hair care practices to
align with or resist prevailing beauty standards, thereby impacting their social identity and sense of belonging.
Hair presentation has remained an important feature of Black culture dating back to American slavery (Patton, 2006). Hair holds both spiritual and social significance and was a key part of selfidentity for enslaved African Americans (Patton, 2006). In the 15th century, hair was even utilized as a form of communication, with pathways being braided onto the scalp (Johnson & Bankhead, 2014; Patton, 2006). This was especially common among the Wolof, Mende, Mandingo, and Yoruba tribes (Johnson & Bankhead, 2014). After European colonists learned the communicative purpose of hair within certain African communities, colonists began having Africans’ heads shaved upon enslavement, stripping them of their humanity (Patton, 2006). As slaves were not allowed cosmetic products, they used household items like bacon grease and butter to retain moisture in their hair (Patton, 2006).
In contemporary American beauty standards, the notion of “good hair” persists, characterized by straightness, softness, ease of manageability, and the ability to grow long with minimal styling or treatment—attributes that align more closely to White hair compared to Black hair, which tends to be tightly coiled (Johnson et al., 2017; Robinson, 2011). This social pressure to obtain “good hair,” as perpetuated through mainstream media, is closely related to internalized racist and colorist ideals that may lead to problematic beauty practices among Black women (Johnson et al., 2017; Price, 2020). Engagement in potentially harmful beauty practices (e.g., hair relaxing) and high levels of hair dissatisfaction (i.e., unhappiness with hair presentation) are common experiences among Black women (Harper & Choma, 2019). Through sociocultural mechanisms such as the media, racist and colorist social constructs shape the beauty standards that women are expected to meet (El Jurdi & Smith, 2018). We posited that, to the extent to which Black women associate the White beauty ideal with a positive social identity, hair dissatisfaction and engagement in beauty practices that seek to better conform to White standards (e.g., relaxing) would be more likely to occur. Consequently, modern hairstyling methods in the Black community include chemical relaxers, hot combing, ironing, and sewins, which alter the natural orientation of the hair (Rucker Wright et al., 2011). Chemical relaxers may be introduced into hair routines, even as early as age six (Bellinger, 2007). Use of chemical relaxants can be especially hazardous. These products are applied to the hair to permanently alter the hair pattern and can cause negative health effects such as dermatitis, chemical burns, hair breakage, and hair loss (Donahoo & Smith, 2022). More critically, they have even been
linked to increased rates of breast and uterine cancer (Drugwatch, 2024; Stiel et al., 2016). According to the National Institute of Health (NIH) Sister Study, which includes 33,497 women aged 35–74, those who used hair straightening products—predominantly women of color—had more than twice the risk of developing ovarian cancer over an 11yearfollow up period compared to those who did not (Chang et al., 2022). As a result, a 2024 proposal aims to have the U.S. Federal Drug Administration (FDA) ban formaldehyde, a known carcinogen, from hair straightening products—a measure that the National Institute of Environmental Health Science (NIEHS) asserts is long overdue (Drugwatch, 2024). Further, African Americans spend $225 million annually on hair products and services, with Black women spending three times more than their White counterparts (Patton, 2006). This increased spending may stem from social pressure to conform to White beauty ideals. Examining the social and cultural forces, such as racism and colorism, that potentially fuel hair texture dissatisfaction and the potentially lifethreatening use of chemical relaxers among Black women is of paramount importance. Because of this, we deliberately recruited women who reported using the products within the past year as an initial study to better understand chemical relaxing behavior.
Perceived Racism and Hair
People of color have been victims of racism for centuries in America since the enslavement era in the 17th through 19th centuries. Racism can be defined as the application of prejudice by the use of power directed toward members of certain ethnic and racial groups (Jones & Carter, 1996; Pieterse et al., 2012). Perceived racism can (co)occur at many magnitudes, particularly at the institutional, cultural, and interpersonal levels (Pieterse et al., 2012). People of color may report exposure to racism and the extent to which they perceive the situation to be stressful, but it is sometimes difficult to ascertain apparent racism due to microaggressive and covert behaviors (Carter, 2007; Pieterse et al., 2012).
Hair is among the traits that may quickly identify an individual as being a member of a racial category, subjecting them to the associated stereotypes and biases of that category—a phenomenon that can be described as racial phenotypicality bias (Maddox, 2004). For Black girls and women, those whose hair texture is most closely associated with “Blackness” are more likely to be subjected to the (often negative) corresponding stereotypes and evaluations surrounding their race (Maddox, 2004). This treatment, whether positive or negative, can influence their self esteem and racial identity formation, but also their perceptions of how
they are viewed by others (Johnson & Bankhead, 2014; Price, 2020). Racial identity and perceived racism have the potential to impact the choices Black women make regarding their hair presentation and the products that they use (Lukate & Foster, 2023; Price, 2020). The persistent workplace discrimination faced by Black women due to their hair texture underscores this dynamic. The National Association for the Advancement of Colored People (NAACP; 2020) has even released a document entitled Hair Discrimination is Race Discrimination that highlights how hair is frequently used as a marker of race exposing individuals to bias and exclusion.
Perceived Colorism and Hair
Colorism is defined as prejudicial treatment and discrimination of samerace people based on differences in physical features, such as skin complexion, facial features, and hair texture, within a society that upholds Eurocentric culture (Griffin, 2021). In this sociocultural context, darkerskinned individuals are often unfairly stereotyped as “dirty,” ‘lazy,” or even “ignorant” (Hunter, 2007; Sims, 2009; Sims & Hirudayaraj, 2016). Rooted in European colonialism, colorism has been documented since the 19th century, beginning with the enslavement of African American and Latin populations (Patton, 2006). White slave owners afforded greater privileges to lighterskinned slaves by assigning them to roles within the home, while dakerskinned slaves, often relegated to fieldwork, were subjected to harsher treatment (Patton, 2006). Colorism in turn, has been internalized by many people of color today, fostering a preference for lighter skin and straighter hair, both in their own selfperception and in their perceptions of other Black individuals.
Existing research on body image has primarily focused on the impact of colorism related to skin tone satisfaction, while dissatisfaction with hair texture among Black women remains relatively underexplored. Having a darker skin tone has been linked to lower selfesteem among Black women, a relationship that is not observed among Black men (Thompson & Kieth, 2001). Further, young Black women tend to hold less favorable attitudes toward darker skin among other Black women (Mathews & Johnson, 2015). Although colorism may impact all individuals of color, its prejudice is regarded as a gendered phenomenon in that colorism is especially damaging for women of color compared to men of color (Sims & Hirudayaraj, 2016; Thompson & Kieth, 2001).
Texturism refers to the prejudice that privileges loose, straight hair textures over coiled or tightly textured hair (Griffin, 2021). Existing literature suggests an explicit preference for smooth hair over naturally textured hair (Johnson et al., 2017; Rudman & McLean, 2016; Woolford et al., 2016). This bias manifests not
only within the broader context of racism—where Black women face discrimination based on their hair texture—but also within their own communities, as it intersects with colorism. As such, hair dissatisfaction among Black women may prompt them to engage in potentially harmful beauty practices, such as using chemical relaxants to permanently straighten their hair. Although there is limited research specifically addressing texturism and its impact on Black women’s body image, it is often directly associated with colorism, which has been more extensively studied (Hunter, 2007; Sims, 2009; Sims & Hirudayaraj, 2016). In this study, we conceptualize hair texture as an integral aspect of colorism, drawing on social identity theory to explore how these intersecting biases influence hair presentation among Black women.
The Present Study
Lowy and colleagues (2021, p. 323) have made a call to encourage scientists to apply “culturally sensitive and intersectionalityinformed theory” to improve body image research that is more inclusive and addresses the unique needs of individuals of color. Further, Watson et al. (2019) emphasized the importance of contextualizing the unique body image concerns of women of color within the intersections of gender and racial oppression. These distinctive issues for women of color may include aspects of skin tone (Harper & Choma, 2019; Selani et al., 2023) and hair texture (Harper & Choma, 2019) that may impact their body image and how they are perceived and treated in modern society. Due to the lifethreatening health hazards of using hair relaxants (Chang et al., 2022; Donahoo & Smith, 2022; Drugwatch, 2024; Stiel et al., 2016), we deliberately recruited women who used them to determine if their hair dissatisfaction was associated with the sociocultural forces of racism and colorism. The present study focused specifically on hair texture dissatisfaction, hair texture discrepancy, and use of toxic hair relaxants among Black women in relation to racism and colorism, which are factors known to negatively impact body image among this population (Awad et al., 2015; Harper & Choma, 2019; Paradies et al., 2015; Pieterse et al., 2012). By exploring these issues through the lens of social identity theory, this research seeks to understand how racial prejudice and colorism influence hair dissatisfaction and consequent use of harmful chemical hair relaxants. Drawing from existing social psychology and body image research, we based our investigations on the following five main exploratory research questions:
1. Are there significant correlations between hair texture dissatisfaction and racism and colorism? We hypothesized that hair texture dissatisfaction
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would be significantly and positively associated with perceived racism and colorism.
2. Are there significant correlations between hair texture discrepancy and racism and colorism? We hypothesized that hair texture discrepancy would be significantly and positively associated with perceived racism and colorism.
3. Are there significant correlations between chemical relaxing frequency and racism and colorism? We hypothesized that chemical relaxing frequency would be positively associated with perceived racism and colorism.
4. Is there a correlation between hair texture discrepancy and frequency of chemical relaxant usage? We hypothesized that greater hair texture discrepancy would be positively associated with greater chemical relaxant use.
5. Is there a correlation between ideal hair texture and chemical relaxing frequency? We hypothesized that a straighter ideal hair texture would be significantly associated with greater chemical relaxing frequency.
Method
Participants
An internal grant awarded to the researchers by their university was used to provide compensation to the participants for this study. A total of 400 participants who identified as Black women that used hair relaxants were recruited from the Prolific platform and received monetary compensation ($1.06 each) directly through the website after completing the survey. However, 39 participants were removed from the study due to incomplete data (i.e., not responding to all survey items), 42 were removed for failing the attention check, two were removed for being White or Hispanic, and one other participant was removed for identifying as a male. Participants had an average age of 32.6 (SD = 11.2; range: 18–72). Of the final study sample of 316 participants, 91.8% identified as Black/African American, and 8.2% identified as Multiracial/Biracial. For the reported levels of education, 35.4% graduated high school, 24.4% had an associate’s degree, 31.0% had a bachelor’s degree, 6.9% had a master’s degree, and 2% had either a specialist’s degree or a doctoral degree. Most of the sample identified as straight/heterosexual (70.5%). The rest of the sample identified as bisexual (20.3%), gay/lesbian (4.4%), pansexual (2.0%), queer (1.2%), or other (1.2%). In terms of gender identity, 1.3% of participants identified as transgender. Institutional Review Board (IRB) exemption status was granted on September 22, 2022. This research adhered to all aspects of the American Psychological Association’s ethical guidelines.
Measures
Demographics
A demographics questionnaire was administered that included questions about age, race, ethnicity, sexual orientation, gender, and the highest level of education. If prospective participants did not identify as a Black woman (i.e., endorsed male, transmale, nonbinary), were not between 18 and 99 years of age, and did not endorse relaxing their hair at least once in the past year, the survey ended, and they were excluded from participating in the study.
Perceived Racism
Perceived racism was measured using a 9 itesubscale that assessed the reports of ever day discrimination from the Detroit Area Study Discrimination Questionnaire (DAS DQ; Williams et al., 1997). The items are rated on a 6point scale ranging from 1 (almost every day) to 6 (never). The scale was flipped from 1 (never) to 6 (almost every day) so that a higher score on the measure would be indicative of greater levels of perceived racism. Items were totaled, with the highest perceived racism score being 54. In the current study, Cronbach’s alpha was high (α = .92).
Perceived Colorism
Perceived colorism was measured using the InGroup Colorism Scale (ICS; Harvey et al., 2017). The scale is comprised of 20 items rated on a 7point Likert scale and includes five subscales: SelfConcept (SC), Impression Formation (IF), Affiliation (AF), Attraction (AT), and Upward Mobility (UM). The response options range from 1 (strongly disagree) to 7 (strongly agree). The revision made to the original ICS items include changing “skin tone” to “hair texture.” This revision was made to better account for bias related to hair texture rather than skin tone. The subscales of ICS demonstrated coefficient alphas of .87 (SC), .73 (IF), .90 (UM), .81 (AT), and .80 (AF) in the scale development and validation study featuring a sample of Black Americans (Harvey et al., 2017). The mean of all items was calculated to create an overall score ranging from 1 to 7, with higher scores indicating greater internalization of ingroup colorism (α in the present study = .86).
Hair Texture Discrepancy (HTD) Scale
The HTD scale (Appendix A) was developed to assess participants’ desire for an altered hair texture by capturing the discrepancy between their natural and ideal hair textures using a visual measure of various hair types. The visual images used in this measure were adopted from Loussouarn et al. (2007). Each of the eight hair patterns were scored from 1 to 8, with “1” representing the straightest hair pattern and “8” representing the
1.
2.
3.
4.
5.
6.
curliest hair pattern. We used two items: natural hair texture and ideal hair texture. The natural hair texture item asked the respondent to identify which hair pattern most accurately represented her natural hair texture. The ideal hair texture item asked which hair pattern the woman desired. Subtracting the natural hair texture from the ideal hair texture yielded the HTD rating. A score of zero was indicative that one’s natural and ideal hair textures matched (perfectly happy with their hair). To avoid negative values in the statistical analysis, a constant of eight was added to all scores, converting any negative values into positive ones. Higher positive scores reflected a greater desire for straighter hair, and thus more hair texture dissatisfaction. This approach aligns with common practices in body image research, where the comparison between self and ideal is used as a metric of dissatisfaction (Anton et al., 2000; Fitzgibbon et al., 2012; Williamson et al., 1993). The mean difference for our participants was 1.42 (SD = 1.99), suggesting that, on average, participants desired their hair to be approximately one and a half units straighter on the HTD scale. Additionally, participants were asked if the scale was representative of their hair type. Possible responses were “Yes” or “No.”
Ideal Hair Texture
Ideal hair texture (Appendix A) was determined by asking participants on the HTD, “Which is the hair texture you would most like to possess?”
Frequency of Relaxing History Scale (FRHS)
A novel selfreport frequency measure (Appendix B) was used to assess the prevalence of chemical hairrelaxing behavior. The items were rated on a 6point scale ranging from 0 (never) to 6 (almost every day). Higher scores indicate a higher frequency of chemical hairrelaxing behavior in participants. The average score was 3.36 (SD = 1.07), indicating that, on average, participants reported engaging in this behavior with a frequency between “a few times per year” and “a few times per month.”
TABLE 1
Descriptive Statistics and Correlations for Study Variables
Hair Texture Dissatisfaction Scale (HTDSS)
A modified version of the 7item Skin Color Satisfaction Scale (Appendix C; SCSS: Falconer & Neville, 2000) was used to measure hair texture dissatisfaction. Participants rated their agreement on a scale ranging from 1 (strongly agree) to 9 (strongly disagree). For this study, revisions made to the preexisting SCSS included changing “skin color” to “hair texture” for all items. Hair texture dissatisfaction scores were calculated by reverse coding items (e) and (f). A score was created by averaging the items, with higher scores indicating greater dissatisfaction with one’s hair texture. The original SCSS measure demonstrated an acceptable internal consistency score (α = .71) for a sample of African American undergraduate women (Falconer & Neville, 2000). For this study, all items were included in data analysis and Cronbach’s alpha was measured (α = .55).
Attention Checks
To enhance data reliability and mitigate the influence of bots and botlike participants, one attention check was implemented to ensure participants were focused and attentive to the survey. This item, previously used by Abbey and Meloy (2017), clearly instructed participants on which answer to select (e.g., “Select answer three for this item”). Therefore, 42 participants who failed this attention check or gave incomplete data were removed from the data set and did not receive financial compensation.
Procedure
Participants were recruited through Prolific, a research platform used for online humansubject recruitment. Each participant was redirected to a Qualtrics survey via an embedded link. Participants were informed that the survey was focused on Black women’s experiences related to hair texture. The beginning of the survey included a consent form describing the purpose of the study, their rights as participants, and the researchers’ contact information. Participants confirmed their answers to the inclusion criteria, completed the various measures, reported demographic information, and completed an attention check. Upon completion of the survey, participants received monetary compensation of $1.06 directly through Prolific. The average time to complete the survey was eight minutes. All procedures were approved by the IRB of the authors’ institution.
Results
Descriptive analysis was performed to assess the face validity of the HTD scale. A substantial 89.2% majority of participants affirmed that the scale accurately represented their hair type. Conversely, 10.8% of participants
indicated that the scale did not accurately reflect their hair texture. This discrepancy is further explored in the limitations section.
Bivariate Pearson’s correlations were run for all variables and are presented in Table 1. The analysis indicated that perceived racism was significantly related to perceived colorism (r = .27, p < .001). Racism was associated with both measures of hair dissatisfaction (HTD: r = .12, p = .03; HTDSS: r = .13, p = .04), but not hair relaxant use (r = .03). Similarly, colorism was also associated with both measures of hair dissatisfaction (HTD: r = .14, p = .02; HTDSS: r = .19, p = .01), but not hair relaxant use (r = .09). Frequency of hazardous hair relaxant use was correlated with having a straighter ideal hair texture (r = .19, p < .001) and having more hair texture dissatisfaction (HTDSS; r = .18, p = .002). Desiring a straighter ideal hair texture was associated with more hair dissatisfaction using both measures (HTD: r = .20, p < .001; HTDSS: r = .49, p < .001).
Because colorism (ICS) was associated with all of the variables of interest, we conducted further analyses on its subscales (see Table 2). Although no significant relationships were identified between the Self Concept and Affiliation subscales and hair texture dissatisfaction, a relationship was found between the Attraction subscale and more hair texture dissatisfaction (r = .25, p < .001), as well as a desire for a straighter ideal hair texture (r = .23, p < .001). Correlations were observed between the Upward Mobility subscale and hair texture dissatisfaction (r = .20, p < .001), as well as between the Impression Formation subscale and hair texture dissatisfaction (r = .16, p < .05).
Discussion
Despite the pervasive influence of racialized beauty standards, there is a notable scarcity of research examining the unique body image concerns relevant to Black women. Research that has investigated the experiences of Black women has not directly examined the role of hair texture dissatisfaction regarding beauty practices and the relationship between racism and colorism (Griffin, 2021; Harper & Choma, 2019). The present research, grounded in social identity theory, evaluated the role of perceived racism and colorism in Black women’s perceptions of their hair texture, what they considered to be ideal hair, and how this relates to chemical hair relaxant use. Results showed that perceived racism and colorism were modestly related to hair texture dissatisfaction and hair texture discrepancy in our sample. These findings are consistent with previous research showing that exposure to racist and colorist comments alters Black women’s interpretations of “good hair” (Dove, 2021) and align with SIT as individual’s selfconcept is closely tied to their group memberships (Hughes et al.,
2015; Tajfel & Turner, 2004).
Because the colorism scale (ICS) was modestly related to both metrics of hair dissatisfaction and contained individual subscales, we explored those individual constructs and found associations between the Attraction, Upward Mobility, and Impression Formation subscales with hair texture dissatisfaction. Specifically, the Attraction subscale, which examines the perceived attractiveness of various hair textures within one’s group, was associated with greater hair texture dissatisfaction and a preference of straighter ideal hair textures. An example item was, “I prefer straight hair over curly hair when choosing romantic interests” and this aligns with previous research suggesting that Black women may prefer similar traits in partners (Mathews & Johnson, 2015). The Upward Mobility subscale, or perceptions that hair texture affects social and economic mobility (e.g., “Hair texture plays a big part in determining how far you can make it”), correlated with hair texture dissatisfaction, consistent with industrial organization literature highlighting ramifications of natural hair in the workplace (Koval & Rosette, 2021; Summers et al., 2022). The Impression Formation subscale, assessing how hair texture influences others’ impressions suggests that negative perceptions of hair may contribute to greater dissatisfaction. Social identity theory, which posits that personal behavior is influenced by group perceptions (Hughes et al., 2015; Tajfel & Turner, 2004), may help us
TABLE 2 Descriptive Statistics and Correlations
Hair Texture Discrepancy (HTD)
to better understand this finding. When Black women feel their hair texture leads to negative judgments and lower status for their ingroup, it may lead to increased dissatisfaction with their hair and attempts to change it.
The SelfConcept and Affiliation subscales did not correlate with the hair texture measures however. This null finding may reflect subtle distinctions in how hair texture dissatisfaction and colorism interact with social identity. The SelfConcept subscale focuses on personal selfworth related to one’s hair texture (e.g., “My hair texture is an important part of my selfconcept”), while the Affiliation subscale addresses the desire to connect with others based on shared traits (e.g., Most of my friends tend to have the same hair texture”). Social identity theory posits that individuals derive their selfconcept from group membership and intergroup comparisons (Tajfel & Turner, 2004). It is possible that while the Attraction, Upward Mobility, and Impression Formation subscales directly engage with broader social judgments about hair texture, the SelfConcept and Affiliation subscales may reflect more intrinsic, stable aspects of identity that are less directly tied to external perceptions of hair texture.
Because of both cosmetic and serious health consequences of hair relaxant consumption, we examined how frequency of use was associated with these other constructs. We observed that hair texture dissatisfaction was associated with the use of chemical relaxants, aligning with prior research showing that hair texture dissatisfaction relates to the use of hair straightening products (Harper & Choma, 2019). Wanting an ideal hair texture that was straighter was related to chemical relaxants use, suggesting that women who aspire to a particular hair texture—perhaps influenced by Eurocentric beauty norms—are more likely to consume these hazardous products, even in the absence of a pronounced discrepancy with their natural texture. This finding is consistent with prior research showing that internalization of Eurocentric beauty ideals can influence grooming behaviors and the adoption of beauty practices aimed at meeting these standards (Awad et al., 2015). Additionally, the desire for straighter hair texture was associated with both measures of hair texture dissatisfaction. These findings are consistent with social identity theory (Tajfel & Turner, 1979), which suggests that societal ideals can influence personal identity and influence selfpresentation. Contrary to expectations, the hazardous use of chemical hair relaxing was not related to related to perceived racism and colorism, although it was associated dissatisfaction with one’s hair. Notably, hair texture dissatisfaction was associated with perceived racism and colorism. While this study was statistically underpowered to fully explore these
relationships, future research should examine whether hair texture dissatisfaction acts as a mediator between sociocultural forces such as racism and colorism and the use of hazardous hair products.
Limitations and Future Directions
The current study had several limitations. One of the most limiting factors was the inclusion of only Black women who had reported the use of hazardous chemical hair relaxant products within the past year. A more comprehensive comparison of this targeted group to those women who have never used such chemicals remains to be completed. Our decision to target this specific population was informed by the prevalence of reported chemical relaxant use among Black women (Bellinger, 2007; Harper & Choma, 2019) and the study’s aim to better understand the implications of this beauty practice.
Prior research shows that Black women partake in many hairaltering methods such as hot combing, ironing the hair, and sew ins (Rucker Wright et al., 2011). Further, the terms “relaxant” and “perm” may be used synonymously in social settings, despite having very different outcomes for hair texture. Chemical relaxers were not differentiated from perms in the inclusion criteria. Future research should clearly define a chemical relaxer for participants. Additionally, future research will need to develop broader measures of alternative styling that encompass Black women’s repertoire to more fully understand how hair “behavior” relates to hair dissatisfaction and racism and colorism. Second, approximately 11% of women did not rate our selfdeveloped hair texture discrepancy scale (HTD) as representative of their hair type. This may reflect difficulties in matching the online visual scale to one’s hair or failure to capture the kinkiest of hair textures. Future investigation should consider why Black women could not find representation within the developed scale despite a wide array of hair textures. Further, the present study faces several limitations related to the measures employed, particularly with the self developed hair texture discrepancy scale (HTD) and the validation status of other instruments. The HTD scale’s response options for measuring hair relaxant usage are notably unbalanced, ranging from “never” to “almost every day.” Given the potent chemicals in hair relaxers, it is improbable that most participants would use them “almost every day.” Future research may aim to use qualitative data involving asking participants to report the exact number of times they have used hair relaxers in the past year or may further account for a variety of hairaltering products. Additionally, as this study is among the first of its kind, many of the measures utilized have not undergone validation. Although the ICS and DASDQ scales have
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been wellvalidated, the revised version of the SCSS has not, introducing potential uncertainties in data analysis. The scarcity of validated instruments tailored to populations of color underscores the ongoing challenges in this area of research. As scholarship increasingly focuses on these populations, the development and validation of appropriate measures will be critical for enhancing the reliability of future studies.
Another salient limitation was that this study took place on an online platform, meaning that all participants were required to have reliable access to the Internet. This makes it difficult to include those from low socioeconomic (SES) or rural communities with little to no internet access. Research proposes that there is an imbalance in communication resources between urban and rural communities, creating a digital gap (Zhang et al., 2021). At almost every income level, rural households are less likely to own computers than those in urban areas (Consumers Union, 1999, as cited in Strover, 2001). It is plausible that the majority of participants come from fairly urbanized areas, making it critical to highlight potential differences in perceived levels of racism and colorism. Future studies should consider utilizing communitybased samples to better account for Black women across the country.
Conclusion and Clinical Implications
Our findings, though modest, highlight how prejudicial social forces (e.g., racism and colorism) relate to Black women’s feelings about their hair texture and beauty behaviors. Given recent findings that use of chemical relaxants can not only be damaging to one’s hair, but also linked to increased rates of breast, uterine, and ovarian cancers (Chang et al., 2022; Stiel et al., 2016), we echo Lowy and colleagues’ (2021, p. 323) call to encourage scientists to apply “culturally sensitive and intersectionalityinformed theory” to improve future body image research that is more inclusive and addresses the unique needs of women of color. Drawing on social identity theory, this initial investigation of these constructs underscore how group identification and societal expectations may influence personal behaviors. Black women may engage in hair straightening practices (e.g., chemical relaxants) as a way to conform to dominant beauty standards, which are often defined by Eurocentric ideals, Despite the limitations of this study, future research should examine additional hairaltering methods, rather than just chemical relaxants, in relation to perceived racism, colorism, and hair texture discrepancy and dissatisfaction. Further, considering the documented dangers of using products such as chemical relaxants, future research needs to specifically examine why women are using them and advocate for policies
Elliott, Martz, Bazzini, and Selani | Hair Texture Dissatisfaction
regulating their sale and usage.
We encourage future research on perceptions of hair texture dissatisfaction and hair presentation among Black women. The development of valid scales to measure hair texture dissatisfaction is critical for advancing literature on underrepresented groups. This study demonstrated that both the dissatisfaction scale and our selfdeveloped hair texture discrepancy scale, especially it’s ideal hair texture component, may have value in this future research. These scales are also pivotal in addressing the prevalence of potentially harmful beauty practices within the Black community. This study lays the groundwork for more rigorous empirical research on how cultural forces influence the behavior and body image concerns of Black women, with the goal of fostering greater satisfaction with their appearance.
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Author Note
Tori A. Elliott https://orcid.org/0009000858758224
Denise M. Martz https://orcid.org/0000000231977362
Doris G. Bazzini https://orcid.org/000000025286675X We have no known conflict of interest to disclose. This study was supported by the Office of Student Research (OSR) at Appalachian State University.
Positionality Statement: Tori identifies as a queer, cisgender African American woman. Denise and Doris identify as White, heterosexual, and cisgender. Shraddha identifies as a cisgender, heterosexual woman of color. All authors are nondisabled and acknowledge that their perspectives are influenced by their positions within all of these dimensions of identity.
Correspondence regarding this article should be addressed to Denise M. Martz, Department of Psychology, Appalachian State University, ASU Box 32109, Boone, NC, 28608, United States.
APPENDIX A
Hair Texture Discrepancy (HTD)
Discrepancy Score: Based on the hair texture chart below, please indicate your:
1. Natural hair texture without styling: ____
2. Ideal hair texture: ____
image of thin and straight hair image of thick and straight hair image of slightly wavy hair image of wavy hair image of heavier wavy hair image of curly hair image of very curly hair image of kinky curly hair
Note. The raw score was calculated by taking the rating for #1 minus #2. In order to avoid a possible score of a negative number, a constant value of +8 was added to all scores to allowing for statistical analysis to be run. To view the eight hair images used for the current study, see Loussouarn et al. (2007).
APPENDIX B
Frequency of Relaxing History Scale (FRHS)
A relaxer is a lotion or cream-based treatment used to chemically straighten curly and textured hair. Use the Likert scale to answer the question below: 0
In the past year, how often have you used any of the following permanent hair straightening products?
- Dark & Lovely Triple Nourished Silkening Relaxer
(b) Compared to most other Black women, I believe my hair texture is _____ (c) If I could change my hair texture, I would make it straighter or curlier _____
Note. The revision made to the original SCSS was changing “skin color” to “hair texture” for all items. For item (e) “lighter” was changed to “less curly” and for item (f), “lighter” was changed to “straighter.” For items (g) and (b) “African American” was changed to “Black women”. Hair texture dissatisfaction scores will be calculated by reverse coding items (e) and (f) as higher scores on these items will indicate hair texture dissatisfaction.
When Feeling Positive Brings Out the Negative: The Impact of Nurturant Love on Perceptions of Ambiguous Situations
Valeria Panameno, Taylor S. Perea, Daniel M. Vasilyev, Samantha Mae C. Flores, Kelly T. Cazinha, and Makenzie J. O’Neil*
Department of Psychology, Saint Mary’s
College of California
ABSTRACT. Positive emotions involve an array of complex cognitive, behavioral, and motivational processes. The present research examines the impact of 2 positive emotions, contentment and nurturant love, on the perception of an ambiguous situation. This study collected data from 2 distinct samples: 1 of undergraduate students (n = 106), and another using the CloudResearch Connect online platform (n = 292). In both samples, participants were primed via images into 1 of 3 emotion conditions (i.e., nurturant love, contentment, or a neutral control) and asked to write a story about an ambiguous photo. Results found that, across both samples, participants in the nurturant love condition wrote more negatively themed stories than those in contentment or neutral conditions (Sample 1: p = .003; Sample 2: p < .001). Contrastingly, participants in the contentment condition wrote more positively themed stories than those in the nurturant love or neutral condition. These findings highlight the importance of differentiating positive emotions from one another, as each serves distinct evolutionary purposes and produces unique perspectives.
Early research has suggested that positive affect can broadly be understood as “being happy” and lead to more pleasant perspectives on the world (Clore et al., 1994; Forgas, 1995; Shiota, Yee et al., 2017). However, positive emotions are far more nuanced than this traditional approach would suggest, and recent research has begun to emphasize the emotional complexity of the human experience in regards to both positive and negative affect (Quoidbach et al., 2014). For instance, holding one’s newborn baby for the first time drastically differs from a child’s excitement on Christmas morning. Although these are both positive events, they elicit different emotional experiences, and thus demonstrate the variability of positive emotions experienced in everyday life. The types of positive situations that a person experiences vary widely, and by extension the affective and behavioral responses these situations induce should be expected to vary as well (Revord et al., 2021; Zadra & Clore, 2011). Even a slight variation in an eliciting situation may alter the resulting response. For instance, imagine a parent
who is watching their toddler play happily at the park. Seeing their child laugh and enjoy themselves fills the parent with joy and love, until the child starts hanging upside down on the monkey bars, and fear kicks in as the parent worries their child will fall. What initially was a passive positive experience turns into one with hyperfocused concern for the child’s welfare. This shift in their emotional state would then be expected to have an effect on the parent’s cognition, motivation, attention, and behavior (Ekman, 1992; Levenson, 1999; Shiota, Yee et al., 2017). Prior research has demonstrated the complexity of these responses resulting from positive emotion; however, there remains much that has not been investigated. Thus, the present research aims to add to this literature by examining how particular positive emotions produce differing responses towards certain situations. Specifically, across two studies, we examined how two positive emotions (i.e., nurturant love and contentment) impact people’s interpretations of an ambiguous stimulus.
Literature Review
Previous research has demonstrated a “rosecolored glasses” effect of positive emotional states generally, in which positive emotions are thought to elicit more pleasant perspectives on stimuli or situations (Shiota, Yee et al., 2017). Similarly, the wellestablished Broaden and Build Theory suggests that positive emotions (e.g., joy, love, contentment) broaden an individual’s “thoughtaction repertoires” and are beneficial for creating longterm resources, particularly through expanding an individual’s cognitive processes and scope of attention (Fredrickson 1998, 2001; Fredrickson & Branigan, 2005; Stifter et al., 2020). Although these traditional theories provide important context of the impact of positive emotions generally, an explanation of variability in positive emotional responding had been missing. More recent research, however, has begun to address this gap by highlighting the diverse, rich, and intricate range of both positive and negative emotions that human beings experience, known as emodiversity (Barrett, 2009, 2013; Barrett & BlissMoreau, 2009; Quoidbach et al., 2014). Experiencing a wide range of emotions, regardless of whether they are positive or negative, seems to have positive impacts on both physical and mental wellbeing (Quoidbach et al., 2014; Wang et al., 2020). This research demonstrates that, just like negative emotions, positive emotions are complex parts of the human experience and merit further investigation into how they function and differ from one another.
One way that researchers have tried to understand these nuances of emotion is through a functional lens. This perspective involves evaluating the specific affordances of particular emotions; that is, how an emotion uniquely helps people avoid threats or pursue opportunities in their environment (Beall & Tracy, 2017; James, 1884). The functional approach suggests that emotions work as systems that combine both survival instincts and adaptive decision making to best assess responses to the situation (Revord et al., 2021). Many negative emotions have been strongly linked with promoting particular behaviors and cognitions which help to avoid threats in one’s environment (e.g., disgust promoting avoiding potential contamination; fear motivating avoidance of a dangerous stimulus; Rozin & Fallon, 1987; Rozin & Royzman, 2001; Schaller & Park, 2011). Meanwhile, emotion research historically generalized positive emotion more broadly as simply being “happy” or “joyful” without as much consideration for the particular affordances being promoted (Cordaro et al., 2016; Ekman, 1972; Izard, 1971). More recent research, however, has begun to address this gap by identifying particular affordances that specific positive emotions serve (Beall & Tracy, 2017; Shiota, Campos et al., 2017; Shiota et al., 2014).
For instance, contentment produces feelings of satisfaction and emotional fullness as a result of having needs met at a desired or excessive level (Griskevicius, Shiota, & Nowlis, 2010; Shiota et al., 2006; Shiota et al., 2014; Tong, 2015). A primary function of contentment is that it results in individuals fully savoring their present situations, and, importantly, in identifying strategies for maintaining or creating their contented state in the future (Izard, 1977). Contentment is correlated with overall life satisfaction (Berenbaum et al., 2013; Cordaro et al., 2024), positive selfevaluation (Cordaro et al., 2021), and increased appreciation for the present without worry about past events or future consequences ( Campos et al., 2013; KabatZinn, 2003; Mathews, 1990; NolenHoeksema, 2000). Contentment also reduces activity in the sympathetic nervous system and produces physiological responses that resemble relaxation (e.g., slower heart rate; Cordaro et al., 2021; Kreibig, 2010). In line with traditional theories of emotion, contentment tends to increase reliance on heuristics; reduce motivation for careful, systematic processing; and decrease sensitivity toward threats or dangerous environmental triggers (Bless et al., 1996; Campos et al., 2013; Cordaro et al., 2021; Griskevicius, Shiota, & Neufeld, 2010; Schwarz & Bless, 1991; Shiota et al., 2014). This research closely aligns with what the ‘rosecolored glasses” effect and Broaden and Build theory would suggest (Fredrickson 1998; 2001; Fredrickson & Branigan, 2005; Shiota, Yee et al., 2017); however, not all positive emotions would be expected to show these same outcomes.
Nurturant love, for instance, is a positive emotional state that would be expected to have differing outcomes when compared to something like contentment. Nurturant love, or tenderness as it is also called, is elicited by Kindchenschema targets–the cute physical traits that indicate youthfulness or helplessness, including chubby cheeks, large eyes, and small nose and mouth ( Hrdy, 1999 ; Lorenz, 197 1; Nittono & Ihara, 2017; Trivers, 1974). This emotion serves the motivational function of promoting caregiving and protection to these Kindchenschema targets–most adaptively importantly, one’s own offspring (Cho et al., 2022; Kenrick et al., 2010; O’Neil et al., 2018, for review). Nurturant love enhances positive support and warmth, leading to more trusting interdependent relationships; encourages soothing touches and melodic tone of voice toward the Kindchenschema targets; and is associated with oxytocin release suggesting an increase in social bonding and connectedness (Champagne et al., 2003; De Dreu et al., 2010; De Dreu et al., 2012; Goodall, 1986; O’Neil et al., 2018; Shiota et al., 2014).
However, nurturant love also elicits protective responses that increase vigilance and awareness of
PSI CHI JOURNAL OF PSYCHOLOGICAL RESEARCH
Nurturant Love and Ambiguous Situations | Panameno, Perea, Vasilyev, Flores, Cazinha, and O’Neil
potential threats (Shiota et al., 2014). For instance, although positive emotions are typically associated with an over reliance on heuristics, nurturant love looks more similar to an emotion like fear by increasing systematic processing, careful behavior, and more narrowed attentional focus (Bless et al., 1996; Griveskius, Shiota, & Neufeld, 2010; Nittono et al., 2012; Schwarz & Bless, 1991; Shiota et al., 2014; Yoshikawa et al., 2020). These responses may assist in one’s ability to provide needed support, and to identify potential threats or dangers within an environment (Cheon & Esposito, 2020). Additionally, nurturant love is associated with a reduction in the parasympathetic nervous system, suggesting an increase in readiness and attention without necessarily activating the “fight or flight” response (Sherman et al., 2009; Shiota et al., 2011; Tkaczyszyn et al., 2013). Moreover, although oxytocin release is most commonly associated with promoting positive social bonding, research also suggests that oxytocin tends to raise anxiety about unknown threats, and can increase aggression toward outgroup members when vulnerable ingroup members are threatened (Bradley, 2009; De Dreu et al., 2010, 2012; Evans et al., 2019; Grillon et al., 2013). Thus, although nurturant love is a positive emotion that may feel and seem decidedly pleasant on the surface, it also activates distinct responses due to the affordances it promotes.
Present Study
The present research aims to build on prior work regarding how positive emotional states differ in responses to perceptions of particular situations (Griskevicius, Shiota, & Neufeld, 2010; Shiota et al., 2011; Shiota et al., 2014). There is ample research distinguishing negative emotional states (Rozin & Fallon, 1987; Rozin & Royzman, 2001; Schaller & Park, 2011), but the goal of the present research was to specifically investigate how different positive emotions impact situational perceptions. Additionally, although prior research suggests that elements of nurturant love enhance protectiveness or even aggressive tendencies, and increases vigilance to one’s environment (De Dreu et al., 2010; Hrdy, 1999; Shiota et al., 2014), there remains much that is unknown about how nurturant love impacts people’s cognitive responses. Thus, the present research also aims to further investigate how nurturant love specifically impacts participants’ perceptions of an ambiguous stimulus. We expected that nurturant love would lead to more negative perceptions relative to a different positive emotional state. Specifically, contentment is a positive emotion that prior research would indicate should lead to the established “rosecolored glasses” effect (Campos et al., 2013; Griskevicius, Shiota, & Nowlis, 2010; KabatZinn,
2003; Mathews, 1990; NolenHoeksema, 2000; Shiota, Yee et al., 2017).
To address these aims, across two studies, the present research examined people’s perceptions of a particular ambiguous situation when they were feeling nurturant love, as compared to being in a neutral state or a comparison positive emotion (i.e., contentment). We hypothesized that participants primed to feel nurturant love would perceive the ambiguous situation more negatively than those in the other two conditions, and those feeling contentment would perceive the situation more positively.
Method
The present research consisted of conducting the same study twice with two separate samples. Sample 1 involved undergraduate students during COVID19 restrictions; thereby utilizing a more tightly controlled undergraduate sample. Sample 2 involved a broader sample from the general population, taking place after COVID19 restrictions had been lifted. This study allowed for the hypothesis to be tested with a larger, more generalizable sample, as well as investigated if the results replicated postCOVID19 restrictions. The samples are described below in detail, but the method remained the same for both samples and is thus described only once below. Both studies were approved prior to data collection by the Institutional Review Board at Saint Mary’s College of California.
Participants: Sample 1
Participants consisted of 106 undergraduate students enrolled in an Introductory Psychology course at a small liberal arts college in California, U.S., who received partial course credit for their participation1. See Table 1 for demographic information on the sample. Participants were randomly placed into three emotion conditions, detailed below: nurturant love (n = 36), contentment (n = 35) and a neutral control (n = 35).
Participants: Sample 2
Participants consisted of 292 United States residents recruited from the CloudResearch Connect platform database, who received $1.50 in compensation. See Table 1 for demographic information on the sample. Participants were randomly placed into three emotion conditions, detailed below: nurturant love (n = 99), contentment (n = 99), and a neutral control (n = 94).
1Part of the requirements of this Introductory Psychology course is that students may either participate in research outside of class or write an additional class paper. Students who choose to participate in research are given many studies to choose from to decide which they would like to participate in.
FALL 2025
Panameno, Perea, Vasilyev, Flores, Cazinha, and O’Neil | Nurturant
Materials
Emotion Manipulation
Using the Qualtrics platform, participants were randomly assigned to one of three emotion conditions: nurturant love, contentment, or a neutral control. In order to prime the particular emotion, participants were shown 10 images each for five seconds at a time. In line with prior research validating this manipulation technique (Lench et al., 2011; Uhrig et al., 2016), the content of the images was chosen specifically because of the relevance to the elicitation of the respective emotion condition. For nurturant love, images included baby animals displaying Kindchenschema characteristics (e.g. big eyes, small nose and mouth; Kalawski, 2010; Sherman et al., 2009; Shiota et al., 2011). For contentment, images included warm, cozy, environments or objects, such as fireplaces and warm blankets (Fredrickson & Levenson, 1998; Griskevicius, Shiota, & Neufeld et al., 2010; Stephens et al., 2010). For neutral control, images included random every day environments, such as a classroom or bedroom. At the end of the study, participants then completed a manipulation check to assure they were in the intended emotional state for their respective condition. This involved having participants indicate during the “earlier memory task,” how positively or negatively (1 = very negative; 8 = very positive) they felt, and how strongly (0 = no emotion at all; 8 = strongest ever felt) they felt particular emotions (e.g. contentment, affection, tenderness).
Ambiguous Situation
To evaluate perceptions of an ambiguous situation, the Qualtrics survey showed participants an image 2 (see Figure 1) selected from the Picture Story Exercise (PSE; Schönbrodt et al., 2020). The researchers selected this specific image because it shows human subjects, but does not suggest any direct emotional cues by the subjects in the photo. Participants were given five minutes to write a vignette about what was happening in this photo, and were not permitted to move on to the next page of the survey until after the five minutes had passed.
Design and Procedures
At the beginning of the study, using Qualtrics’ randomization, participants were randomly assigned to one of the three emotion conditions: nurturant love, contentment, or neutral. They viewed the images for their condition, and were told that the purpose was for a memory task, where they would be asked to recall the images later in the study. Although participants were asked to recall the images later, this instruction was included as a form
of deception and not of primary interest to this study. Participants then viewed the ambiguous image from the PSE, and were instructed to spend five minutes writing “a story about what [they] think is taking place in the image shown.” Once finished with their vignette, participants completed the manipulation check.
2The image was taken from the Open Science Framework database of photos found here: https://osf.io/pqckn/wiki/home/. The image used is “newpic14”.
FIGURE 1
PSE Ambiguous Image Shown to Participants
Note. Figure 1 displays an image depicting two individuals sitting on a bench facing a river with a bridge in the background. This image (newpic14 by Oliver Schultheis) was selected from the Picture Story Exercise (PSE; Schönbrodt et al., 2020; https://osf.io/pqckn/wiki/home/), a tool used to measure and analyze individuals’ implicit motives by having them create a narrative based on ambiguous images.
Nurturant Love and Ambiguous Situations | Panameno, Perea, Vasilyev, Flores, Cazinha, and O’Neil
Vignette Codebook Creation
To evaluate the particular themes and overall valence of the vignettes that participants wrote, we conducted a content analysis of the vignettes after data was collected. This analysis involved creating a codebook that specified particular themes we expected to observe, based on prior research and theory (Shiota et al., 2011; Shiota, et al., 2014; Shiota, Yee et al., 2017; Tong, 2015). The vignettes for Sample 1 were coded by a research team of three undergraduate lab assistants, blind to the hypotheses and the specific emotion conditions. At the time of analyses for Sample 2, one member of the original coding team was no longer involved in the lab, but two new lab assistants were trained on the codebook; thus, there were four total coders for Sample 2, also blind to hypotheses and emotion conditions. For both studies, the coding team all completed reliability vignettes (estimated 20% of each sample), as well as coded vignettes individually. Coders held weekly meetings to discuss any discrepancy in reliability, as well as any individual vignettes that weren’t easily categorized. In these instances of disagreement, they were easily addressed through a short discussionwhere consensus was then quickly reached.
The primary code of interest was to assess the overall affective valence (positive, negative, or neutral) of the entire vignette. This code was included to assess the aims of evaluating how two positive emotions lead to differing evaluations of an ambiguous stimulus, and specifically if nurturant love, though positively valenced itself, may lead to negatively valenced vignettes. Vignettes coded as “positive” emphasized themes like connection, bliss, and romantic love throughout the story. “Negatively” valenced vignettes involved themes such as grief, sadness, anger, or uncertainty. “Neutral” vignettes included little to no expression of emotional sentiments. We evaluated overall valence using this categorical approach (positive, neutral, negative) in order to investigate a clear valenced difference in responses between two positive, but functionally distinct, emotions. Interrater reliability of the affective valence of the vignettes was at levels consistent with acceptable to high reliability (for Sample 1, Fleiss’ Kappa = .71 and for Sample 2, Fleiss’ Kappa = .82; McHugh, 2012).
In addition to this primary code, the codebook also detailed several other codes that were added for exploratory purposes, including: if the vignette described a close relationship between the two individuals; the type of relationship described (e.g., romantic, familial); the particular type of situation that was described; and the specific emotions that were presented. As coding continued, some of these exploratory codes lost relevance; for example, the vast majority of all the vignettes described
a close relationship, making this code too prevalent to detect differences across conditions. Nonetheless, the research team continued to code for these themes in order to maintain consistency across the entire datasets, and reliability remained high across these codes. However, we have only included the content analyses for those codes that showed meaningful patterns.
Results
Preliminary Analyses
Manipulation Check
A check of the emotion condition manipulation revealed that the manipulation did work as planned. Participants in both the nurturant love (MSample 1 = 6.56, MSample 2 = 6.42) and contentment (MSample 1= 6.19, MSample 2 = 6.01) conditions felt more positively than those in the neutral condition (MSample 1 =5.06, MSample 2 = 5.30), FSample 1(2, 104) = 7.64, p < .001, FSample 2(2, 289) = 10.59, p < .001. Moreover, the manipulation of the specific emotions was also successful. Participants in the nurturant love condition ( M Sample 1= 6.36; M Sample 2 = 6.10) felt more nurturant love/tenderness than those in the neutral condition (MSample 1 = 3.23; MSample 2 = 3.87), FSample 1(2, 104) = 12.796, p < .001, FSample 2(2, 287) = 18.13, p < .001. Participants in the contentment condition (MSample 1 = 4.86, MSample 2 = 5.92) felt more contentment than those in the neutral condition ( M Sample 1 = 3.69, M Sample 2 = 4.34), F Sample 1 (2, 104) = 4.995, p = .008, FSample 2(2, 288) = 13.04, p < .001.
Vignette Length
The content of participants’ responses suggested they took the task seriously, and tended to write vignettes of typically around 2–3 sentences, (MVignette Word Count Sample 1 = 119.83, SDSample 1 = 59.55; MVignette Word Count Sample 2 = 96.61, SDSample 2 = 68.90). Sample 1 showed no notable differences between the conditions (M nurturant love = 119.00, M contentment = 121.97, M neutral = 114.50) on length of vignettes written, F(2, 105) = 0.14, p = .87. Sample 2 showed a marginal difference between conditions such that relative to those in the neutral condition (Mneutral = 108.68), those in the nurturant love (Mnurturant love = 85.13) and contentment ( M contentment = 96.61) wrote shorter vignettes, F(2, 289) = 2.85, p = .06.
Analysis for Sample 1
We hypothesized that those in the nurturant love condition would write more negatively themed vignettes than those in the contentment and neutral conditions. In line with this hypothesis, a chisquare test indicated that emotion condition did significantly impact the affective valence of the participants’ vignettes, χ2(4, N = 106) = 15.99, p = .003, with a phi coefficient of Φ = .39. Participants primed to feel nurturant love
Panameno, Perea, Vasilyev, Flores, Cazinha, and O’Neil | Nurturant Love and Ambiguous
Situations
wrote 66.67% negative stories, 22.22% neutral stories, and 11.11% positive stories. Participants primed to feel contentment wrote 31.43% negative stories, 22.86% neutral stories, and 45.71% positive stories. Those in the neutral control condition wrote 31.43% negative stories, 37.14% neutral stories, and 31.43% positive stories. See Figure 2.
Analysis for Sample 2
Similarly, a chisquare test of sample 2 demonstrates the same significant finding of emotion condition impacting the affective valence of the vignettes, χ2(4, N = 292) = 18.73, p < .001, with a phi coefficient of Φ = .25. Participants primed to feel nurturant love wrote 44.44% negative stories, 19.19% neutral stories, and 36.36% positive stories. Participants primed to feel contentment wrote 27.27% negative stories, 21.21% neutral stories, and 51.52% positive stories. Those in the neutral control condition wrote 31.91% negative stories, 38.30% neutral stories, and 29.79% positive stories. See Figure 2. Across both samples, this finding indicates that when participants are in a nurturant love state, they were significantly more likely to write a negatively themed story, and that those feeling contentment wrote more positive stories.
Content Analysis
These analyses demonstrate that participants’ emotional state impacts the narratives of the vignettes that they wrote. Specifically, participants in the contentment condition wrote stories that emphasized a sense of calmness, safety, happiness, enjoyment, and love. For instance, one participant wrote about a marriage proposal and detailed the positive qualities of the woman that led the man to fall in love:
...his feelings for her have grown over these last three years, and he smiles thinking about how much she means to him… she [looks at him and sees] the same smile that has lit up her life...this river flows like their love. Strong, steady, calm, but full of life.
Another vignette describes how the characters appreciate the present moment and company: “Neither of them are on their phones, and the man is listening to what the woman is saying.” Although the specific details of the vignettes differed across participants, these general themes of positivity emerged more often for those feeling contentment.
Contrastingly, predominantly negative themes emerged within the nurturant love condition’s vignettes, including romantic breakups, gloomy ambiances, or the loss of someone special. For example, a participant explained
that the man in the photo “... no longer wants to marry her because he found another woman who makes him happier because she is very demanding and always angry.” Another indicated that the photo reflected an ongoing unhappy relationship: “This is not the first time he’s been accused of infidelity and she’s tired of it.” Additionally, some of the vignettes detail the experience of intense negative emotions, with one participant in particular capturing this intensity by writing that after a breakup the woman in the image wished: “She could just dive in the water so she wouldn’t have to be sitting there while her heart breaks.” Although heartbreak was one common theme among the nurturant love condition’s stories, other vignettes centered around death or trauma. One vignette describes a murdermystery story where the two characters question one another to uncover who the murder culprit may be. The man shared that there were eyewitnesses who saw the woman leave the
Note. Figure 3 demonstrates valence (positive, neutral, negative) of vignettes based on emotion condition. In both samples, nurturant love led to more negative vignettes, contentment led to more positive vignettes, and neutral led to more neutral vignettes.
FIGURE 2
Affective Valence of Vignettes Based on Emotion Condition
Sample 1:
Sample 2:
crime scene. As she seemed to be cornered by the man, the woman “pulled a knife from behind her shirt and tried to stab him in the neck.” Another participant described that following a “traumatic situation,” the woman was “so broken and nearly dead to the world that he [her husband] cannot reach her.” The described traumatic event was overwhelming to the point where the character dissociated from the present situation.
Despite these negative themes, many of the vignettes in the nurturant love condition also allude to caring for one another through these distressing situations (a notable exception being the previously described murder mystery plot). One participant wrote about a husband who displayed warmth and support during a hardship: “This was the third pregnancy of hers that had ended prematurely. Pierre held her hand and looked at her and told her that he loved her… Maria knew that Pierre cared for her and that they would live a good life [together].” Another example follows siblings who reconnect with one another and reminisce about their past with their dying relative: “The brother is consoling the sister on her worries, just as she had done…before…They are… talking about moments… to help navigate through the pain. They are bonding once again…” Although these vignettes center around difficult experiences, there seems to also be a theme of care and protection that the characters display towards each other. Once again, these specific situations that participants wrote about varied, but those in the nurturant love condition tended toward writing these more negatively themed stories, often with a caregiving or protective undertone.
Discussion
This research sought to examine how the positive emotional state of nurturant love, as compared to contentment, impacts people’s perceptions of an ambiguous situation. As hypothesized, the data demonstrated that generally, participants experiencing nurturant love wrote more negatively valenced stories, but participants feeling contentment wrote more positively valenced stories. These findings support research demonstrating the unique responses that different positive emotions can elicit (Shiota et al., 2014). Participants feeling contentment wrote stories in line with the conventional expectation that positive emotion should elicit pleasant perspectives on situations (Shiota, Yee et al., 2017). Contentment is thought to promote calming physiological effects, produce feelings of warmth and comfort, and lead people to savor their present circumstances. (Cordaro et al., 2021; Griskevicius, Shiota, & Nowlis, 2010; Kreibig, 2010; Shiota et al., 2006; Shiota et al., 2014). The content of the contentment condition vignettes demonstrated thematic examples that support these patterns, with descriptions of stories that often
included feelings of relaxation, bliss, and romantic love. Conversely, the function of nurturant love is to provide care and protection to the Kindchenschema target–prototypically, one’s offspring (O’Neil et al., 2018). Having heightened vigilance to threats, as well as more wary, systematic information processing would likely be necessary to ensure the safety of the target (Griskevicius, Shiota, & Neufeld, 2010; Nittono et al., 2012; Shiota et al., 2014). The finding that participants feeling nurturant love wrote more negative stories based on the ambiguous photo demonstrates support for this type of wariness and vigilance. Participants did not see the world through “rosecolored glasses,” as one might expect of a positive emotional state. Instead, their perception of the ambiguous situation seems guided by potential threats or ways in which the scenario could go wrong. Many participants in this nurturant love condition wrote about breakups, relationship problems, or the loss of loved ones. Often these negative vignettes were paired with one of the subjects in the photo trying to care or support the other in distress or hardship. These findings demonstrate that nurturant love, a positive emotion, may promote careful consideration of possible negative outcomes in order to provide care and protection for vulnerable others.
Limitations
Although this study did find support for the hypothesis across both samples, it should be noted that the data for both samples was collected entirely online. The first sample of undergraduate participants was collected online out of necessity due to COVID19 safety procedures; and the second sample was collected online in order to reach a larger and more diverse sample pool. Although Sample 2 provides the benefit of more generalizability of these findings, it should also be noted that the statistical analyses in Study 2 yielded results with more variability than Study 1. The reason for these differences was not directly tested in the current research, but could be due in part to differences in the sampling pool selected for each sample, and to how closely participants were actively focused and fully engaged in the research. Participants of the undergraduate sample who received partial course credit (i.e. Sample 1), may have been more motivated to maintain high engagement with the tasks than in Sample 2, where participants were in their homes/workplaces and may have had other distractions preventing their full undivided attention. That participants in Sample 1 wrote on average longer vignettes than those in Sample 2 would suggest support for this speculated dip in engagement. However, regardless of the underlying explanation, even with this greater variability, the emotion manipulation check and assessment of the writing quality of vignettes, suggest
FALL 2025
Panameno, Perea, Vasilyev, Flores, Cazinha, and O’Neil | Nurturant Love and Ambiguous Situations
that participants in Sample 2 generally took the study seriously. Furthermore, this sample still produced significant results in the hypothesized direction, replicating the findings from Sample 1.
The qualitative nature of these analyses also presents an additional limitation. Qualitative research lends itself to being more subjective and prone to researcher bias, and less able to draw conclusions of statistical significance. In this research, we tried to minimize these potential issues by assuring the coders were blind to participant condition, and by maintaining high interrater reliability of the overall valence code across both studies. However, this research did not have enough power to conduct further statistical analyses on other qualitative observations. The exploratory content analysis revealed recurring themes and patterns among the vignettes (e.g. attempts to provide support to someone in distress in the nurturant love condition), but due to this lack of power, we could not draw any further statistical conclusions. Moving forward, research should more closely evaluate the themes identified here, perhaps with a larger sample, to identify if statistically significant findings emerge.
Future Directions
Although this study adds to the current body of research examining the differing processes of nurturant love and contentment, there are still questions remaining that future research should consider. The present research demonstrated that nurturant love increases negative perceptions of ambiguous situations, but did not investigate the underlying driving mechanism. Prior research shows that nurturant love promotes a clear protective function (Cho et al., 2022; Griskevicius, Shiota, & Neufeld, 2010; O’Neil et al., 2018; Shiota et al., 2014), and combined with the present findings, may suggest that nurturant love increases perception of threats. Future research should investigate this finding more closely by directly examining the connection between nurturant love and people’s perceptions of and responses to threatening stimuli.
The present research compared two positive emotions–contentment and nurturant love–in order to add to the understanding of the diverse positive states that people experience. These emotions were chosen in order to compare a positive emotion that has many features associated with a traditional view of generalized positive affect (i.e., contentment), with one (i.e., nurturant love) that has unique functions that may lead to differing outcomes than general positive affect would predict (Bless et al., 1996; De Dreu et al., 2010; Griskevicius, Shiota, & Nowlis et al., 2010; Griveskius, Shiota, & Neufeld, 2010; Hrdy, 1999; Shiota et al., 2014; Tong, 2015). Additionally, we chose to evaluate the overall valence (positive, negative, or neutral) of the vignettes
participants wrote, so as to clearly demonstrate that a positive emotion is not exclusively associated with positive outcomes and in fact, as the present research demonstrates, may lead to negative cognitive perceptions. However, as research on emodiversity has demonstrated, there are many additional subtleties to the emotion system that warrants further investigation (Quoidbach et al., 2014, Wang et al., 2020). For instance, future research could examine the responses to an ambiguous situation using a more dimensional or finegrained scale of valence (e.g. a 7 or 9point Likert scale of Positive to Negative) in order to have a more nuanced evaluation. Future research could also continue to compare differing positive emotional states with an alternative emotion manipulation strategy for comparison, and perhaps also examine how varying negative emotions impact perceptions of ambiguous stimuli. The present research demonstrates promising findings on the nuance of different emotional states as it relates to perception, and opens the door to many potential future studies.
Conclusion
Our findings indicate that all positive emotions do not necessarily lead to positive perceptions; as we saw in this case, nurturant love actually leads to negative perceptions. These findings suggest that, like negative emotions, positive emotions are functionally distinct from one another and merit further investigation to understand their specific cognitive processes. By investigating these nuances of positive emotions we gain a better understanding of how people perceive, react to, and take in information, allowing us to more deeply understand human motivation and behaviors.
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Author Note
Makenzie J. O’Neil https://orcid.org/0009 0008 6571 836X
Valeria Panameno and Taylor S. Perea contributed equally to this work.
Correspondence concerning this article should be addressed to Makenzie O’Neil, Psychology Department, St. Mary’s College of California, 1928 Saint Mary’s Road, Moraga, CA 94575, United States. Contact: mjo6@stmarysca.edu
COVID-19
and Current Mental Health of College Students
Morgan R. Bodker and Megan M. M. St. Peters* Department of Psychology, Murray State University
ABSTRACT.
This study examined whether current mental health differs between college students who have and have not had a prior COVID19 diagnosis. An online survey was completed by 106 college students (age M = 19.32; SD = 2.73). Current depressive symptoms were measured using the Patient Health Questionnaire (PHQ9; Kroenke & Spitzer, 2002). Participants who had a COVID19 diagnosis reported significantly higher levels of depression than participants who have never had COVID19, t(104) = 1.67, p = .049, d = 0.34. Participants who reported having a prior COVID19 diagnosis were asked to rate the severity of their COVID19 symptoms. A linear regression revealed severity of COVID19 symptoms at time of diagnosis is predictive of current depression levels, F(1, 64) = 4.22, p = .044, R2 = .06. Potential explanations and ramifications for why college students who had COVID19 previously may be at a higher longterm risk for depression are explored.
Keywords: COVID19, depression, symptoms, college students
The COVID 19 pandemic triggered a global health crisis, resulting in unprecedented psychological pressure (Kupcova et al., 2023; Li et al., 2021). Social separation and isolation during the pandemic were pervasive contributing factors to mental health decline (Kupcova et al., 2023; Murayama et al., 2021). However, numerous stressors such as loneliness; fear of infection, suffering, and death; bereavement; and financial worries contributed to the universal decline in mental health (e.g., Li et al., 2021).
College students face additional stressors such as academic pressure and worry about employment prospects (Wu et al., 2023), and the compounding impact of these stressors on their mental health was particularly staggering. A metaanalysis on the association between the COVID19 pandemic and mental health of college students found that depression and anxiety were the most reported psychological problems (Li et al., 2021). Studies showed increased psychological stress and anxiety observed across the college student population worldwide, with anxiety ratings reaching nearly 78% during the pandemic (Wu et al., 2023). Among 2,031 American college students, 48% suffered from moderatetosevere depression, 38% showed moderateto severe anxiety, and 18% had suicidal thoughts
(Wang et al., 2020). Wang and colleagues found that these mental health issues corresponded to changes in sleeping habits, eating patterns, and depressive thoughts. Unfortunately, young individuals suffering from mental disorders have high morbidity and mortality risks that translate into a 10–20 year reduction in life expectancy, with depression having one of the highest suicide risks (Chesney et al., 2014).
Although the COVID 19 pandemic impacted nearly everyone, those who reported a positive COVID19 diagnosis face additional hardships. COVID 19 symptoms vary but include tiredness, muscle aches, headaches, cough, congestion, sore throat and fever (Eloy et al., 2021). One study reported that patients diagnosed with COVID19 were nearly three times more likely to experience depression than the general population during the pandemic (Ettman et al., 2020). And, a metaanalysis of 51 studies examining 18,971 patients diagnosed with COVID19 reported prevalence of depression at 12.9%, which was higher than the general population at that time (Shetty et al., 2023).
Research has continued to illustrate that the pandemic’s effects are larger and longer lasting than initially envisioned (e.g., Tilstra et al., 2024; Wu et al., 2023). Given the unique characteristics of college
students that set them apart from the general public or even other young people, they are typically identified as highrisk groups for psychological problems (Wu et al., 2023). Identifying highrisk populations in young people is essential to selective screening that can be used to implement preventative psychiatry aimed at improving anxiety and depressive symptoms (FusarPoli et al., 2021). Yet, whether there are unique mental health challenges for college students who had a COVID19 diagnosis remains understudied. The current study measured depression in college students and explored whether it differed between those who have and have not had a COVID19 diagnosis. It was hypothesized that students with a previous COVID19 diagnosis would report more depressive symptoms currently, and that the severity of COVID19 symptoms would be predictive of current depressive symptoms.
Methods
Participants
A total of 106 college students participated in the study. An a priori G*Power analysis conducted using significance criterion of α = .05, power = .80, revealed a minimum sample size needed to be N = 84, thus this sample was sufficient for the proposed study. Average age was 19.32 years old (SD = 2.73), with 60.4% being firstyear students, 16.0% sophomores, 11.3% juniors, and 12.3% seniors. Participants were predominantly women (76.4%; 23.6% men) and White (80.2%). Other racial identities in order of prevalence included Black/ African American (8.5%), biracial (5.7%), Hispanic (4.7%), and Asian American and Pacific Islander (0.9%).
Materials and Procedures
Institutional Review Board approval was received prior to data collection. The study was distributed using Sona, an online research platform at the university. The online survey was available to students from fall 2023 to March 2024. Students in psychology courses were given access to the online research platform to complete studies as a way to fulfill course requirements and/or earn extra credit in psychology courses. Posters soliciting participation were also displayed on campus. Posters had QR codes that directed students to the online survey.
Depression
The Patient Health Questionnaire9 (PHQ9; Kroenke & Spitzer, 2002) was used to assess depression in the last two weeks, consistent with previous COVID19 research on college students (e.g., Wang et al., 2020). It is comprised of nine statements using a 4point scale, ranging from 0 ( not at all ) to 3 ( nearly every day ), with statements such as “feeling down, depressed, or
Bodker and Peters | COVID-19 and Depression
hopeless” and “having little interest or pleasure in doing things.” It was internally reliable in the current study (Cronbach’s α = .88).
COVID-19 Symptom Severity
A single question asked if the person had ever received a COVID19 diagnosis (yes or no). If the participant answered yes, they were then directed to answer seven questions related to COVID19 symptom severity, developed by Eloy and colleagues (2021). The seven questions were on a 4point scale ranging from 0 (no symptoms) to 3 (severe symptoms). These seven symptoms included tiredness, muscle aches, headache, cough, congestion, sore throat, and fever. This scale demonstrated adequate internal reliability in the current study (Cronbach’s α = .83).
Analysis
The PHQ9 was summed to measure depression and could range from 0–27, with higher scores indicating more severe depression. COVID19 symptoms were summed and could range from 0– 28, with higher numbers indicating worse symptoms. Analyses were conducted using IBM SPSS Statistics (v. 26) with an alpha value of .05.
Results
PHQ9 scores for depression ranged from 0–27 in the current study (M = 8.76, SD = 5.87). Most respondents (72.6%) had scores on the PHQ9 indicating some level of depression (defined as a total PHQ9 score of 5 or higher). An independentsamples t test was conducted to examine whether depression differed among college students who had and had not reported having a COVID19 diagnosis at any point during the pandemic. Students with a previous COVID19 diagnosis reported significantly higher levels of depression, t(104) = 1.67, p = .049, d = 0.34 (see Figure 1).
Out of 106 participants, 66 (62%) reported testing positive for COVID19 at some point since the start of the pandemic. The next analysis was restricted to these students to explore whether the severity of COVID19 symptoms is predictive of current depression. The linear regression analysis was significant, F (1, 64) = 4.22 , p = .044, R2 = .06. The regression equation for predicting depression was y’ = 5.89 + .3x, as shown in Figure 2. The correlation between severity of COVID19 symptoms and depression is r = .25.
Discussion
The current study found that college students who had been diagnosed with COVID19 are significantly more depressed than students who have never had a COVID19 diagnosis. It further revealed that, in students who
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COVID-19 and Depression | Bodker and Peters
have had COVID19, symptom severity was predictive of current depression levels.
This study consisted of primarily White female college students with an average age of 19.32 at a single university in the Midwest. Limitations in the
Students With History of
a COVID-19
Diagnosis Report Higher Levels of Depression
Note. Average depression levels as measured using the PHQ-9 are illustrated, with error bars representing +/- 1 SD. Participants who reported having a COVID-19 diagnosis (n = 66) were found to report significantly higher levels of depression than those who had not reported a COVID-19 diagnosis (n = 40; p = .049).
Current Depression Severity Predicted by Past Severity of COVID-19 Symptoms
generalizability of these findings to other locations and groups should be considered and explored. Other studies examined the disproportionate health impact of COVID19, finding race, socioeconomic, and gender disparities (e.g., Patel et al., 2023). For instance, Tai and colleagues (2022) found that Black, Latinx, and American Indian persons are more likely to be hospitalized and die from COVID19 than White persons, even after adjusting for age and state. Thus, it is likely that the current study may be conservative in estimating influence of past COVID19 symptom severity in nonWhite college student populations. Literature on gender differences is less clear. Some studies report men had more prevalent COVID19 symptoms than women (Patel et al., 2023), whereas other studies find opposing results. For instance, Larsson and colleagues (2022) found that female gender, older age, and acute illness severity were at highest risk for negative selfrated health. In a smaller study focused on college students, WalshMessinger and colleagues (2021) found that of the 51% of college students who reported postCOVID syndrome (symptoms ≥ 28 days), 96% were women. Although those studies focused on physical symptoms, another study found that Slovak women under the age of 30 with higher levels of education was most vulnerable to anxiety during the pandemic (Kupcova et al., 2023). More research is needed to discern whether there are race and ethnicity or sex differences on COVID 19 symptom severity and current mental health.
Symptom Severity
Note. Of the 66 participants who reported having a COVID-19 diagnosis at some point, their symptom severity was found to be a significant predictor of their current mental health, as measured using the PHQ-9 (p = .044).
Another notable limitation of the current study is that it was selfreport and retrospective. Thus, it is imperative to clearly state this study is associational and does not seek to make causal claims concerning a COVID19 diagnosis and depression. Some participants may have had COVID19 unknowingly, or misremembered whether they had a diagnosis. Furthermore, those who reported a diagnosis were asked to reflect on the severity of symptoms that could have spanned three years prior. Depression can impact perception, as depressed persons are often less optimistic (Korn et al., 2014) and thus, this may have affected their recall of symptom severity. There may be a shared third factor that contributes to the relationship found in the current study. Indeed, many other factors likely contribute to the decline in student mental health that could be explored as potential predictors–for instance, vaccine status, frequency of testing, and mental health history. Other studies have measured screen time, positive and negative affect, life satisfaction, perceived stress, anxiety, BMI, physical activity, and sleep quality, among other factors, during the COVID19 pandemic (e.g., Wright et al., 2023) that may be useful to explore as potential predictors to current student mental health in our
FIGURE 1
FIGURE 2
postpandemic society. Replications with additional predictors would be insightful and add to our understanding as to the enduring effects of COVID19 on young people.
Results from this study magnify the need to continue measuring the impact COVID19 has on society. Protracted COVID19 symptoms remain an area highly investigated. For instance, one study found that 91% of COVID19 positive college students reported full recovery, yet 51% reported protracted symptoms, of whom 59% had persistent symptoms longer than 50 days (WalshMessinger et al., 2023). Impaired concentration was the most frequently reported symptom, followed by headache, rhinitis, exercise intolerance, dyspnea, sleep impairment, brain fog, appetite loss, fatigue, and chest pain. They reported higher severity of depression in the postCOVID syndrome group compared to recovered COVID19 college students, but only for the subscales of sleep and thinking. Although they found no difference in depression when comparing postCOVID syndrome to COVID19 negative participants, the small sample sizes and lack of information provided (e.g., means, effect sizes), only highlights the importance of more research needed in this area. Such a need is reiterated in other studies; one metaanalysis reported that the relatively large heterogeneity among studies illustrates that further research is “indispensable” (p. 4, Li et al., 2021).
The current study did not explore continuing physical symptoms of COVID19, but rather explored whether previous symptom severity could predict current mental health status. Results from the current study suggest college students who had COVID19 may be more vulnerable to depression. College students are already an atrisk population for psychological problems such as depression, which has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication (Liu et al., 2022). Thus, knowing whether the student had COVID19 and if so, the severity of their symptoms could help identify those with an increased risk for depression. Why they are particularly at an increased risk warrants further investigation to understand if this relationship is related to symptom severity or if there are interactions with this particular group. Indeed, research on the aging population found they were more resilient to the effects of the pandemic on anxiety and depression, although they did not differentiate those who had and had not received a COVID19 diagnosis (Webb & Chen, 2021). Some researchers suggest that college students lack the ability to self regulate and selfrescue (Li et al., 2021). A large scale national online survey during the pandemic (May–June 2020) found that psychological flexibility accounted for 50.5% and 49.5% of depression and anxiety levels, respectively
Bodker and Peters | COVID-19 and Depression
Author | Title
(McCracken et al., 2021). However, those previous studies failed to differentiate those who had received a COVID19 diagnosis at some point. Identifying those most vulnerable and examining factors that increase resiliency is an area that needs additional exploration. In conclusion, the current study suggests that if a college student was diagnosed with COVID19 at any point during the pandemic, they may be at a higher risk of depression, and that increased risk may be related to severity of COVID19 symptoms. Any findings that help identify atrisk populations provides insights into potential strategies to educate professionals, treat those most vulnerable, and fuel future research. Furthering this understanding is necessary for strengthening preparedness, response, and resilience to not only the enduring effects of COVID19, but to similar emergencies in the future.
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Author Note
Morgan R. Bodker https://orcid.org/0009000021656330
Megan M. M. St. Peters https://orcid.org/0000000317551565
Correspondence concerning this article should be addressed to Megan St. Peters, Department of Psychology, Murray State University, 203 Wells Hall, Murray, KY 42071, United States.
Email: mstpeters@murraystate.edu
“Our
How
Friends
and How We
Make Them”:
Disordered Eating Tendencies Could Contribute to Friendships Based in Either Competition or Mutual Success
Abigale G. Hartwig Department of Psychology, University of Wisconsin–La Crosse1
ABSTRACT. Previous research has found that higher levels of disordered eating tendencies were correlated with friendship struggles. The purpose of this study was to investigate the nature of friendships in college students with disordered eating tendencies. Participants responded to an online survey measuring their body dissatisfaction and disordered eating tendencies (n = 135). Approximately half of the participants with the highest scores were invited to a followup study where they were asked to bring a close friend (n = 17 pairs). Then, these individuals completed questionnaires that measured disordered eating tendencies, friendship quality, competitiveness, and codependency. The researcher hypothesized that there would be a relationship between an individual’s disordered eating tendencies and their friend’s disordered eating tendencies. The researcher also hypothesized that competitiveness would predict lower friendship quality and codependence would predict higher perceived friendship quality. Results showed that higher levels of competitiveness predicted poorer friendship quality, adjusted R2 = .12, F(1,32) = 4.50, p = .04. For every one unit increase in friendship, an individual’s competitiveness score went down by 0.19 (SE = 0.09), t(34) = −2.12, p = .04. These findings contribute to a deeper understanding of friendships in which one or more individuals have disordered eating tendencies, which can help to inform clinical treatment of disordered eating.
Research has demonstrated that college aged women are at an increased risk of dieting behaviors if they have friends who also engaged in dieting (Miething et al., 2018). This perceived pressure to be thin has predicted engagement in restrictive disordered eating and development of an eating disorder (ED; Rohde et al., 2015). However, an individual who has restricted their food intake, overexercised, or lost a significant amount of weight might consider these traits as positive aspects of themselves. Many researchers have investigated the
1Ryan A McKelley, PhD is the faculty mentor.
social aspects of why individuals engage in disordered eating, but most research has focused on familial or romantic relationships when exploring interpersonal connections (Newton et al., 2005; Rowa et al., 2001); ED literature has rarely investigated platonic relationships. This study investigated friendship quality and elements of codependency and competitiveness. Because previous literature has discussed how college and high school are critical times of transition where individuals relied on their peers as identity agents (Young et al., 2015), it is of the utmost importance to consider how these connections
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could influence maladaptive behaviors.
Research has identified college as an epicenter of sensationseeking due to the prevalence of drinking, substance use, and unhealthy sexual habits. Substance use, specifically, is a normalized aspect of college culture. Although many individuals do not desire to use substances and have not received explicit pressure to do so, the environment and perception of close others can influence college students’ substance use behavior (Keyzers et al., 2020). Moreover, one study found that some friends who utilized substances similarly were more likely to perceive themselves as having a higher friendship quality (Boman et al., 2013), supporting the notion that similar levels of engagement in maladaptive activities could influence friendships.
Friendship and Goal Orientation
Crocker’s theory of interpersonal goals (2011) explores relationships, selfimage goals, and compassionate goals. A selfimage goal is defined as being perceived with positive qualities by friends to raise low selfesteem. However, these goals can have a paradoxical effect. They can lead to dips in self esteem and increased relationship dissatisfaction because the individual has overinvested in themselves and not in their social partner. Moreover, these adverse outcomes increase in intensity as the desire to be perceived in a specific way increases. Compassionate goals in a social relationship describe an actor who focuses on increasing their partner’s wellbeing without having to focus on their own selfesteem. Paradoxically, this is theorized to increase a social actor’s selfesteem.
Clinical literature has found that restrictive disordered eating tendencies are associated with low selfesteem (Brechan & Kvalem, 2015). However, the literature has not explored how these disordered eating tendencies function as a tool for selfimage goals. Social identity theory describes how relationships centered around disordered eating habits are exacerbated by weight loss and restriction because individuals view these behaviors as positive aspects and integral to their group membership (Ellemers & Haslam, 2012). Nevertheless, these behaviors have also been associated with lowered selfesteem which could influence social actors to reinforce their self image goals with their platonic counterparts.
Risk Factors for Disordered Eating in College Students
Depending on the assessed college, the prevalence rates of individuals in the diagnostic range of an ED is anywhere between 2% and 14% in university samples (FitzsimmonsCraft et al., 2019). It is difficult to estimate
the prevalence of atypical presentation of eating disorders in collegeaged students due to the varied definitions of ‘atypical’ in the literature (Harrop et al., 2021), though one study estimated that 17% of students were at risk for developing an atypical eating disorder (CastelaoNaval et al., 2019). Another study found that approximately 86% of eating disorders were reported before age 20, with 43% of those with eating disorders being diagnosed with anorexia between the ages of 16 and 20 (LaCaille, 2013). However, the subclinical population remains relatively unexplored. Studies have estimated that up to 60% of collegeaged individuals within the studies’ samples met the requirements for subclinical disordered eating (Fitzsimmons Craft et al., 2019). However, selfselection likely inflated this estimate. Additionally, the researcher noted that there were inconsistent standards for the criteria an individual had to meet to be considered subclinical between studies. Considering the rate of disordered eating symptoms in college populations, it is possible that various aspects of college life influence the development of disordered eating behaviors.
One defining characteristic of the college experience for many is student athletics. These programs have fostered community among college athletes. Although these environments can nurture healthy habits such as physical activity, goal setting, and teamwork, research has demonstrated that student athletes are at an increased risk of developing eating disorders. A study of over 2,000 student athletes estimated that approximately 25% of participants were at an increased risk for developing an ED (TorresMcGehee et al., 2023). Moreover, they were more likely to express emotional symptoms of eating disorders, such as low selfesteem, competitiveness, and perfectionism.
In addition to athletics, other elements of college culture have created an environment that encourages disordered eating tendencies. For instance, drunkorexia is a colloquial term for individuals engaging in compensatory behaviors to offset the mass caloric intake associated with binge drinking (Bryant et al., 2012). Estimates show that anywhere between 3% and 9% of collegeaged students engaged in severe compensatory behaviors, such as utilizing laxatives or purging, resulting from excess alcohol consumption. In contrast, many more engaged in less severe compensatory behaviors, like extreme exercise and dieting (Bryant et al., 2012). The rumination associated with caloric intake and binge drinking could also contribute to cultivating a college community steeped in diet culture.
College is also a period in which individuals are experiencing significant life transitions that could lead to emotional distress. In adolescence and early
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adulthood, individuals looked to their peers for support more than their families (Sharpe et al., 2014). Research has demonstrated that peer support is integral during transitional times, and those who lacked peer support were more likely to turn to disordered eating. Thus, the transitions associated with college life can potentially contribute to disordered eating behaviors and impact more than a student’s academics.
Negative Impacts of Eating Disorders
Due to the normalization and prevalence of restrictive disordered eating behaviors, many individuals have engaged in these habits without medical concern (Hagan et al., 2000). Thus, individuals living in large or average bodies could have experienced the negative effects of restriction without treatment (Hagan et al., 2000). This lack of treatment can lead to many academic, financial, psychological, and physical consequences that have direct impacts on a patient’s future. For instance, brain fog is a common symptom of a restrictive eating disorder, which can lead to a lower level of cognitive function and less effective memory (Keeler et al., 2022). Brain fog has affected students’ academics, capacity for critical thought, and even life satisfaction.
In addition to cognitive functioning, anorexia nervosa can impact the serotonin system negatively in many patients, leading to feelings of depression (Jimerson et al., 1990; Riva, 2016). Depression is associated with lifelong issues such as struggles to make social connections and decreased life satisfaction. Research has also identified comorbidity between anxiety and eating disorders, because they both share similar underlying factors, such as trauma, stress, or biological predispositions (Pallister & Waller, 2008). Thus, it is common to encounter individuals with behaviors aligning with both diagnoses. Although there are many detriments associated with EDs, these detriments are not without contributing factors, such as the potential influence of dysfunctional platonic relationships.
Disordered Eating and Friendship
For the purposes of this study, friendships were viewed as either based on mutual success or competition. A mutual success friendship based on disordered eating tendencies was defined as two people engaging in their restrictive eating habits together and supporting each other in a way that is ultimately maladaptive. There were multiple reasons why mutual success may underlie friendships based on disordered eating. First, the theory of attraction based on similarity guided the approach to EDbased friendships. From an attractionsimilarity theory perspective, if an individual perceived their friend as similar to them in terms of ED behavioral tendencies,
these perceptions of similarity led to behaviors that made it more likely that friendship formation would occur (Morry, 2005). The researcher operationalized mutual success with a codependency scale because the researcher ultimately based a mutually successful friendship on an uncontrolled emotional attachment between a pair. Moreover, having friends who validated maladaptive behaviors, such as disordered eating, helped others to validate their sense of self (Talaifar & Swann, 2020), encouraging the pair to continue their unhealthy friendship.
In contrast to mutual success friendships, a competitive friendship based on disordered eating tendencies can be defined as a toxic pairing of two people with EDs who engaged in behaviors meant to demean the other’s appearance or critiqued their ED behaviors as ineffective. For example, consider a situation in which Friend A might have commented on Friend B’s physique while Friend B ate an “unhealthy” snack or meal; in response, Friend B commented on Friend A’s large meal the night prior. Previous research has demonstrated associations between competitiveness and engaging in ED behavior (Burckle et al., 1999; Osborne, 2023), supporting this proposed concept. Numerous people with EDs have engaged in the degradation of others to improve their selfesteem (Burckle et al., 1999). In a competitive ED friendship, people find themselves regularly insulted and “oneupped” by their friends. This increases the person’s desire to engage in restrictive and aggressive eating behaviors. The consequences of this ED friendship include a person’s disordered eating progressing to the point of extreme harm. The nature of the competitive friendship is inherently “me vs. you,” but the pair find themselves addicted to the rush of potentially being sicker than the other. Thus, they stay in this damaging friendship.
Gaps in the Literature
Unfortunately, few studies have directly examined friendships between people with disordered eating tendencies as being cooperative or competitive. A limited body of research related disordered eating to lower friendship quality (Sharpe et al., 2014). In other words, there has been no analysis of how competitive or cooperative friendships could contribute to diminished friendship quality within the context of disordered eating. This study aimed to fill a gap in the literature by investigating the relationship between perceived friendship quality, competitiveness, codependency, and disordered eating tendencies. Understanding how characteristics consistent with disordered eating could influence social relations may provide insight to clinicians and academics, fostering the effective development
Hypotheses
Hypothesis 1: There will be a positive correlation between engaging in ED behaviors and surrounding oneself with others who engage in ED behaviors.
Hypothesis 2: Higher levels of mutual success will predict higher levels of friendship quality, and higher levels of competitiveness will predict lower levels of friendship quality.
Method
Sampling Methods
This research utilized a twopart quantitative design and recruited participants from a convenience sample of introductory psychology students at a Midwestern university participating in research studies for course credit. Participants completed an initial quantitative measure to assess inclusion in the study and then completed additional questionnaires in the presence of the principal investigator.
Procedure
Prior to data collection, the institutional review board at the university where data were collected approved the study. Participants responded to an online screener survey where they answered questions regarding disordered eating behaviors and responded to a body dissatisfaction measure. They received course credit for their participation. Those who met the qualifications for the main study were invited to the lab and asked to bring an oncampus friend of the same gender. In the lab, both the participant and their friend completed an online survey accessed through a QR code. Both participants completed separate surveys that contained the same questions regarding their perception of their friendship quality with their partner, disordered eating habits, competitiveness, and codependent tendencies. The main study reassessed the degree of disordered eating tendencies of the original participant and assessed their partner for the first time during the main study. They were in the same room but asked to make space between one another so they could not see their partner’s answers, and they were observed by the principal investigator throughout the duration. Depending on their individual preferences, both participants received compensation for their time with $5 cash or course credit. It is important to note that the individual who qualified in the screener study received an additional incentive in the main study while their friend only received an incentive from the main study because they did not participate in the screener study. Participants were debriefed after completing their survey and received an additional debriefing in the form
of an email that provided resources to the university’s counseling center.
Operational Definitions
The primary research question investigated disordered eating between platonic partners, codependency, and competitiveness and how they impacted friendship quality. This study focused on restrictive disordered eating, and covered mostly anorexic tendencies, which were measured with an emphasis on restricting food intake and preoccupation with one’s body. Moreover, orthorexia is included in this definition and measured by analyzing how preoccupied participants were with eating healthy and working out. Bulimia is defined as episodes of binging and purging. For the purposes of this study, the researcher placed their focus on restrictive disordered eating tendencies, with some focus on bulimic/compensatory behaviors. The researcher did not examine Binge eating disorder (BED) in this study. Thus, this study used disordered eating as an umbrella term and does not include binge eating.
Regarding friendship dynamics, competitiveness is defined as aggressively pursuing success over another. Those in a competitive friendship were likely to put down their platonic partner to fulfill their sense of success and engage in constant comparison. Finally, mutual success is defined as a maladaptive teamworklike codependent relationship in which individuals engaged in disordered eating tendencies together. Moreover, the study emphasized the codependent dynamic of relationships because individuals relied on their friends for continued support in their disordered tendencies, and they felt great levels of satisfaction when their friend also achieved their disordered eating goals.
Sample Characteristics
Participants were students from a Midwestern university and were recruited through an online portal to meet course credit requirements. Demographic questions can be found in Appendix A, along with all other appendices can be found at https://osf.io/s6avd As displayed in Table 1, 135 participants (76% women, 21% men, 1% nonbinary/third gender) responded to the screener, and the participants were ages 18–22 ( M = 18.74, SD = 0.79). Most participants in the screening were White (94%), and the rest identified as Asian American (3%), Native Hawaiian or Other Pacific Islander (1%), Hispanic or Latino/a (1%), or Other (3%). 51 participants qualified for part two of the study, and 17 agreed to participate. Thus, 17 pairs of undergraduate students (88% women, 12% nonbinary/third gender) between the ages of 18 and 22 (M = 19.24, SD = 1.34) completed the main study.
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Most participants in the main study were White (88%), and the rest identified as Black (3%) or Other (9%).
Materials
Participants in the screener study titled “Body Image and Eating” took the Body Dissatisfaction Scale adapted from Mutale et al. (2016; Appendix B) and the Eating Disorder Examination Questionnaire (EDEQ; Fairburn, 2008; see Appendix C). The EDEQ consisted of 23 multilevel response questions (α = .92) investigating disordered eating behavior over 28 days. An example item is “Have you been deliberately trying to limit the amount of food you eat to influence your shape or weight (whether or not you have succeeded)?” with response options of 1 (no days), 2 (1–5 days), 3 (6–12 days), 4 ( 13 – 15 days) , 5 ( 16 – 22 days), 6 ( 23 – 27 days) , and 7 (every day). The Body Dissatisfaction Scale consisted of two forcedchoice questions paired with female or male body stimuli depending on the participant’s gender identification. These stimuli have nine bodies ranging from severely slim to very large. Participants answered the first question, “Which body is your ideal body for yourself?” and the second question asked, “Which body is your current body?” Participants selected the body type that most closely fits their perception.
Participants who took the screener study and were in the upper mean split of the EDEQ questionnaire (M = 2.38, SD = 1.07) qualified for the main study. Participants were brought into a lab environment with their friends and scanned a QR code with their phones to take the survey, “Food and Friendship.” Participants took the Eating Attitudes Test (Garner et al., 1982), Drive for Muscularity Scale (McCreary & Sasse, 2000), Friendship Qualities Scale (Bukowski et al., 1994), the McGill Friendship Questionnaire (Mendelson & Aboud, 1999), the Holyoake Codependency Index (Dear & Roberts, 2005), and the Competitiveness Orientation Measure (Newby & Klein, 2014). All means, standard deviations, correlations and overall Cronbach’s alpha values for the main study were reported in Table 2.
The Eating Attitudes Test (EAT 26; Garner et al., 1982; see Appendix D) is a 26item measure that measures disordered eating behaviors through multilevel response questions ranging from 1 (always) to 6 (never) for statements such as “I am terrified of being overweight.” This questionnaire included a 13item Diet subscale (α = .88), a 6item Bulimia/Food Preoccupation subscale (α = .89), and a 7item Oral Control subscale (α = .53).
Participants were then presented with the Drive for Muscularity scale (McCreary & Sasse, 2000; see Appendix E), whose psychometric properties have been assessed thoroughly as reliable in the clinical literature (de Carvalho, 2019; Wojtowicz & von Ranson, 2006).
This scale had 15 questions that analyzed a person’s desire to be muscular or “fit.” The researcher integrated with hopes of reaching male populations. The researcher measured this scale using multilevel response questions ranging from 1 (always) to 6 (never), with statements such as “I think that I would look better if I gained 10 pounds in bulk.”
Participants completed the McGill Friendship Questionnaire (Mendelson & Aboud, 1999; see Appendix F). Overall, the McGill Friendship Questionnaire analyzed how platonically close two individuals were to each other. Statements from this questionnaire included “_____ would still want to be my friend even if we had a fight.” This scale had 30 multilevel response questions ranging from 1 (never) to 9 (always), which contained six subscales. The Help subscale contained five items ( α = .79 ) and measured how much a friend provided guidance, assistance, and tangibles to the other. Emotional Security contained five items (α = .88) and measured the amount of comfort a friend provides in a threatening situation. The Self Validation subscale consisted of five items (α = .88), which measured how
TABLE 1
Demographic Characteristics of Participants
much the friend helped maintain another’s selfimage. The Intimacy subscale contained six items (α = .87) and measured the “sensitivity to other’s needs” (Mendelson & Aboud, 1999) and the acceptance of another’s feelings. This subscale also covered selfdisclosure. The Stimulating Companionship subscale contained five items (α = .84) that measured how regularly a pair of friends engaged in activities together that they enjoyed. The Reliable Alliance subscale consisted of four items (α = .85) and measured the certainty that the partner would be available and loyal.
Participants replied to statements from the Friendship Qualities Scale (Bukowski et al., 1994). This scale measured how platonically close two individuals were to one another. Though originally intended for youth, it has been utilized in older adolescent and young adult populations (Ponti et al., 2010) and its questions were deemed appropriate for the population measured. It had 23 multilevel response questions from 1 (very accurate ) to 5 ( very inaccurate ) . This questionnaire contained five subscales that measured Companionship in four items (α = .60), Conflict in four items (α = .59), Help in five items (α = .35), Security in five items (α = .73), and Closeness in five items (α = .77). This scale had statements such as “If there is something bothering me, I can tell my friend about it even if it is something I cannot tell to other people.”
Participants were presented with the Competitiveness Orientation Measure (Newby & Klein, 2014), which had 37 multilevel response questions that ranged from 1 (strongly disagree) to 5 (strongly agree). This measure contained four subscales that measured different elements of competitiveness. For instance, General Competitiveness contained 13 items (α = .84) and measured how much an individual enjoyed competition. Next, Dominance contained 12 items (α = .91) that measured the extent to which an individual would randomly compete in their daytoday life; moreover,
TABLE 2
Mean, Standard Deviation, Cronbach’s Alpha, and Pearson Correlation Matrix of Observed Variables Variables
1.
2.
3.
4.
5.
6.
7.
it measured the extent to which a person wants to be compared to others. Competitive Affectivity contained eight items (α = .88) that measured the extent to which an individual exhibited feelings of extreme superiority from competition. Next, Personal Enhancement contained four items (α = .74) that measured the extent to which a person desired to compete to demonstrate competence and achievement. An example of the statements from the competitiveness orientation measure is, “I view almost every situation as a way to prove that I am better at things than others.”
Participants were asked to complete the Holyoake Codependency Index (Dear & Roberts, 2005). This 13 question measure included three subscales that measured SelfSacrifice, External Focus, and Reactivity. SelfSacrifice contained four items (α = .60) that measured the participants’ focus on other’s needs instead of their own. External Focus had four items (α = .73) and measured the concern for another’s approval. Reactivity measured how regularly the participant altered their beliefs toward the problematic behavior of family members in five items (α = .59). An example of the questions asked in this index is “my life is controlled by my friends’ behavior and problems.” The researcher measured this scale using multilevel response questions from 1 (strongly disagree) to 5 (strongly agree)
Results
Following the screener survey, participants in the upper mean split (M = 2.33, SD = 1.07) were invited to participate in the main study. Preliminary analyses suggested that the participants from the screener study had a difference in their perceived versus ideal bodies according to the Body Dissatisfaction Questionnaire, with most participants expressing a desire to be thinner than they currently perceive themselves (M = 1.01, SD = 1.64).
To test the first hypothesis, the researcher randomly assigned each pair member to a group and assured the opposite group contained the other member. Then, the researcher used a Pearson r correlation between the pairs’ different EAT26 average scores. No significant correlations were found, r(17) = .16, p = .55, 95% CI [−.35, .59]. A post hoc power analysis, utilizing the expected effect size Cohen’s q = .30, indicated a power of .12 to detect significance at alpha level .05, indicating an underpowered study. Future studies with a larger sample size might be able to detect a relationship between a pairs’ disordered eating habits.
The second hypothesis stated that higher levels of codependency (Holyoake Codependency Scale) would predict higher levels of friendship quality (Friendship Qualities Scale or McGill Friendship Questionnaire). In comparison, higher levels of competitiveness
(Competitiveness Orientation Measure) would predict lower levels of friendship quality (Friendship Qualities Scale or McGill Friendship Questionnaire). The researcher used an Ordinary Least Squares (OLS) regression, as demonstrated in Table 3. The analysis included age as a predictor in the model due to a significant positive correlation, as seen in Table 2. An OLS regression found that codependency did not explain a significant proportion of variance in friendship quality in the Friendship Qualities Scale (adjusted R2 = −.01, F(2,31) = 0.87, p = .43), t(34) = − 0.72, p = .48, or the McGill Friendship Questionnaire (adjusted R2 = −.06, F(2,31) = 0.5, p = .95), t(34) = − 0.24, p = .81. A post hoc power analysis, utilizing the expected effect size Cohen’s f2 = .15 indicated a power of .59 to detect significance at alpha level .05. This power analysis revealed low power in the study. Future studies with a larger sample size might be able to detect a relationship between codependency and friendship quality.
An OLS regression analysis found that competitiveness did not explain a significant proportion of the variance in friendship quality in the McGill Friendship Questionnaire (adjusted R2 = .06, F(1,32) = 3.21, p = .83), t(34) = − 1.79, p = .08. The next OLS regression found that competitiveness explained a significant proportion of variance in friendship quality in the Friendship Qualities Scale (Bukowski et al., 1994; adjusted R2 = .12, F(1,32) = 4.50, p = .04). For every one unit increase in friendship, an individual’s competitiveness score went down by 0.19 (SE = 0.09), t(34) = −2.12, p = .04. In other words, the less competitive the pairs were, the more likely they were to have a higher friendship quality.
Discussion
Summary
Disordered eating is an ailment that has influenced many individuals’ lives, whether they themselves have experienced it or have seen someone they care about experience it. Although the current literature is interested in the concept of familial ties/issues and disordered eating, it is essential to consider other social factors, such as friendship.
Hypothesis 1, which predicted a positive correlation between engaging in ED behaviors and surrounding oneself with others who engaged in ED behaviors, was not supported. Using Crocker’s framework, this suggests that disordered eating is unrelated to selfimage goals. Essentially, it is not a positive aspect individuals would assert in their relationships or a trait their platonic partner would desire to associate with. However, these findings contradict the similarityattraction hypothesis in that individuals were not attracted to others who share similar traits in this area. It is important to consider that
these findings could be due to low power in the study.
Hypothesis 2 stated that higher levels of mutual success would predict higher levels of friendship quality, and higher levels of competitiveness would predict lower levels of friendship quality. However, analysis revealed no predictive relationship between mutual success and friendship quality. Within Crocker’s framework (2011), it is possible that this is because codependency is inherently pathological but can present as prosocial (McGrath & Oakley, 2012); thus, it does not have a clear influence on friendship quality. However, higher levels of competitiveness in a friendship predicted lower friendship quality, which is consistent with other studies. The findings are consistent with Crocker’s theory of interpersonal goals (2011) in that perceived friendship quality decreased with the over assertion of disordered eating habits as a perceived positive trait.
Clinical Implications
Previous research has shown that anorexia nervosa is associated with competitiveness (Burckle et al., 1999; Osborne, 2023). Thus, a person with anorexia who is competitive might experience lower friendship quality. Although this study did not examine the direct link between disordered eating behaviors and competitiveness, it remains an area of importance in disordered eating research. Adverse effects on body dissatisfaction and compensatory behavior may be indirectly influenced by lower friendship quality, which can lead to feelings of loneliness, anxiety, and depression, all of which are precursors to developing disordered eating tendencies (Davies, 2004).
These findings could benefit college communities, where counseling centers can prioritize teaching the importance of healthy platonic relationships. Moreover, clinicians could benefit from exploring the makeup and
TABLE 3
Predictors of Friendship Quality
DV: Friendship Qualities Scale
DV: McGill Friendship Questionnaire
Note.
dynamics of their clients’ social circles to understand the necessity of discussing healthy friendships with clients.
Limitations and Future Directions
Several limitations to this study might have influenced the ability to detect the effects of friendship quality and disordered eating tendencies. This study lacked statistical power due to the small, specific sample size, so it is possible that a larger sample size would reflect differences in the sample population. Additionally, there were multiple potential contributors to low volunteer rates. Initially, invitations to the main study were sent out to those who averaged one standard deviation above the mean on the EDEQ. However, only one participant from this cohort of 22 agreed to participate. Studies have found that higher rates of depression or disordered eating are associated with an increased likelihood of dropping out of eating disorder interventions (von Brachel et al., 2014). It is possible that participants with the highest levels of disordered eating had a decreased likelihood of continuing with the study. Thus, selfselection could also impact the clarity of results by potentially skewing data or decreasing generalizability.
Next, most participants were cisgender white women, and the results did not contribute to closing the gap of racial disparities in eating disorder research. A metaanalysis of eating disorder literature found that most of the studies had a predominantly White female population. Moreover, the same study found inadequate amounts of psychological literature regarding cultural considerations and eating disorders (Acle et al., 2021). Future studies should focus on having a more inclusive sample to mitigate these disparities. Moreover, no males were assessed in the final study, even though many qualified in the screener study. The researcher found the reason why men did not volunteer for the main study to be unclear, but it is possible the answer regards the stigma of body image and disordered eating in men (Maloney et al., 2024). Unfortunately, this research does not contribute to analyzing disordered eating in men due to the lack of volunteers.
This study focused on restrictive disordered eating habits and did not explore binge eating disorders. It would be inappropriate to apply these findings to BED, and a separate study should investigate similar research questions with disordered eating behaviors aligning with BED. Moreover, there were only six questions in the EAT26 that assessed bulimic tendencies, making it inadequate to draw conclusions between friendship and bulimic disordered eating habits.
Finally, it is necessary to consider that this study had some logistical limitations. For instance, the participants likely brought a friend out of convenience with scheduling rather than the degree of friendship quality.
Additionally, participants could have friends they engage in disordered eating with because friend groups can be incredibly large, but they did not bring such friends to the study. A potential way to mitigate this is by not utilizing a paired analysis method and encouraging participants to think about their closest friend when answering questions related to relationship quality.
With these limitations in mind, this study provides a perspective that could benefit mental health practitioners and widen the scope of understanding of a client’s eating habits. Especially in adolescence and early adulthood, individuals tend to push away from their parents and rely more on their peers (Sharpe et al., 2014). Thus, including a client’s social circle in a practitioner’s understanding of a client’s disordered eating habits would be beneficial, especially in assessing the degree to which a client’s friendship network also engages in disordered eating behaviors.
Future studies should include a qualitative element when exploring platonic relationships and friendship dynamics to truly comprehend the depth and complexity of such relationships. Additionally, investigators could include an intuitive eating intervention where friendship quality is assessed between a pair of friends before and after practicing intuitive eating. Otherwise, future researchers would benefit from investigating parallel disordered eating behavior between friends through the lens of selfdetermination theory, essentially exploring parallel maladaptive behaviors as an act of relatedness. Including social media use and exposure to proanorexia websites as a variable when investigating the relationship between disordered eating and friendship quality among adolescents or young adults could also be insightful in future studies.
Conclusion
The importance of platonic relationships in overall wellbeing has been underresearched in the psychological community. Within the context of restrictive disordered eating tendencies, friendships endorsing maladaptive behaviors could contribute to new or worsening eating restrictions. The main takeaway from this study is the continued support for competitive traits predicting lower friendship quality. Clinician identification of this trait in patients could provide insight into potential strain in their patients’ social circle. This could be especially relevant if their patient presents with restrictive disordered eating tendencies. Understanding the interconnectedness of these traits could help guide clinical intervention. However, it is vital that research continues to explore the concepts of codependency, competitiveness, friendship quality, and disordered eating to develop a nuanced understanding of disordered eating’s influence on patients’ everyday lives.
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Author Note
Abigale G. Hartwig https://orcid.org/0009000755562034
There are no known conflicts of interest to disclose. This study was supported financially by University of Wisconsin–La Crosse’s Undergraduate Research and was counseled by the University of Wisconsin–La Crosse’s Statistical Counseling Center. The author received permission to publish all measures listed in the appendices, which can be found at https://osf.io/s6avd. Thank you, Dr. Ryan McKelley, for your mentorship.
Mindful Strides: A Brief Associative Attention Intervention to Improve Running Performance Among College Students
Natalie K. Johnson, Madeline E. Adair, Lindsay Johnson, Robert R. Wright*, and Samuel L. Clay* Department of Psychology, Brigham Young University–Idaho1
ABSTRACT. Despite the welldocumented health benefits of engaging in 150 minutes of weekly moderateintensity aerobic activity, less than 1 out of 4 Americans meet this guideline. Challenges in maintaining exercise routines often arise due to a lack of immediate performance results, creating substantial perceived barriers to engaging in physical activity. Associative attention, which is a cognitive technique where one focuses on bodily sensations and mindfulness during physical activity, shows promise as a trainable strategy to improve performance, particularly running. This study examined the efficacy of brief 15minute intervention training associative attention mindfulness techniques to enhance running performance among nonathletic college students. Participants (N = 117) were assigned to 1 of 4 conditions: exercise only, associative attention, combined exercise and associative attention (associative exercise), and control. Performance was assessed using performance time and attention style ratings at baseline and followup (one week later) 1 mile runs. ANOVA revealed significant performance improvements in all groups compared to the control group F(3, 112) = 3.46; p = .019, Cohen’s f = .30, η 2 = .08 , with the associative only group demonstrating the most substantial improvement (M = 42.58 seconds, SD = 71.69; p = .002). Attention style ratings showed a similar pattern, with associative ratings increasing and dissociative ratings decreasing from baseline to followup. These findings underscore the efficacy of brief interventions in enhancing performance and shifting attention styles, offering practical implications for nonathletic populations.
Despite the multifaceted benefits of physical activity in maintaining good health and wellness, including enhanced brain health, weight management, and decreased disease risks, a substantial disparity persists: only 24.2% of Americans adhere to the recommended guideline of 150 minutes of moderateintensity aerobic activity per week (Centers for Disease Control and Prevention, 2022). This concerning trend is not limited to the general population, but also applies to young adults aged 18 to 25, as evidenced by a national study indicating that only 51.3% of college students engage in sufficient exercise (American College Health Association, 2014), with a recent study suggesting physical activity has decreased within the last 5 years during the COVID19 pandemic (Wright et al., 2023). Furthermore, college students face unique cognitive, social, and emotional challenges that increase barriers to
incorporating regular exercise. Many may struggle with academic pressures (Holschuh, 2019), financial stress from balancing work and school and/or inadequate funding (Onuoha & Idemudia, 2020), and increasing mental health challenges (Kitzrow, 2003). Moreover, most U.S. college students are developmentally emerging adults in a stage of frequent transitions and other unique stressors (Park et al., 2020), and many even fail to complete college (Gerdes & Mallinckrodt, 1994). Considering the cumulative impact of all these demands, it is unsurprising that many college students experience significant barriers to prioritizing exercise routines, as well as potentially lacking the focus needed to improve their performance.
Collaborative efforts among various entities such as public health campaigns, educational initiatives, media outlets, and healthcare professionals aim to
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promote physical activity through channels like the internet, social media, and mainstream media (Dunlop et al., 2016; Goodyear et al., 2021; World Health Organization, 2018). Research suggests that these efforts have been effective, as up to 94% of Americans are aware of the benefits of physical activity (Morrow et al., 2004). Nonetheless, despite this widespread awareness, the gap between knowledge and action remains apparent.
Some individuals encounter challenges when integrating exercise into their daily lives, possibly due to perceived delays in performance improvements. Considering the heavy mental load under which college students routinely operate, a lack of performance improvement may even act as a larger motivational barrier for college students than for the general population. However, psychological strategies can be used in training to assist athletes in maximizing their capabilities through settings such as classes, programs, and interventions. In endurance sports, the associative attentional style refers to an emphasized focus on internal cues such as breathing and muscle tension, and has been shown to enhance performance, particularly among college athletes (Masters & Ogles, 1998). Building on this groundwork, our study aimed to investigate the differences in attentional focus related to performance on a running task among nonathlete college students, a group that has not been a focus of this type of mindfulness training. Specifically, we evaluated the efficacy of a brief 15minute intervention emphasizing associative attention (i.e., measured as a rating) and its impact on onemile running performance (i.e., completion time) among active and inactive college students.
Attentional Style and Physical Activity
The widespread lack of regular physical activity among college students can be attributed to several factors rooted in psychological perceived barriers and lack of motivation. These challenges significantly impact individuals› ability to engage in beneficial activities like walking, running, swimming, and cycling (WHO, 2018). Some common barriers include lack of time or priority, insufficient social support from friends and family, feelings of being unfit or unskilled, and health issues (e.g., heart disease, diabetes, high blood pressure; Golaszewski & Bartholomew, 2019; Trost et al., 2002). As a result, individuals may find their options for physical activity limited, and their comfort and confidence levels during participation dampened (Posadzki et al., 2020). Additionally, individuals may become discouraged due to a lack of immediate results, such as weight loss or improved physical performance (e.g., faster running times; ColladoMateo et al., 2021; Duncan et al., 2010). Although the lack of immediate performance
improvement is just one factor contributing to lack of regular exercise, it is tied to both physiology and psychology. In recent years, there has been a growing emphasis on understanding the intricate connection between the mind and body in the field of performance psychology, which has produced new psychological techniques to help athletes enhance their sport performance (Weir, 2018). These include several psychological skills, techniques, and models that have allowed practitioners to help athletes in very practical ways (Lochbaum et al., 2022). For example, the practice of mindfulness, or cultivating a mental state focused on the present moment, has been widely studied as a psychological skill for athletes. Hill et al. (2020) found mindfulness training to improve the experience of flow along with enhancing running economy in trained runners. Furthermore, Kee et al. (2012) found that mindfulness induction improved performance in a postural balance task and promoted external attentional focus strategies. A metaanalysis of studies examining impacts of mindfulness practice on performance outcomes found improvements to both physiological and psychological performance, including particular outcome benefits for precision sports (Bühlmayer et al., 2017). Another relevant psychological technique is attentional focus, which refers to an individual’s way of focusing on or being mindful of specific stimuli in a particular moment (American Psychological Association, 2018). Attentional focus strategies, particularly external focus, have been found to enhance motor performance and learning across different tasks, skill levels, and age groups (Wulf, 2013), whereas increased cognitive load and cognitive fatigue during selfpaced running negatively affected performance (McCarron et al., 2013; MacMahon et al., 2014). Conversely, moderateintensity exercise such as running has been shown to enhance sustained attention (Radel et al., 2018), particularly through impact to attentional control mechanisms (Dodwell et al., 2021), suggesting a dynamic bidirectional relationship between attentional focus and running. Other researchers have examined athletes’ attentional strategies in endurance sports and have identified two main strategies: associative and dissociative attentional styles (Masters & Ogles, 1998; Morgan & Pollock, 1977). Although these attention styles can coexist, they are distinct, involve voluntary action in their adoption, and typically when one style is dominant, the other tends to be used less (Masters & Ogles, 1998). Although some literature favors a broader, mindfulnessbased conceptual model over the distinction between associative and dissociative attentional styles discussed by Masters and Ogles (1998) and Morgan and Pollock (1977; Salmon et al., 2010), the associative and dissociative attentional styles as cognitive
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strategies continue to be relevant in sports psychology research. For example, Rodríguez et al. (2017) demonstrated that engaging in both associative and dissociative attentional tasks during moderateintensity running more effectively activates the central nervous system.
The associative attention style, often favored by elite athletes, emphasizes mindfulness with a focus on the body, including muscle tension, breathing, and exertion (Hutchinson & Tenenbaum, 2007). This style also includes attention to task specifics, like one’s position relative to other runners or target performance goals (e.g., laps), which helps eliminate distractions, enabling runners to concentrate fully on their performance (Brick & Campbell, 2014). Conversely, dissociative attention involves disconnecting from physical sensations and focusing on external content or unrelated thoughts, effectively diverting attention away from the body and the task at hand (Masters & Ogles, 1998). For instance, during running, dissociative attention might lead a person to think about what they will do after their performance or focus on some external stimuli that is unrelated (e.g., ambient sounds). In noncompetitive settings, a dissociative mindset can help alleviate pain and discomfort by turning the focus towards distractions such as music, conversation, or unrelated thoughts (Brick et al., 2014). However, although tuning out discomfort may enhance enjoyment and training benefits, it can lead to slower performance times.
Associative Attention Style and Physical Activity
In competitive situations where performance is measured, the associative style has been shown to be more effective at improving performance. In the late 1970s, Morgan and Pollock (1977) were among the pioneering researchers investigating elite marathon runners’ cognitive strategies during performance. Contrary to their initial hypotheses, they discovered that elite athletes mainly utilized associative strategies, focusing on present sensations rather than efforts to distract or distance themselves from physical pain and discomfort during the physical exertion. This focus differed from that of recreational athletes who employed dissociative strategies with distraction techniques like reminiscing or imagining unrelated scenarios. These findings challenged the researchers’ expectations, as they had anticipated that the elite marathoners would use dissociative techniques to alleviate the enduring discomfort, not the recreational athletes who participated in less demanding competitive environments.
Since the study conducted by Morgan and Pollock (1977), several other studies have examined the relationship between attention style and improved performance (Masters & Ogles, 1998). Consistent with Morgan and
Pollock (1977), many of these studies suggest that associative attention styles are more effective. For instance, in a study involving 60 experienced runners, different running environments and cognitive strategies were tested for their impact on performance and perceived exertion. Runners completed three 5kilometer runs in varied conditions: on a treadmill, indoor track, and outdoor road. Half monitored heart rate and pace (associative), and the other half listened to music (dissociative). Results indicated the associative group performed significantly better, finishing runs 1 minute 47 seconds, on average (LaCaille et al., 2004).
Attentional style interventions have demonstrated utility in enhancing performance for athletes at all levels (Reinebo et al., 2024; ReyesBossio et al., 2022). For instance, in one study, novice rowers underwent ten training sessions over several weeks to train attentional style and investigate subsequent performance (Scott et al., 1999). The study involved three conditions (associative, dissociative video, and dissociative music) using 40minute workout sessions. The results supported the associative attentional style as the most effective condition, leading to the most significant gains. Similarly, Razon et al. (2010) and Stanley et al. (2007) provided valuable insights into the effects of attentional strategies on perceived exertion and performance among emerging adult college students, both finding associative attention to optimize performance and manage perceived exertion.
Associative Attention Style Brief Interventions and College Students
Psychological interventions, though impactful, can often be timeconsuming and lengthy. Fortunately, research indicates that even simple and brief interventions can yield longlasting effects with the potential to create lasting impacts that persist for months or even years (McGonigal, 2015). For instance, Crum et al. (2011) found that mindsetbased interventions can significantly influence physiological responses. Participants who believed they consumed a highcalorie “indulgent” shake showed a greater decline in hungerregulating hormones than those who thought they had a lowcalorie “sensible” shake. Similarly, Crum et al. (2023) addressed vaccine hesitancy through a three minute video, leading to reduced worry about symptoms, fewer reported symptoms, and an increased intention to vaccinate.
Brief interventions provide the benefit of not taking as much time, which is particularly attractive for nonathlete college students. Indeed, nonathlete college students often prioritize academics, social interactions, and personal interests over adhering to structured exercise routines (LaFreniere, 2024). Consequently, they may spend
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Strides | Johnson, Adair, Johnson, Wright, and Clay
prolonged periods sitting while studying or attending classes, contributing to sedentary behavior becoming a social norm among college students. A recent metaanalysis found that they spend an average of 7.29 hours sitting per day(Castro et al., 2020). Additionally, they frequently engage in sedentary leisure activities like social media, movies, and video games, which further detract from physical exercise opportunities (Lachman et al., 2018). Thus, brief interventions targeting a physical activity such as running may be a particularly effective way to train the associative attention style and observe running performance improvements.
Current Study
Despite many studies on interventions using associative attention styles, no study to date that we are aware of has examined the potential efficacy for enhancing performance outcomes using a brief intervention among average (as opposed to elite or athletic) young adults for running performance. This is important because many college students struggle with regular physical activity, and this training could empower them to overcome discouragement and continue physical activity (Zhu et al., 2024). Hence, the current study assessed whether a brief 15minute intervention can improve running performance of general college students. The current study examined the impact of a brief intervention in the interim of two one mile runs one week apart. This approach is particularly novel as it targets nonathlete students and utilizes a brief 15minute intervention, which could improve engagement and retention of associative techniques. By combining these two innovative elements, we anticipate that the unique context of nonathletes, coupled with the effectiveness of a brief intervention, will lead to pronounced improvements in performance and attentional style. The intervention included instructional guidance, practical application facilitation, and sharing knowledge, which is based on other mindset change protocols (McGonigal, 2015). We predicted that those receiving the associative intervention would show more improvement than those not receiving the intervention. This anticipation is reflected in our hypotheses as follows: H1: Attentional style training for associative will lead to performance time improvements; H2a: Attentional style training for associative will lead to associative ratings increases; and H2b: Attentional style training for associative will not be associated with dissociative rating increases.
Method
Participants
After receiving approval from the university’s institutional review board, students were solicited as a convenience
sample from several upper division college courses (e.g., health psychology), resulting in a total sample of 117 student participants. Based on power analyses aimed at achieving .80 power, we initially sought a sample size of at least 125 participants to detect a 0.50 Cohen’s d effect size with t tests and 0.30 Cohen’s f effect size with ANOVA. Initially, 133 students volunteered to participate, but 16 were removed because they did not complete all parts of the study. All student participants were at least 18 years old and provided consent. Each participant received compensation consisting of a $5 cafeteria coupon. Additionally, they were provided with snacks after running including sports drinks, bananas, and granola bars. All participants were also offered extra credit by their instructors in the course from which they were recruited. The average age of the participants was 22.11 (SD = 2.44), and 70.94% of them were women with 29.06% being men. Most were White (82.2%), with 9.3% Hispanic/Latino(a), 4.7% more than one race, 3.1% Black/African American, and 0.01% Asian American (one participant) also represented.
Procedure
Conditions and Intervention Groups
The study encompassed four distinct conditions: exercise only (exercise group), associative attention style (associative group), combined exercise and associative attention style (associative exercise), and a control group. Data collection spanned eight total weeks, wherein each group underwent a specific two week interval for data collection in this order: the exercise group, the associative group, the associative exercise group, and finally the control group. Participants within each group engaged in both baseline and follow up run performance (time) assessments, with baseline runs occurring on odd weeks and followup runs on even weeks. This sequential design ensured a systematic and phased data collection approach across all conditions. Participants selected designated time slots tailored to align with the typical availability of college students and to ensure consistency in the diurnal timing of their runs. To maintain methodological rigor, we excluded time slots earlier than 3:30 p.m., as well as Mondays and Fridays, acknowledging insights from previous research highlighting mood fluctuations throughout the week (Bruggisser et al., 2023; Harvey, 2015).
Measures
Baseline Survey and Run. All procedures detailed below were uniformly applied across the four conditions, starting with participants completing an online questionnaire via a Qualtrics link. Participants indicated their sex, age, and race. Biological sex was determined
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using a dichotomous variable, with options for “Female” or “Male.” For age, participants were asked to report their age in years. Race was determined through the following question: “What is your racial background? (select only one).” Answer choices included American Indian/Alaskan Native, Asian, Native Hawaiian/Pacific Islander, Black/ African American, White, Hispanic/Latino(a), more than one race, and other. Upon survey completion, participants were equipped with heart rate monitors, which they placed around their sternum. Subsequently, under research assistant (RA) guidance, participants ran on a treadmill at the university fitness center, with encouragement to complete a mile as fast as they could. Following a scripted protocol, researchers provided information and encouragement, emphasizing participants’ autonomy in controlling treadmill speed and the option to stop at any point if they experienced excessive discomfort. Participants were not permitted to listen to music while running to maintain control and consistency and facilitate reliance on internal cues such as attentional focus. Participants were encouraged to ask questions if needed.
An initial warmup phase consisted of a 2.5minute walk, during which the researchers set the speed at a comfortable brisk pace of 2.5 miles per hour that all participants were capable of walking at. Brisk walking pace ranging from 2.5–3.5 miles per hour is considered beneficial as it has been shown to help reduce the risk of injury while running by warming up the legs (Cigarroa et al., 2023; Fradkin et al., 2006). To prevent participants from viewing their time or speed during the subsequent run, visual obstruction using tape and boxes was arranged so the researcher could see the distance and heart rate but the participant could not. This precautionary measure aimed to ensure unbiased performance during the run and maintain the integrity of the study design. During the warmup, researchers monitored the timing and ensured heart rate monitor functionality by standing to the side of the participant. Participants were then instructed to commence running at their maximal pace for a mile. During the run, the researchers used their phones to time participants and recorded the distance covered on the treadmill machine (including the warmup distance plus one mile), ensuring participants completed a full mile. Additionally, the researcher delivered scripted words of encouragement periodically, recorded participants’ heart rates at oneminute intervals to ensure consistency between baseline and followup run efforts, and documented the finishing time. Upon reaching the onemile mark, participants concluded the run, followed by a 2.5minute cooldown walk set at 2.5 speed, designed to lower the heart rate within approximately 2 minutes, allowing for a gradual recovery (Mayo Clinic, 2023).
Johnson,
After the cooldown, participants were prompted to rate their levels of associative and dissociative attention styles during the run using a rating scale (1 = rarely, 7 = just about the entire time). Definitions were provided for clarity such that: associative attention style focused on bodily sensations, muscle tension, breathing, and performance cues; dissociative attention style involved blocking pain through distractions like conversation, or unrelated thoughts. These rating questions were counterbalanced for random order presentation during both baseline and followup run assessments. Following these procedures, participants were offered refreshments (e.g., sports drinks, bananas, granola bars). Reminders regarding return for followup run dates and questionnaires were provided, and participants were thanked for their participation.
Intervention. Participants assigned to the associative group and associative exercise group engaged in a targeted 15minute intervention, held in a designated classroom on campus one to three days following their baseline run and before their followup run session. Attendance was verified to ensure participants’ involvement, and a snack was provided upon checkin. Conducted by the researchers using a prepared presentation, the session focused on teaching the distinctions between associative and dissociative attention styles, offering guidance regarding conditions when each style is most beneficial. As an interactive component for the associative attention style, participants engaged in a 30second wall squat, during which researchers primed them to focus on specific elements, such as breath, ambient sounds in the room, and where their feet were placed. Following this brief exercise, participants engaged in discussions with one another, sharing their experiences and how to incorporate associative attention style into their future exercise routines.
Lastly, participants were encouraged to actively implement associative attention style during their upcoming followup run session later that week. They were informed that their follow up run experience would closely resemble their baseline run, with the primary difference being the emphasis on utilizing the associative attention style. Additionally, participants were informed that a researcher would use the same priming strategy during the followup run, encouraging participants to remember to use the associative attention style. This comprehensive approach, aimed to teach, practice, and plan, aligns with established intervention methodologies (McGonigal, 2015).
Follow-up Run and Survey. One week later, participants returned for their followup run assessments. The protocol closely resembled that of the baseline run, maintaining consistency across the exercise and
Mindful Strides | Johnson, Adair, Johnson, Wright, and Clay
control groups. Researchers for these groups followed the same script, guiding participants through the process with heart rate monitors, a 2.5minute warmup at 2.5 speed, a onemile run, and a 2.5minute cooldown at 2.5 speed. For participants in the associative only and associative exercise groups, researchers utilized a distinct script labeled Associative, incorporating identical instructions as the baseline run. The script included specific phrases aimed at priming participants to engage with the associative attention style during their onemile run. These phrases were timed to occur when participants reached distance points of 0.2, 0.4, 0.6, and 0.8 miles, and reminded participants to focus on specific cues using phrases such as “Focus on your breath.” Following the run, participants from all groups were once again prompted to rate their usage levels of associative and dissociative attention styles on a rating scale 1–7 (1 = rarely, 7 = most of the time), with provided definitions. Finally, debriefing was handled similarly to the baseline run protocol described above, with the addition of an explanation of the purpose and design of the study.
Data Analysis
Data were analyzed in R statistical software (version 2023.03.0) utilizing various statistical methods. First, to examine changes in performance time (H1) among the different conditions, we conducted a series of paired samples t tests for each group to investigate differences in performance time from the baseline run to the followup run. Cohen’s d coefficient estimates were also calculated as a measure of the effect size of
TABLE 1
Between Groups
each condition. To further investigate the efficacy of the associative intervention itself (H1), we grouped the sample according to those who received the intervention and those who did not (associative and associative exercise compared to exercise and control) and conducted independentsamples t tests regarding performance time (H1). Second, following the same procedure outlined for H1, we examined H2 for both associative and dissociative use by using the selfreported ratings given by participants immediately following each run. Third, we conducted three oneway ANOVA tests to determine significant differences along with Cohen’s f effect sizes between the four groups for each of the following variables: calculated difference scores in performance time (H1), associative attentional usage rating (H2a), and dissociative attentional usage rating (H2b). For all three tests, the group acted as a single factor with four levels (exercise, associative, associative exercise, and control). A Type III sum of squares correction was applied to adjust for some imbalance in group sizes. Next, post hoc contrasts were conducted using Fisher’s LSD correction to compare time differences and attentional style differences between each combination of the groups. Finally, we conducted Pearson’s R correlations to more closely examine the strength of the relationship between attentional style usage ratings and performance time (H1). Correlations were conducted between ratings and performance times at both time points as well as difference scores for ratings and performance times between both time points.
Results
The average time across the entire sample for the baseline run was 602.10 seconds (SD = 153.30), or 10 minutes 2 seconds (SD = 2:33). For the followup run, the average time was 580.30 seconds (SD = 154.91), or 9:40 (SD = 2:35), which was, on average, 22 seconds less or faster. Heart rates were combined into an average for each participant at each time point. Across all participants the average heart rate was 173.45 (SD = 18.79) BPM at the baseline and 172.29 (SD = 14.61) BPM at the followup.
Regarding performance time differences (H1), paired sample, t tests uncovered significant time decreases in the groups that received the associative intervention (see Table 1). As highlighted in Figure 1, the associative group showed the largest time improvement at 42.58 seconds (SD = 71.69; p = .002). However, all conditions except the control group showed time improvements, supporting H1 (see Table 1).
In further support of H1, ANOVA revealed significance in the average time difference from baseline to followup across all four conditions F (3, 112)= 3.46; p = .019, Cohen’s f = .30, η2 = .08. Subsequent post hoc
independentsamples t tests between each pairing of the conditions found significant differences in performance time change between the groups, as reported in Table 1. The most significant difference and strongest effect was found when comparing the associativeonly group with the control group (see Table 1).
Regarding intervention efficacy, differences in selfrated attentional style usage between baseline and followup were significant, providing support for H2. Paired t tests found that groups who received the associative intervention significantly increased their usage of associative attentional style and decreased their usage of dissociative attention (see Tables 2 and 3; Figure 2). Changes in selfreported attentional styles were not significant for the exercise group in either associative (p = .880) or dissociative (p = .846) attention, nor for the control group in associative (p = .588) or dissociative (p = .668) attention.. ANOVA found significant differences between the groups in the change in associative style ratings (F = 4.13; p = .008, Cohen’s f = .33, η2 = .10) and change in dissociative style ratings (F = 3.72, p = .01, Cohen’s f = .32, η2 = .09). As displayed in Tables 2 and 3, post hoc independent t tests revealed that comparisons between the groups that received the associative mindset intervention and groups that did not were significant, suggesting support for the intervention’s efficacy (H2) in increasing associative attention and decreasing dissociative attention usage during performance.
In further support of intervention efficacy, independentsamples t tests provided additional support for H1 and H2 by combining the two groups that received the associative intervention and comparing the resulting group to the other grouping of participants who did not receive the intervention. These groups were significantly different from each other from the baseline to the followup run in terms of time difference, associative rating differences, and dissociative rating differences (see Table 4). From our 117 participants, post hoc analyses indicated that we achieved approximately .76 power both for t tests and ANOVA. Finally, Pearson’s r correlations are reported in Table 5 that highlight relationships between performance time and attentional style ratings. The strongest positive relationship was between performance time at the followup timepoint and dissociative ratings (r = .25), suggesting that those who rated themselves as using more dissociative attentional style during the followup run tended to run more slowly during that same run. The strongest negative relationship was between the performance time change and the change in associative ratings, suggesting that those who shifted to utilizing more associative attention during the second run also tended to decrease their time during the second run.
Johnson, Adair, Johnson, Wright, and Clay | Mindful Strides
Discussion
The current study investigated the effectiveness of a brief intervention to facilitate a shift in associative attention style to improve performance among college students. Whereas previous literature has noted the efficacy of associative attention style training interventions among college athletes (Wu et al., 2021), our study specifically targeted average college students using baseline and followup one mile runs with a brief 15minute intervention in the interim to measure performance improvements and attention style changes. Results were
FIGURE 2
Baseline and Follow-Up Associative and Dissociative Ratings by Group
Note. Values reported above show mean (standard deviation) in seconds.
FIGURE 1
Baseline and Follow-Up Performance Time by Group
Note. Values reported above show mean (standard deviation) in seconds.
Mindful Strides | Johnson, Adair, Johnson, Wright, and Clay
promising, demonstrating significant changes to both attentional style and performance among participants in the intervention conditions and suggesting that our intervention facilitated these changes. These findings have important implications for helping average college students with physical exercise as they imply that a simple, timeefficient approach such as the brief intervention we utilized can increase associative attentional usage, which in turn can improve performance.
TABLE 2
Associative Ratings Baseline to Follow-Up:
Note. Mean difference (SDdiff) is reported in rating points. * p < .05. ** p < .01.
TABLE 3
Dissociative Ratings Baseline to Follow-Up:
Note
The principal finding was that participants effectively changed their mindset, which corresponded with significant improvements in their physical performance. Improvements were shown by substantial reductions in running times for those who were trained to use an associative attention style mindset, especially compared to those who did not receive such training.
First, consistent with the observed performance improvements in this study, the effectiveness of the associative attention style in improving physical performance is often thought to be primarily due to increased internal awareness during physical activities (American Psychological Association, 2018). By maintaining an internal focus, participants may have been able to develop a stronger mind body connection, thus enhancing motor control, physiological efficiency, and even mental concentration during physical tasks (Querdasi & Callaghan, 2023). These enhancements were evident in our findings, as participants in the associative groups demonstrated significantly greater time reductions in tandem with increased associative attention style use at their followup runs compared to the control group. This trend suggests that associative attention style may act as a unique catalyst for performance improvement, even among those who do not engage in regular physical activity.
Indeed, our observation that associative attention ratings increased while running times decreasedhighlights the connection between mind and body in physical activity. Such focused engagement not only seems to improve physical performance but can also positively impact mental well being, reducing the feeling of effort and increasing satisfaction with the task (Wei et al., 2017). These psychological benefits are essential for maintaining motivation and enhancing the overall exercise experience (Sun et al., 2024). Our results in conjunction with previous findings imply that adopting an associative attention style may correspond with improved physical performance and numerous psychological benefits, including maintenance of physical activity through key mechanisms of motivation, mood, and wellness. Thus, associative attention style is a powerful tool for both athletic training and general physical activity, providing significant advantages in both performance and possibly psychological wellbeing.
Second, it is noteworthy that when participants were grouped by the additional factor of the exercise program, important differences emerged. For instance, the associativeonly group, compared to the associative and exercise group, exhibited more substantial performance improvements, reducing their average mile time by nearly a full minute. Although we initially expected that the participants who engaged in both the exercise program and the associative intervention (associative exercise group) would see the
most time improvements, those who were not exercising and received the attention style training (associativeonly group) improved the most. This finding may align with a typical pattern observed among regularly exercising individuals, who often experience limited changes in performance times over short periods, such as the oneweek timeframe our participants underwent. This may be due to a ceiling effect, where those already exercising have less room for improvement, leading to a performance plateau and diminishing returns after an extended period of the same exercise (Bouchard & Rankinen, 2021; Clark, 2016; Farrell & Turgeon, n.d.). This trend is particularly evident in running behavior, where tangible improvements typically occur within a timeframe of 4 to 12 weeks (PrietoGonzález & Sedlacek, 2022; Turner et al., 2003), particularly in middledistance races spanning 800 meters to 3,000 meters (Boullosa et al., 2020; Thompson, 2017).
As another explanation for the larger improvements of nonexercisers, regular exercisers may have already developed skills needed to cope with discomfort, channel anxiety to improve performance, and otherwise adapt to challenging exercise settings, that those who do not consistently exercise may have not yet learned or adopted. Although our study did not directly measure participants’ coping mechanisms in these situations, existing research suggests that regular exercisers often cultivate such skills over time (Tod et al., 2011; Van Sprundel, 2022). Thus, an associative attention mindfulness intervention may provide limited benefits for individuals already skilled at coping with exerciserelated stress, possibly indicating a ceiling effect. Conversely, individuals who are not accustomed to regular exercise and have not yet developed coping mechanisms could benefit even more from a mindfulness practice such as associative attention style training. Focusing on the body and the task at hand can help people manage exerciserelated performance anxiety, conserve energy, and boost confidence during and after running sessions (Fish, 2015; Noetel et al., 2019). Indeed, when taught a unique coping skill such as associative attention style, those who do not engage in regular exercise may have the most ability to gain, as they likely began with fewer coping strategies initially (Wang et al., 2023). Thus, the average college student may be able to gain even more than the college athlete in terms of performance through a brief intervention designed to promote and train associative attention techniques.
Third, as expected, participants who were taught the associative attentional style, practiced it, discussed it, and were encouraged to use it during the followup run, seem to have succeeded in doing so. Their success was evidenced by significant increases in the average selfreported associative rating. Concurrent with substantial decreases
Johnson, Adair, Johnson, Wright, and Clay | Mindful Strides
in dissociative usage. The successful attentional shift that these participants demonstrated after receiving even a brief, 15minute intervention suggests that our intervention was effective in facilitating an attentional style shift, and similar brief interventions could have other widespread benefits. This finding aligns with existing literature indicating that mindset changes can significantly influence performance and psychological outcomes. For example, our findings coincide with other studies demonstrating that even short, focused interventions can shift individuals’ mindsets, leading to longterm enhanced health outcomes and behavioral changes (Crum et al., 2017).
Moreover, these findings suggest that associative interventions could be particularly beneficial for those who struggle with regular exercise regimes. By demonstrating more proximal and noticeable benefits, these interventions might encourage sustained engagement, particularly for individuals who typically experience difficulty in maintaining a regular exercise routine, with implications for promoting longterm health and fitness habits (Allen et al., 2021; Brick et al., 2014). Building on the benefits of associative attention mindfulness techniques for those who struggle with regular exercise regimes, personal trainers and physical therapists could incorporate these brief, targeted interventions to help their clients achieve quicker physical results (Rogers et al., 2016). The more immediate and visible benefits from such interventions can be incredibly motivating for clients who may be discouraged by slower conventional
TABLE 4
Note. Baseline and Follow-up in column 1 represent the correlation between performance time at the given time point and attentional style ratings for associative (column 2) and dissociative (column 3), whereas delta represents the difference score (change from baseline to follow-up) for performance time correlated with the difference scores for associative ratings (column 2) and dissociative ratings (column 3).
TABLE 5
Mindful Strides | Johnson, Adair, Johnson, Wright, and Clay
progress. Furthermore, the simplicity and brevity of the brief associative attention intervention could make it an ideal tool for educational environments as well. Physical education teachers and coaches in middle schools and high schools can use this technique to enhance students’ engagement and performance in sports (e.g., running, soccer, basketball). For young athletes new to a sport, learning to focus their attention associatively can improve their performance and possibly enjoyment of the activity. This approach fosters a positive sports experience and promotes the development of longterm health and fitness habits, equipping young individuals with the skills to maintain regular physical activity throughout their lives including while in college (PerisDelcampo et al., 2024).
Potential Limitations and Future Research
Despite the potential benefits highlighted, we acknowledge some potential limitations of this study. First, the sample was predominantly women and White, with an average age of 22 years, suggesting some difficulties with generalizability to more diverse populations, as it may not represent the broader population of college students or those from other age groups and backgrounds. Second, although the study employed a singleblind design where participants were unaware of their group assignments to prevent participant bias, the researchers were aware of these assignments, which may introduce bias, despite efforts to minimize it through standardized scripted interactions by research assistants. Third, we were unable to account for several potential confounding factors that could have affected the results, such as differences in participants’ training loads, types, and motivational levels. For instance, individuals with higher motivation levels may have benefitted more from the intervention, but those with lower motivation levels may have needed more personalized strategies (Jones et al., 2015). Future research should assess these variables using a theory based scale (e.g., self determination theory), before the study and potentially stratifying participants based on motivational factors to evaluate the interaction of motivation types with the intervention. A similar approach to assessing different training loads or types before grouping individuals could also help in understanding how these factors interact with the intervention. For instance, comparing individuals who engage in more aerobic exercises with those who engage in more anaerobic exercises could provide valuable insights. Fourth, the crosssectional nature of the study also presents a limitation, capturing only a snapshot in time without accounting for potential seasonal variations, hormonal fluctuations during the menstrual cycle, or longitudinal changes that might influence performance or the effectiveness of associative interventions
(Janse de Jonge, 2003). Although the short duration of the study was intended to maintain focused participation, future research could benefit from longitudinal designs that evaluate the impacts of associative attention over different seasons, academic terms, or locations. Furthermore, although our findings indicate immediate improvements in performance and associative levels for nonathletes, existing literature, such as Wu et al. (2021), has found that athletes who use associative mindfulness techniques see longterm performance improvements. Investigating whether nonathletes experience similar sustained effects is crucial, especially considering that brief interventions have been shown to have lasting impacts (McGonigal, 2015). Although we have reasons to believe that there could be lasting impacts for nonathletes, further research could use longitudinal designs to clarify whether the immediate benefits observed in this study, including higher associative levels and performance improvements, are sustainable over time in nonathlete populations.
Indeed, future studies should consider exploring the efficacy of associative interventions across different age groups and demographics to assess the universality of these findings. Additionally, replicating this research in more naturalistic settings such as tracks or roads—environments more typical of everyday exercise routines than treadmills—could enhance the ecological validity of the results. Also, further studies should consider individuals’ levels of psychological effort to see if an associative attention style helps with psychological fatigue, which other studies have identified (Wei et al., 2017). Another intriguing avenue would be to examine whether associative techniques could be adapted within academic settings for other health behavior changes (Wright et al., 2020), coping effectively with stressors pertaining to college students (Wright, 2020), or potentially aiding students in better maintaining focus and improving test performance through similar mechanisms. Longitudinal research is essential to determine the longterm effects of brief associative attention style interventions, so future work should aim to track participants over extended periods to ascertain if the initial motivational boosts translate into sustained exercise frequency, increased selfconfidence, and persistent engagement in physical activities over time. These insights could bridge the gap between the acknowledgment of exercise benefits and actual behavioral change, leading to more widespread adoption of regular physical activity practices among the general population. These extended research efforts could fundamentally shift how researchers approach motivation and engagement in both physical and cognitive tasks, offering implications for public health and educational strategies.
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Conclusion
In conclusion, this study highlights the effectiveness of a brief associative attention intervention in enhancing physical performance among average college students. Our findings indicate that even a short 15minute intervention can significantly improve running performance and shifts in attentional focus. The significant decrease in mile times for participants trained in associative attention highlights the practical applicability of this technique beyond just athletic populations. Importantly, these interventions proved especially beneficial for students who do not exercise regularly, suggesting that such strategies can effectively motivate and sustain physical activity in a broader demographic. By demonstrating immediate and noticeable benefits, these interventions might encourage sustained engagement and foster longterm health and fitness habits. Future research should replicate these results in more diverse settings and populations and explore the longterm impacts of brief associative attention interventions on physical and psychological wellbeing. Through such efforts, the mindbody connection can be better leveraged to promote healthier, more active lifestyles among college students and others.
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Author Note
Robert R. Wright https://orcid.org/0000000241017840
This research received internal funding from Brigham Young University–Idaho for studentdirected research. The data used to support the current study’s findings are available from the corresponding author upon reasonable request. We would like to thank (in alphabetical order) Anna Aho, Cade Anderson, Maren Batman, Michael Broqueza, Skyler Brough, Josh Castro, Torie Denkers, Scott Flynn, Madison Gage, Bandon Jones, Josi Kelsey, Eris Koike, Geoffrey Loomis, McKenna Osborne, Andres Rodriguez, Gabrielle Whitmore, and Lavear Whitney for their assistance throughout the study. The authors declare no conflicts of interest.
Correspondence concerning this article should be addressed to Natalie K. Johnson Department of Psychology, Brigham Young University–Idaho, 210 West 4th South Rexburg, ID 834602140, Telephone: 2086078780, Email: natalielinford777@gmail.com
Examining the Associations Among Relationship Obsessive-Compulsive Tendencies, Extreme Love Beliefs, and Big Five Personality Traits in a Nonclinical American Sample
Grace A. Thompson1, Indica R. Machost1, Jeremy Tost*1, Clay King*2, and Alexander C. Olson1
1Department of Social and Behavioral Sciences, Colorado Mesa University
2Department of Mathematics, Stonehill College
ABSTRACT. Relationship obsessive compulsiveness refers to obsessivecompulsive (OC) tendencies that center on an individual’s intimate relationships. This paper focused on the relationshipcentered subtype. An online survey using the Big Five Personality Inventory (BFI 44), Extreme Love Beliefs Scale (EXLS), and Relationship Obsessive Compulsive Inventory (ROCI) was distributed, and 153 nonclinical American participants’ responses were analyzed. The researchers’ primarily aimed to examine associations among relationshipcentered OC tendencies, extreme love beliefs, and the Big Five personality traits. The secondary objective was to test whether extreme love beliefs differed by relationship status. Correlations revealed a negative relationship between Agreeableness and ROCI scores ( r = .37, p = .002), a negative correlation between Conscientiousness and the Relationship Rightness dimension of the ROCI (r = .26, p = .03), and a positive correlation between Neuroticism and the Partner’s Love dimension (r = .28, p = .02). Extraversion demonstrated a positive correlation with Relationship Rightness (r = .29, p = .02), contrary to hypotheses. A regression analysis identified Agreeableness ( B = −0.35, SE = 0.13, t = −2.77, p = .007), Conscientiousness (B = −0.32, SE = 0.13, t = −2.40, p = .02), and Extraversion ( B = 0.32, SE = 0.11, t = 2.83, p = .006) as ROCI predictors, accounting for 23.09% of the variance (adjusted R² = .23). EXLS scores were higher among single participants than those in relationships ( p = .006). Results suggest that OC themes may present independently within individuals, underscoring the need for further investigation into the diversity of OC experiences.
Keywords: relationship obsessivecompulsive disorder, obsessivecompulsive disorder, Big Five personality traits, extreme love beliefs
This article earned Open Science Badges for Open Materials and Open Data. All materials and data are available at https://osf.io/s7nuj
Obsessive compulsive disorder (OCD) is a mental health condition that is characterized by obsessions (repetitive, unwanted thoughts/ images/urges) and compulsions (mental or behavioral acts aimed at reducing the discomfort associated with obsessions; American Psychiatric Association, 2022). In recent years, experts from both the scientific literature and mental healthcare organizations have increasingly proposed that individuals may experience obsessions and compulsions surrounding many aspects of their lives, and that OCD exhibits many subtypes (e.g., Abramowitz & Buchholz, 2020; Doron & Derby, 2017; Hudepohl et al., 2022; International OCD Foundation [IOCDF], n.d. a; OCD Center of Los Angeles, n.d.; Pinciotti et al., 2022). For example, obsessivecompulsive (OC) symptoms may interfere with religious rules, moral precepts, or an individual’s relationship to their concept of God or a higher power (Abramowitz & Buchholz, 2020; Doron & Derby, 2017; IOCDF, n.d.a; OCD Center of Los Angeles, n.d.). OC symptoms may also emerge during pregnancy and following birth, impacting the parent child relationship and childcare procedures (Doron & Derby, 2017; Hudepohl et al., 2022; IOCDF, n.d.a; OCD Center of Los Angeles, n.d.). OC symptoms can also center on an individual’s sexual orientation and gender identity (IOCDF, n.d.a; OCD Center of Los Angeles, n.d.; Pinciotti et al., 2022).
Among the other documented OCD subtypes is relationship obsessivecompulsive disorder (ROCD; Doron & Derby, 2017; IOCDF, n.d. a; OCD Center of Los Angeles, n.d.). ROCD refers to OC symptoms that center on an individual’s romantic partnerships (Doron & Derby, 2017). Much like those with OCD, individuals with ROCD experience intrusive thoughts that cause distress and lead the individual to engage in actions to relieve the associated anxiety. Research has suggested that although compulsions may initially result in stress reduction, over time, their effectiveness diminishes, potentially requiring more complex and timeconsuming compulsions to obtain the relief that the old compulsion initially achieved (Purdon, 2023). ROCD symptoms have been linked with personal distress as well as relationship issues and sexual dissatisfaction (Doron et al., 2012a, 2012b; Doron et al., 2014; Doron et al., 2016; Melli, Bulli, et al., 2018). In Doron et al. (2016), 22 clinical ROCD clients, 22 OCD clients, and 28 community controls were compared on ROCD symptom severity, interference, and maladaptive beliefs. Results revealed that ROCD clients reported more severe ROCD symptoms, as well as more pronounced maladaptive beliefs related to both OCD and relationships when compared to the OCD and control groups. Additionally, ROCD clients exhibited greater depression
symptoms than community controls, emphasizing the unique emotional and relational challenges associated with ROCD (Doron et al., 2016).
A challenge in studying various OC subtypes is balancing the integrity of formal diagnostic procedures with the exploration of emerging dimensions. To avoid misuse of the term disorder and to reflect the reality that the current work stems from a nonclinical sample, the authors of the present study opt to refer to ROCD as relationship OC tendencies or relationship obsessivecompulsiveness1
Relationship obsessivecompulsiveness has been separated into two primary subtypes: partnerfocused and relationshipcentered. Partnerfocused obsessivecompulsiveness concentrates on various aspects of one’s romantic partner—their physical appearance, sociability, morality, emotional stability, intelligence, and competence (Doron et al., 2012b). For example, an individual experiencing partnerfocused OC tendencies might become preoccupied with whether their partner is socially competent enough and feel compelled to “check and recheck” by internally analyzing interactions between their partner and others. Other OC tendencies may center on their partner’s perceived physical flaws, whether they are intelligent enough, moral enough, or equipped to succeed in life (Doron et al., 2012b). Relationship centered obsessive compulsiveness revolves around the nature of the relationship, and its tendencies fall into three relational dimensions: (a) the “rightness” of the relationship, (b) one’s love for their partner, or (c) their partner’s love for them (Doron et al., 2012a). For example, individuals with obsessive tendencies surrounding the “rightness” of their relationship might worry excessively that their relationship does not feel “like a romance should.” A common result is neutralizing behavior, such as seeking reassurance from others that one appears to be in the “right” relationship (Melli, Bulli, et al., 2018). Another common compulsion is an exaggerated tendency to monitor one’s internal state— “measuring” or “checking” one’s feelings of attraction or love for one’s partner (Melli, Bulli, et al., 2018). This study concentrated on the relationshipcentered subtype of relationship obsessivecompulsiveness.
Personality and Relationship
Obsessive-Compulsiveness
Personality assessment has a longstanding history in psychology, and research has demonstrated its importance in effective mental health treatment (Samuels et al., 2020). The Big Five is a model of personality
1ROCD has yet to be officially recognized by an authoritative organization (such as the American Psychiatric Association or World Health Organization).
consisting of five factors: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. These factors are commonly referred to by the acronyms OCEAN or CANOE. As explained by McCrae and Costa (1997), individuals who score high in measures of openness are eager to experience new things. Those high in conscientiousness are goaloriented, structured, and systematic. High extraversion corresponds with elevated levels of social energy and excitability. High levels of agreeableness are reflective of kind, trusting, or altruistic tendencies. High neuroticism is associated with irritability, emotional fluctuation, and moodiness (McCrae & Costa, 1997).
The relationships between the Big Five traits and OCD symptoms are complex. Ok and Gören (2018) observed that specific OCD dimensions exhibit distinct relationships with various Big Five personality traits. They found that conscientiousness exhibited significant negative correlations with the OCD dimensions of dysmorphic thoughts and sexual obsessions and significant positive correlations with the symptom dimensions of contamination, symmetry, morality, and checking. Overall, conscientiousness demonstrated a significant positive relationship with total OCD scores. Neuroticism was found to positively correlate with OCD symptoms (Ok & Gören, 2018). Furnham et al. (2013) also found significant positive correlations between OCD and neuroticism and conscientiousness. OCD symptoms were also found to negatively correlate with extraversion and positively correlate with agreeableness (Furnham et al., 2013).
The connection between relationship obsessivecompulsiveness and personality has remained largely unexamined. To our knowledge, research has yet to explore the connection between the Big Five traits and relationshipcentered OC tendencies in an American sample. Consistent with the preceding OCD research (Furnham et al., 2013; Ok & Gören, 2018), we hypothesize that relationship centered OC tendencies will demonstrate significant positive relationships with the Big Five traits of conscientiousness, agreeableness, and neuroticism, and a significant negative relationship with extraversion. We anticipate that openness will not significantly relate to relationshipcentered OC tendencies as reflected in the literature (Furnham et al., 2013; Ok & Gören, 2018).2
From a theoretical standpoint, the correlational directions among the Big Five traits and relationshipcentered obsessive compulsiveness make sense. Perfectionistic beliefs and behaviors are associated
2 Findings from two recent studies (Rezaei et al., 2023; Zhang & Takahashi, 2024) would have informed our hypotheses had they been published before the culmination of our work. Their results are incorporated into the Discussion section.
with OC tendencies, and individuals with OCD often excessively repeat compulsions in an attempt to achieve a “just right feeling” that the compulsion was completed properly (Purdon, 2023). Additionally, individuals with relationshipcentered obsessivecompulsiveness often meticulously monitor fluctuations in their internal states of attraction or love toward a partner (Melli, Bulli, et al., 2018), highlighting how the precision and detailorientation characteristic of conscientiousness may be a personality predisposition for this type of hypervigilance. Therefore, high conscientiousness was anticipated in individuals with relationship obsessivecompulsiveness.
As discussed by Purdon (2023), obsessions often become enmeshed with an individual’s moral system, with thoughts that contradict values being particularly distressing. Valueinconsistent obsessions are frequently interpreted as warnings or precursors to a loss of control, intensifying fears of acting against one’s principles. Consequently, compulsions are erroneously perceived as the sole means of preventing these feared actions, reinforcing their necessity and perpetuating the OC cycle (Purdon, 2023). Doron and Derby (2017) similarly maintain that relationship OC tendencies frequently demonstrate a moral component and are often egodystonic (Doron & Derby, 2017)—meaning that the individual’s intrusive thoughts contradict their values and, therefore, create emotional discomfort. Morality has been implicated as a facet of agreeableness (Crowe et al., 2018), supporting the logic that those high in agreeableness are likely predisposed to moral concerns. Therefore, a positive association between relationship OC tendencies and agreeableness was expected.
As an indicator of emotional stability, neuroticism has been implicated in many mental health conditions, as supported by a meta analysis of 59 longitudinal/ prospective studies (Jeronimus et al., 2016). Thus, high neuroticism is an expected trait among those who experience a high prevalence of relationship OC tendencies.
Correlation alone cannot establish causation, nor can it distinguish whether one variable results from or simply associates with another due to an external, unmeasured factor. Thus, the interplay between experience, relationship OC tendencies, and personality cannot establish causality or clarify mediating variables. However, experience is likely a contributing factor in the developmental probability of relationship obsessivecompulsiveness.
Cognitive factors such as internal scripts for romantic behavior and relationship expectations are likely to be shaped through experience and, in turn, possibly contribute to relationship OC tendencies. Apostolou and Tsangari (2022) found that among participants from Greece and the Republic of Cyprus,
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extraversion was a significant predictor of involuntary singlehood. Individuals with lower extraversion were more likely to be single not by choice, and to experience prolonged periods of singlehood compared to those with higher extraversion. Their findings indicated substantial differences in involuntary singlehood based on an individual’s level of extraversion (Apostolou & Tsangari, 2022). Therefore, extraversion was expected to negatively correlate with relationship centered obsessivecompulsiveness, an association potentially mediated by an individual’s beliefs about love—a cognitive component likely shaped by romantic experiences and thought to contribute to relationship OC tendencies.
The Cognitive Component of OCD
The cognitive behavioral model of OCD remains a prominent model within the field (Purdon, 2023).
According to a history of the model by Purdon (2023), clinician J. Rachman (1934–2021) was an influential developmental figure. Rachman posited that OCD is maintained by a strong cognitive component, and he continually advocated for treatment types that included a cognitive focus. His research demonstrated that most individuals experience obsession like thoughts, the difference between individuals with and without the disorder being (a) the individual’s ability to dismiss such intrusions and (b) the manner in which the individual appraises such intrusions. Researchers have documented a series of beliefs and appraisals associated with OCD symptoms that directly relate to compulsive behavior (i.e., heightened responsibility, thoughtaction fusion, perfectionism, expectation of complete mental control, threat overestimation, and uncertainty intolerance; Haciomeroglu, 2020; Lopatka & Rachman, 1995; Parrish & Radomsky, 2006; Purdon, 2023; Wheaton et al., 2010). Additional research has demonstrated that these beliefs can change through successful treatment (Radomsky et al., 2020; Wheaton et al., 2010)—thereby reaffirming the importance of the cognitive facet of OCD.
In the same manner, researchers have set forth to establish whether relationship obsessivecompulsiveness also demonstrates an underlying cognitive component. Doron and Derby (2017) have proposed a set of beliefs about love, termed “extreme love beliefs,” that they suggest are particularly important in the scientific understanding of the development and maintenance of relationship obsessivecompulsiveness. Extreme love beliefs are unrealistic standards for love and exaggerated expectations for the internal experience of love. These beliefs are drastic and often unattainable. Therefore, normal relational experiences become distressing to those who hold such beliefs as natural interactions are perceived as problematic (Doron & Derby, 2017).
The Extreme Love Beliefs Scale (EXLS) was created to assess an individual’s extreme love beliefs (Doron & Derby, 2017). To our knowledge, few studies have utilized this inventory. However, Doron and Derby (2017) have reported that their ongoing research has indicated that relationship OC tendencies positively correlate with extreme love beliefs.
Underlying romantic expectations can lead to perfectionistic standards that are difficult or impossible to meet and overgeneralizations about a partner’s behaviors (Kracht & Powell, 2021). Unrealistic expectations can lead to avoidance of problemsolving within the relationship and resistance to professional help (Hefner et al., 2017; Kracht & Powell, 2021). These expectations have been found to negatively correlate with willingness to improve a relationship (Eidelson & Epstein, 1982) and to decrease relationship satisfaction (Swensen & Trahaug, 1985). However, increased commitment to a relationship (e.g., marriage) has been associated with an increase in relationship satisfaction, and longer partnerships are indicative of more realistic romantic expectations (Swensen & Trahaug, 1985). Stable markers of longterm relationships, such as effective communication and the development of conflict resolution strategies, have been cited as contributing factors to longterm marriages (more than ten years; Herlambang, 2024). Time and effort, regarding romantic relationships, appear to be crucial in helping individuals reconcile unrealistic expectations with their lived experience.
Clinicians report that relationship obsessivecompulsiveness is not solely restricted to individuals within relationships—in fact, OC preoccupations may center on previous relationships or prevent individuals from pursuing romantic partnerships (IOCDF, n.d.b). Additionally, some individuals report that their OC tendencies emerge following major romantic decisions, such as getting married or having children (IOCDF, n.d.b). Perhaps romantic changes, such as entering a new relationship or dissolving an existing relationship, may temporarily alter OC experiences. Although the current work did not compare relationshipcentered OC tendencies across different relationship statuses (i.e., single, married) due to study design, we did examine whether extreme love beliefs varied among them. To our knowledge, no published literature has examined whether extreme love beliefs differ by relationship status.
Focus and Scope of the Current Study
Using previously established measures for relationshipcentered OC tendencies (Relationship Obsessive Compulsive Inventory; ROCI), extreme love beliefs (Extreme Love Beliefs Scale; EXLS), and personality (Big Five Personality Inventory; BFI44), the primary
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research question of the current work was as follows: Are there any associations among relationshipcentered OC tendencies, extreme love beliefs, and the Big Five personality traits?
Hypothesis:
1. As informed by the literature (Furnham et al., 2013; Ok & Gören, 2018), we hypothesized that relationshipcentered OC tendencies and extreme love beliefs would demonstrate significant positive correlations with the Big Five factors of conscientiousness, neuroticism, and agreeableness, a significant negative relationship with extraversion, and a nonsignificant relationship with openness.
2. We hypothesized that extreme love beliefs would be strongly associated with relationshipcentered OC tendencies.
Secondary question: Do extreme love beliefs differ depending on relationship status? (Single, married, etc.)
Hypothesis:
1. We hypothesized that extreme love beliefs would be lower among married individuals and individuals in a romantic relationship compared with single individuals. As indicated by Swensen and Trahaug (1985), increased relationship commitment is associated with positive relationship outcomes, as longer partnerships are associated with more realistic romantic expectations.
Method
Participants
Out of a total of 153 participants, the age range was 18 to 86 years (M = 32.05, SD = 17.40). Additionally, 69.93% were assigned female at birth, and 30.07% were assigned male. In terms of gender identity, 68.63% identified as a woman, 29.41% identified as a man, 0.65% identified as nonbinary, and 1.31% selfdescribed. Regarding sexual orientation, 75.16% of our sample were heterosexual, 3.27% gay/lesbian, 16.34% bisexual, 1.96% asexual, and 3.27% were unspecified. The racial breakdown of our sample was 80.39% White/European American, 7.84% Hispanic, 5.23% Asian American/Pacific Islander, 0.65% Black/African American, 0.65% American Indian or Alaskan Native, and 5.23% multiple ethnicities/other. Out of the total sample, 55.56% indicated they were currently in an exclusive romantic relationship, and 44.44% were not.
Materials
The study took place at a midsized western university in the United States and made use of various social
media platforms for means of recruitment. Participants were directed to an anonymous online survey administered through the Qualtrics platform. They were first presented with various demographic items, then the EXLS and BFI44 inventories, and finally, two questions regarding their familiarity with relationship obsessivecompulsiveness and the Big Five (these last two items were not used in the current research due to problematic item wording). An alternate form of the survey (that included additional demographic items and the ROCI) was administered to those in a romantic relationship. All measures were selfreported.
The Relationship Obsessive Compulsive Inventory (ROCI)
The ROCI assesses the severity of relationshipcentered OC tendencies on three relational dimensions (the “rightness” of the relationship, one’s feelings toward the partner, and the partner’s feelings toward oneself; Doron et al., 2012a). The items are a collection of statements that participants rated from 0–4 (“Not at all” to “Very Much”). The statements are written in the present tense and pertain to current relationship experiences. As such, we only administered the inventory to individuals who reported that they were in a romantic relationship at the time of data collection. The ROCI is a 12item psychometrically validated inventory, and previous research has documented its internal consistency (Doron et al., 2012a). Some versions of the inventory (e.g., Doron & Derby, n.d.b; Melli, Carraresi, et al., 2018) contain two additional items (items 2 and 8) that check for response sets/identical ratings. These two items do not measure relationshipcentered OC tendencies according to the three relational dimensions and were excluded from total score calculations.
A total composite score was created from the sum of all inventory items except items 2 and 8 (ROCITotal). The inventory’s internal consistency for the ROCITotal was sufficient (Cronbach’s α = .88). A composite score for the Relationship Rightness dimension was created by summing items 3, 5, 9, and 12 (Cronbach’s α = .78). A composite score for the Love for Partner dimension was created by summing items 1, 7, 10, and 14 (Cronbach’s α = .75). A composite score for the Partner’s Love dimension was created by summing items 4, 6, 11, and 13 (Cronbach’s α = .81). Doron et al. (2012a) reported Cronbach’s α = .93 for the total composite score, and the reliability coefficients for the three relational dimensions were as follows: Relationship Rightness α = .89, Love for Partner α = .84, and Partner’s Love α = .87. The version of the inventory utilized in the current research may be obtained from Doron and Derby’s website, ROCD.net (Doron & Derby, n.d.b). Relationship Obsessive-Compulsiveness And Personality | Thompson, Machost, Tost, King, and Olson
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The Big Five Inventory (BFI-44)
The BFI44 (Fetzer Institute, n.d.) is psychometrically validated (John et al., 1991) and utilizes a 5point Likert scale. Composite scores for the five subscales were created by summing the items within each subscale. For the current research, the BFI 44 demonstrated sufficient internal consistency (Cronbach’s α = .86 for Extraversion, α = .75 for Agreeableness, α = .72 for Conscientiousness, α = .83 for Neuroticism, α = .70 for Openness).
The Extreme Love Beliefs Scale (EXLS)
All participants were presented with the EXLS—a tenitem, 7point Likert scale inventory. The EXLS measures levels of extreme love beliefs and is in the process of psychometric validation (Doron & Derby, 2017). The inventory was obtained from Doron and Derby’s website, ROCD.net (Doron & Derby, n.d.a). Through intercorrelation of the inventory’s items, we found that item 6 did not sufficiently correlate with the other items, and thus, we excluded it from our analyses. In the current research, the EXLS demonstrated sufficient internal consistency (Cronbach’s α = .79). A composite score for each participant was created from the sum of all EXLS items, excluding item 6.
Procedure
The current study was approved by the Institutional Review Board (IRB) of Colorado Mesa University prior to data collection. This study upheld the ethical standards governing research with human subjects, and informed consent and debriefing procedures were employed. At the time of data collection and analysis for this study, there was an absence of prior literature examining the relationships among the Big Five personality traits and relationshipcentered OC tendencies. Consequently, we did not have adequate guidance for estimating effect sizes, and a formal power analysis was not conducted. Instead, the sample size was determined based on practical resource constraints. Additionally, because the ROCI was only administered to participants who were in a romantic relationship, the statistical analyses involving this construct were based on a small sample size. We acknowledge this as a study limitation. Data were collected from March 2 to March 24, 2023. Participants were recruited through convenience and snowball sampling methods. The recruitment process was conducted through Facebook, Snapchat, TikTok, and Instagram posts, through an extra credit opportunity for select classes at a Western university, through posters, and through word of mouth. From an initial sample size of 175, we analyzed 153 valid responses. Our analyses did not include 20 responses due to incomplete status,
participation outside of the United States, required age range of 18 and above, or indication of response sets. An additional response was removed as the participant had preexisting knowledge of the research that impacted response validity. Another participant was removed as they were an extreme outlier on several measures and inclusion of their score changed results. Correlation analyses were used to assess the relationships among BFI44, EXLS, and ROCI scores. A regression analysis was performed to predict ROCI scores and tests of group differences were conducted to determine whether EXLS scores differed by relationship status.
Results
Correlations Among Constructs
To examine the relationships among personality traits, relationshipcentered OC tendencies, and extreme love beliefs, Pearson’s r correlations were calculated. Due to some participants opting not to answer all items of our survey, certain analyses demonstrated differing degrees of freedom. As can be seen in Table 1, a significant negative relationship at the .01 level was observed between Agreeableness and ROCI Total scores, r (66) = .37, p = .002. Of the three relational dimensions of the ROCI (Relationship Rightness, Love for Partner, and Partner’s Love), only Relationship Rightness and Partner’s Love exhibited significant negative relationships with Agreeableness.
A nonsignificant negative relationship was found between conscientiousness and ROCI Total scores, r (65) = .24, p = .05. The relational dimensions also
TABLE 1
Intercorrelation of Relationship-Centered OC Symptoms, Extreme Love Beliefs, and Big Five Personality Traits With Means and Standard Deviations
.001. Cronbach’s alphas reported in bold on the diagonal. Relationship Obsessive Compulsive Inventory total score calculation (ROCI-Total) was created by summing all inventory items (except 2 and 8) and represents a total score for relationship-centered OC symptoms over the three relational dimensions (Relationship Rightness, Love for Partner, Partner’s Love).
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exhibited negative relationships with conscientiousness, but only Relationship Rightness was significant (at the .05 level), r(65) = .26, p = .03.
Neuroticism demonstrated a nonsignificant positive relationship with ROCITotal scores. The relational dimensions similarly exhibited positive correlations, but only Partner’s Love was significant (at the .05 level), r(66) = .28, p = .02.
A nonsignificant positive relationship was observed between extraversion and ROCITotal scores. Only the relational dimension of Relationship Rightness exhibited significance at the .05 level, r(66) = .29, p = .02.
No significant correlations were found between openness and the ROCI. No significant correlations were found between EXLS scores and the Big Five. The EXLS did not significantly correlate with the ROCI. For all correlation values, reference Table 1.
Regarding the hypothesized variables for this study, only agreeableness was significantly negatively correlated with ROCITotal scores. Conscientiousness was significantly negatively correlated with the Relationship Rightness dimension, and neuroticism was significantly negatively correlated with the Partner’s Love dimension. Neither were significantly correlated to the ROCITotal scores. Extraversion was significantly positively related to the Relationship Rightness dimension, but not the ROCITotal scores. There were no significant correlations between openness and the ROCI.
Regression Analysis
To predict ROCI scores, we conducted a regression analysis using an allsubsets approach that tested models with every possible combination of one or more regressors. The best model was determined as the one with the highest adjusted R2 value. The full set of independent variables we assessed was as follows: EXLS, openness, conscientiousness, extraversion, agreeableness, neuroticism, marital status (whether or not the individual was married), biological sex (male/female), age, sexual orientation (heterosexual compared to nonheterosexual—we did not have enough participants for more groups), and race (White compared to nonWhite—we did not have adequate representation for multiple groups). The final model was statistically significant and utilized four predictor variables (openness, conscientiousness, extraversion, and agreeableness), F(4, 62) = 5.95, p < .001. The model explained 23.09% of the variance in ROCITotal scores (adjusted R2 = .23). Openness was not a significant predictor (B = .16, SE = .12, t = 1.31, p = .20). conscientiousness was a significant negative predictor (B = −.32, SE = .13, t = −2.40, p = .02), indicating that higher conscientiousness scores were associated with lower ROCI Total
scores. Extraversion was a significant positive predictor (B = .32, SE = .11, t = 2.83, p = .006), demonstrating that higher extraversion scores were associated with higher ROCITotal scores. Agreeableness was a significant negative predictor (B = −.35, SE = .13, t = −2.77, p = .007), indicating that higher agreeableness scores were associated with lower ROCITotal scores.
Group Comparisons
To assess potential differences in extreme love beliefs between different relationship statuses (i.e., single, married), we conducted two independent t tests. Again, due to some participants opting not to answer all survey items, certain analyses demonstrated differing degrees of freedom. Utilizing a Bonferroni adjustment for two comparisons at the .05 significance level leaves a .025 significance level per test. At this level of significance, EXLS scores were significantly higher for individuals not in a romantic relationship ( n = 59, M = 27.20, SD = 9.10) compared to those who were (n = 72, M = 23.14, SD = 7.40), t(129) = 2.82, onetailed p = .006, R2 = .06. We also compared the EXLS scores of married individuals to those in a relationship but not married. Despite the married group containing 27 individuals, we proceeded with the analysis as their responses demonstrated normality (ShapiroWilk p = .25). At the .025 level of significance, there was not enough evidence to conclude that EXLS scores differ between individuals who are in a relationship but not married (n = 45, M = 22.73, SD = 7.26) and those who are married ( n = 27, M = 23.81, SD = 7.73), t(70) = 0.60, p = .55, R2 = .01.
Discussion
Correlations Among Constructs
We anticipated that relationshipcentered OC tendencies would demonstrate significant positive correlations with the Big Five factors of conscientiousness, neuroticism, and agreeableness, a significant negative correlation with extraversion, and a nonsignificant relationship with openness. Our findings only partially confirmed our hypothesis. As expected, we found that relationshipcentered OC tendencies demonstrated a nonsignificant relationship with openness. However, extraversion was not found to have a significant correlation with relationshipcentered OC tendencies overall but did have a significant positive correlation with relationship rightness. Additionally, relationship centered OC tendencies were found to be significantly negatively correlated with agreeableness, relationship rightness was found to be significantly negatively correlated with conscientiousness, and partner’s love was found to be significantly negatively correlated with neuroticism. Relationship obsessivecompulsiveness has been
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described as a “theme” of obsession (Doron et al., 2013; Rezaei et al., 2023) or a “dimension” of OCD (Melli, Bulli, et al., 2018). As such, we expected that relationship obsessivecompulsiveness would have a similar association with the Big Five personality traits as OCD. However, we found that relationshipcentered OC tendencies and the Big Five exhibited correlations that were very dissimilar from previous studies on OCD and personality.
A recent study from Iran (Rezaei et al., 2023) connected relationship obsessivecompulsiveness to the Big Five traits, finding that relationshipcentered OC tendencies demonstrated significant negative correlations with extraversion, agreeableness, and conscientiousness, and a significant positive correlation with neuroticism. We propose the following rationale as one possible explanation for the difference: As personality factors are thought to distinguish between individuals, a significant difference in these factors may suggest that relationship obsessivecompulsiveness can be found independently—without the existence of other obsessional themes—in some individuals, a sentiment supported by some clinicians (IOCDF, n.d.b). This means that, at a given time, some individuals may only experience obsessions and compulsions surrounding their relationship and not in other areas of their lives. Significant differences in personality factors between OCD and relationship obsessive compulsiveness may have practical implications in the detection and mitigation of each, a sentiment that has been echoed in other studies (Tinella et al., 2023). Furthermore, studies have shown that different personality factors, including the Big Five traits, impact the effectiveness of treatment types, including Serotonin Reuptake Inhibitor (SRI) and Selective Serotonin Reuptake Inhibitor (SSRI) treatment response and cognitive behavioral therapy response for OCD (Samuels et al., 2020). Taken together, this implies that clinicians should consider the personality differences between OCD and relationship obsessivecompulsiveness when regarding each. Future research is encouraged to examine other distinguishing traits between OCD and relationship obsessivecompulsiveness and to explore the prevalence of independent OC theme manifestations. The potential of independent manifestations should also be explored for other OC themes, such as those related to perinatal concerns, sexual orientation and gender, and religion/ spirituality (Abramowitz & Buchholz, 2020; Hudepohl et al., 2022; Pinciotti et al., 2022). This future research may inform whether relationship obsessivecompulsiveness and other OC themes should be included in a later version of the DSM.
The negative correlations between relationship
centered OC tendencies and agreeableness, as well as relationship rightness’s negative correlation with conscientiousness, could additionally be explained by relationship satisfaction findings (Holland & Roisman, 2008; Karney & Bradbury, 1997; Watson et al., 2001). High conscientiousness was found to be predictive of high relationship quality (Holland & Roisman, 2008; Karney & Bradbury, 1997), and agreeableness consistently predicted relationship satisfaction among dating couples (Watson et al., 2001). In contrast, relationship obsessivecompulsiveness has been associated with poor relational quality (Doron et al., 2012a, 2012b; Doron et al., 2014). Based on these findings, relationshipcentered obsessive compulsive tendencies were expected to negatively correlate with agreeableness, and relationship rightness was expected to negatively correlate with conscientiousness, which is what the current work found. Recent research (Rezaei et al., 2023) similarly explained negative correlations between relationship OC tendencies, agreeableness, and conscientiousness by this relationship satisfaction rationale. Published after the culmination of our study, Rezaei et al. (2023) examined the associations among relationship OC tendencies and the Big Five personality traits in an Iranian sample. They found significant negative correlations between relationshipcentered OC tendencies and extraversion, agreeableness, and conscientiousness, and a significant positive correlation between relationship centered OC tendencies and neuroticism. Similarly, we found a significant negative correlation between relationshipcentered OC tendencies and agreeableness, a significant negative correlation between relationship rightness and conscientiousness, and a significant positive correlation between partner’s love and neuroticism. However, we found a positive relationship between relationship rightness and extraversion rather than a negative association. Regarding the correlational differences between the current work and Rezeai et al. (2023), differences in sample size between the two studies, as well as cultural differences in personality, may account for the discrepancies in the findings.
Our finding that extreme love beliefs did not significantly correlate with relationshipcentered OC tendencies or any of the Big Five personality traits was unexpected. As a proposed mechanism in the development and maintenance of relationship obsessivecompulsiveness, extreme love beliefs were anticipated to significantly correlate with relationshipcentered OC tendencies and mirror the directionality of correlations among relationshipcentered OC tendencies and Big Five traits. A significant limitation of the current study was that it did not control for level of distress/life interference of relationship OC tendencies. The interplay
between extreme love beliefs and relationship obsessivecompulsiveness likely differs between individuals who experience relationship OC tendencies at a severe level versus those who experience these obsessions and compulsions minimally.3 As maintained by Rachman, obsessionlike thoughts are a shared experience among both individuals with OCD and those without—the key distinction in individuals with OCD likely being their diminished ability to dismiss such intrusions and how they appraise the significance of thoughts (Purdon, 2023). Perhaps extreme love beliefs are similar in their association with relationship obsessivecompulsiveness, where the distinction between individuals with distressing tendencies and those with negligible ones lies in how these beliefs are interpreted. These previous considerations remain a source of inspiration for future scientific inquiry.
Regression Analysis
Differences between the regression model and simple correlations complicate conclusions regarding how variables relate to relationship obsessivecompulsiveness. conscientiousness exhibited a significant negative correlation with the Relationship Rightness dimension of relationshipcentered OC tendencies. This negative association was supported by conscientiousness emerging as a significant negative predictor in the regression model. The negative relationship between agreeableness and relationship centered OC tendencies was consistent across both analyses. Similarly, openness was nonsignificant in both the correlation analysis and the regression model, yet the personality trait’s inclusion in the final model suggests its contribution to the predictive accuracy of ROCI scores.
Neuroticism exhibited a significant positive correlation with the Partner’s Love dimension of relationshipcentered OC tendencies, a weaker than expected association given the trait’s positive relationship to OCD (Ok & Gören, 2018; Zhang & Takahashi, 2024). Neuroticism was not included in the final regression model, suggesting that although it relates to the partner’s love dimension of relationship centered OC tendencies, its unique contribution to prediction is diminished when other personality traits are considered. Extraversion, which demonstrated a significant positive correlation with relationship rightness, emerged as a significant positive predictor of ROCI scores in the regression model. This result highlights the importance of considering Extraversion in the broader context of other personality traits, suggesting its role in relationshipcentered OC 3This is akin to the practice of comparing clinical versus nonclinical participants; however, the classification of a clinical sample is complicated by the lack of a formal diagnostic procedure for relationship obsessivecompulsiveness.
tendencies becomes more pronounced when evaluated holistically rather than in isolation.
Overall, these findings illustrate the complexity of identifying the important factors associated with relationship obsessivecompulsiveness. Our final regression model explained a meaningful portion of the variance of ROCI scores, supporting the idea that several personality traits—most notably conscientiousness, agreeableness, extraversion, and openness—are closely related to this mental health experience. The differences in results between the correlation analysis and regression model highlight the importance of considering personality traits in context instead of individually.
Multiple linear regression accounts for the overlap in variance among predictors (multicollinearity), which could be a possible explanation for the discrepancy between the correlation and linear regression analyses. The regression analysis, therefore, accounted for each predictor’s unique contribution to relationshipcentered obsessive compulsiveness. Multicollinearity is a particularly relevant possibility when interpreting neuroticism’s exclusion from the regression model despite its significant correlation with the Partner’s Love dimension. neuroticism likely shares variance with other predictors, rendering its unique predictive power negligible.
Group Comparisons
Individuals not in an exclusive romantic relationship exhibited significantly higher levels of extreme love beliefs compared to those in an exclusive romantic relationship. However, the effect size (R2 = .06) indicated that this difference was small and, therefore, there was not a drastic difference between those in a relationship and those not. Among those in a relationship, extreme love beliefs were not significantly different between married and unmarried individuals. These results indicated that extreme love beliefs may be partially accounted for by whether the individual is in a relationship, but the transition from relationship to marriage is nonsignificant. We propose three possible explanations for these findings: (a) Perhaps individuals with higher levels of extreme love beliefs are reluctant to commit to relationships as their expectations for romance are too extreme to be fulfilled; (b) Perhaps potential partners avoid relationships with those who exhibit high extreme love beliefs (thereby decreasing dating opportunities); (c) Perhaps romantic experiences reshape and temper an individual’s romantic expectations.
Many factors likely contribute to the development of an individual’s beliefs about, expectations for, and behaviors within a romantic relationship. Two possibilities of such factors are religious affiliation and parental
FALL 2025
Thompson, Machost, Tost, King, and Olson | Relationship
modeling. Wright et al. (2024) maintain that religious involvement strongly influences many behaviors central to dating and relationships, specifically noting its importance in conservative Christian faiths as a contributor toward marriage. Additionally, Cowan & Cowan (2005) assert that parental romantic behavior influences an individual’s romantic expectations and beliefs throughout adulthood. Based on these findings, religious affiliation and parental modeling are likely to impact an individual’s romantic expectations and beliefs. Future research is encouraged to examine whether religious affiliation and early parental modeling could predispose individuals to extreme love beliefs, heightened personal importance placed on relationship status, or obsessivecompulsive experiences.
Romantic expectations and beliefs are also likely influenced by media consumption. The notions of “happily ever after,” “soulmates,” and “loveatfirst sight” are common tropes depicted in social media, romances, and even children’s media, although some studios have begun to move away from these themes in recent years (Hefner et al., 2017). Future research is encouraged to explore the influences of media consumption on extreme love beliefs and relationship OC tendencies.
Limitations and Future Directions
The current research exhibits notable limitations, particularly related to recruitment methods. Random sampling techniques were not employed and reliance on social media platforms for recruitment may have influenced results. Wright et al. (2021) found an association between the type of social media platform used and an individual’s social and physical health profile. Specifically, imagebased social media platforms like Instagram and Snapchat (both utilized in our recruitment process) are associated with poorer mental health outcomes compared to videobased like Marco Polo or professional platforms like LinkedIn (Wright et al., 2021). This raises the possibility that relationshipcentered OC tendencies were inflated in our study simply due to the nature of participant recruitment.
Regarding our sample’s married population, we did not survey marital satisfaction, marriage length, partner gender, or other factors that could attest to the quality of the marriages. Therefore, our limited descriptive ability, along with the small sample size for this group, restricts our ability to generalize the findings.
Additionally, as a result of our sample being undersized for a study of this type, our sample was not demographically representative. University students were overrepresented, and groups such as Black/ African American individuals and members of the LGBTQ community were underrepresented. This
lack of diversity limits our study’s generalizability and application. Additionally, a larger sample size might have revealed stronger correlations by providing greater statistical power and reducing variability. Furthermore, selfselection bias is likely to be particularly problematic in personality research. Due to the voluntary nature of our study, certain Big Five traits may have been artificially inflated, and it is possible that those who did not participate differed in personality composition from those who did.
Despite the preceding limitations, our study was one of the first to assess the connection between relationshipcentered OC tendencies and the Big Five traits. Additionally, we only administered the ROCI to individuals who reported that they were currently in an exclusive romantic relationship; we believe this increases the accuracy of reported relationship centered OC tendencies as individuals did not have to rely on their memory of past relationships (as was the case for some previous studies). These two factors help to distinguish our work.
In addition to the suggestions previously discussed, we recommend that future work examine the relationships between the Big Five traits and the other romantic relationship OC subtype, partner focused. To our knowledge, no published research has examined the connection between personality and partnerfocused OC tendencies among American participants. If the association between relationship obsessivecompulsiveness and extreme love beliefs is established, we advise that future work replicate ours to indicate if there is a true difference in extreme love beliefs between those in a relationship and those not in a relationship.
Rezaei et al. (2023) also examined the effects of other factors like that of attachment style and thoughtaction fusion in their work in Iran. We suggest that future research be mindful of cultural differences, explore other potential moderating factors such as cultural background or selfesteem, and further examine how relationship obsessivecompulsiveness differs from other obsessivecompulsive themes
Conclusion
Relationshipcentered OC tendencies appear to relate differently to Big Five traits than OCD symptoms—most notably, the traits of conscientiousness and agreeableness positively correlate with OCD symptoms (Ok & Gören, 2018; Zhang & Takahashi, 2024) but negatively correlate with certain dimensions of relationshipcentered OC tendencies. The negative correlations between relationshipcentered OC tendencies and agreeableness, as well as certain dimensions of relationshipcentered OC tendencies and conscientiousness, align with
Relationship Obsessive-Compulsiveness And Personality | Thompson, Machost, Tost, King, and Olson
recent research that connected the Big Five traits with relationship OC tendencies (Rezaei et al., 2023). Our results indicate that extreme love beliefs are lower among individuals in a romantic relationship compared to those not in a relationship. However, extreme love beliefs were not found to correlate with either the Big Five factors or relationshipcentered OC tendencies—warranting further research to establish the construct and uncover its connection to relationship obsessivecompulsiveness.
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Author Note
Grace A. Thompson https://orcid.org/0009000604249737
Indica R. Machost https://orcid.org/0009000403731519
Clay King is now at the Department of Mathematics and Statistics, Colorado Mesa University, Grand Junction, CO. Alexander C. Olson is now at the Department of Psychology, New Mexico State University, Las Cruces, NM. Indica R. Machost is now at the Department of Psychology, University of Tulsa, Tulsa, OK.
Materials and data for this study can be accessed at https://osf.io/s7nuj. This research did not receive any funding from the public, commercial, or notforprofit sectors. The authors have no conflicts of interest (both financial and nonfinancial) to disclose.
Relationship obsessivecompulsive symptoms have been previously studied using the terminology of relationship obsessivecompulsive disorder (ROCD). We chose to refer to ROCD as relationship obsessivecompulsive (OC) tendencies and relationship obsessivecompulsiveness as our work utilizes a nonclinical sample, and the construct has yet to be officially recognized by an authoritative organization (such as the American Psychiatric Association or World Health Organization). This decision is not intended as a gesture of disrespect toward the multitude of previous researchers whose theory and data the current work builds upon. Instead, our choice of terminology is an effort to clarify the construct for those unfamiliar with this mental health experience and the true definition of the word disorder. Correspondence concerning this article should be addressed to Jeremy Tost, Department of Social and Behavioral Sciences, Colorado Mesa University, 1100 North Avenue, Grand Junction, CO 815013122, United States Email: jtost@coloradomesa.edu
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