Spectrum Spring 2016 Vol 6 (3)

SPECTRUM Journal of Student Research at Saint Francis University

Volume 6 (3) Spring 2016

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Table of Contents “Women Didn’t Kill this Way”: Sharp Objects and the Subversion of Femininity and Motherhood Tara L. Fritz; Robin L. Cadwallader

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Media Coverage of 2016 Presidential Primary Debates: Information or Infotainment?

Amanda N. Schiavo; Patrick G. Farabaugh

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Effects of Food Association on Color Preference Sarah E. Polito; Alyson T. Pritts; Marnie L. Moist

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2015 Office of Student Research Awards for Research Excellence

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Call for papers

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(Student authors’ names underlined.)

Faculty Editors: Balazs Hargittai Professor of Chemistry bhargittai@francis.edu

Grant Julin Assistant Professor of Philosophy gjulin@francis.edu

Student Editorial Board: Allison Bivens ’12 Morgan Dutrow Cathleen Fry Paul Johns ’07 Sarah McDonald Steven Mosey ‘14 Miranda Reed William Shee Stephanie Wilson

Managing Designer: Grace McKernan

Cover: Photo by Grace McKernan

Kayla Brennan Hayden Elliott Eric Horell ’13 Elise Lofgren ‘14 Jonathan Miller ’08 Morgan Onink Hannah Retherford Margaret Thompson Staci Wolfe

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“Women Didn’t Kill this Way”: Sharp Objects and the Subversion of Femininity and Motherhood [Research conducted for ENGL 407 (Principles of Literary Research, Theory, and Practice)] Tara L. Fritz Literature & Languages Department School of Arts & Letters tlf100@francis.edu

Robin L. Cadwallader Literature & Languages Department School of Arts & Letters rcadwallader@francis.edu

A mother is supposed to be supportive, nurturing, and willing to give up anything for her children—at least that is what we have been taught.

However, in the novel Sharp Objects, Gillian Flynn subverts the idea of motherhood as we know it.

Though Adora, the mother figure of the story, seems at face value to be the perfect mother, she has, in fact, been twisted and corrupted by the traditional values forced upon her by the town in which she grew up, so much so that rather than protect her children, she deliberately harms them through Munchausen by Proxy syndrome. Flynn’s construction of this ultimately villainous character—as well as the other unlikeable female characters within the novel—shows her dedication to revealing what she refers to as the capacity for female violence. This violence is reflected not only in Adora but also in her daughters, both her real ones (Camille, Marian, and Amma) and the girls she mentors (Ann and Natalie). While Amma ends up following in her mother’s footsteps, and Marian dies due to Adora’s meddling with her health, Camille is punished and punishes herself because she does not embrace the same traditional values as Adora and Amma. Furthermore, Ann and Natalie are murdered because they do not conform to typical “feminine” values. Thus, it is clear that Flynn wished to show not only the potential for violence in women but also that this violence is created through oppression and traditional, patriarchal values.

Sharp Objects details the story of Camille Preaker, a crime reporter living in Chicago who

must return to her small home town of Wind Gap, Missouri, to cover the mysterious deaths of two young girls. Her homecoming forces her to confront not only her loveless mother, Adora, but also her volatile half-sister Amma and the ghost of her dead sister, Marian, that still lingers. Camille carries demons of her own: She is a heavy drinker, is slow to make real connections with the other characters, and carries around the secret of her selfharm, which manifests itself as words cut into her skin. As more and more of the mystery of the murders of Ann Nash and Natalie Keene unravels, Camille learns some of her own family’s dark secrets. It is revealed that Adora suffers from Munchausen by Proxy syndrome, which causes her to inflict illness on her children in such a way that she looks like the heroic mother when she saves them. The murders of Ann and Natalie (as well as the death of Camille’s sister Marian) are initially pinned on Adora. However, when Camille takes Amma back to Chicago with her and a similar murder occurs, it is revealed that Amma was the real murderer all along.

It is evident that Flynn’s novel is full of strongly-written female characters. However, Flynn has been accused of misogyny for the creation of these characters, as none of them are really likeable or heroic (Burkeman). Even Camille, as the narrator of Sharp Objects, is unreliable and rather unlikeable. Ann, Natalie, and Amma are described as violent girls: Ann killed her neighbor’s pet, Natalie was known for biting people, and Amma is the murderer of three girls (Ann, Natalie, and Lily

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Burke, a friend of Camille’s at the end of the novel). Finally, Adora is the ultimate villain, a mother who deliberately harms her children without remorse. Even the other women in the novel are portrayed unsympathetically, from Adora’s catty friends to Camille’s boring high school acquaintances to Amma’s classmates.

Some readers and critics have condemned Flynn’s creation of so many unlikeable and sometimes villainous female characters, even accusing her of “peddling ‘misogynist caricatures’” and writing from a place of hatred for women (Burkeman). However, Flynn defends her characters by insisting that she is “‘frustrate[d] . . . [by] this idea that women are innately good, innately nurturing’” (qtd. in Burkeman). Through Sharp Objects (as well as her other two novels, Dark Places and Gone Girl, which feature similarly villainous and hard-to-like female characters), Flynn attempts to reveal the long-hidden secret of female violence. “Libraries are filled with stories on generations of brutal men, trapped in a cycle of aggression,” she explains; “I wanted to write about the violence of women” (Flynn, “I Was”). Rather than harming the cause of feminism by creating villainous female characters, Flynn is, in fact, opening a dialogue on the multi-faceted nature of women and the ways in which a patriarchal, sexist society can negatively impact these women. It is through this corruption of traditional values that a character like Adora comes about.

“Illness Sits Inside Every Woman”: Representations of Traditional Values The town of Wind Gap, Missouri, is a breeding ground for traditional values. It is both a small town, as well as a town in the southern United States, which is often characterized as leaning toward the conservative side: For instance, once, while in Wind Gap, Camille comes across graffiti that simply reads, “Stop the Democrats” (Flynn, Sharp Objects 170). Camille is one of the only characters who has managed to escape her hometown, at least physically, which, upon her return, enables her to see its nastiness for what it is, a place that “demands utmost femininity in its

4 fairer sex” and imposes the impossible standards of traditional values on its residents (13). As a result, its women are trapped in a vicious cycle of high school cattiness, where the pretty girls prey on the poor, ugly, and/or less fortunate girls. These catty women spring from a culture that creates an impossible standard of femininity and encourages women to destroy each other in pursuing this ideal; this culture is upheld by what Lisa Cosgrove has identified as “coercive mechanisms of surveillance, discipline, punishment, and compulsory heterosexuality” used to keep gender norms intact (93). Camille’s old high school friends are the typical, submissive wives, concerned with nothing more than keeping a clean house and having as many babies as possible: “I’ve always dreamed of a big houseful of kids, that’s all I’ve ever wanted . . .

[W]hat’s so wrong with being a mommy?” wails one of the women as they all complain about their hardships as mothers (132). But these women are not looking for little girls of their own; indeed, Ann Nash’s sisters are described as “extraneous,” while her own birth as the third daughter in the Nash family is described as a “righteous dismay” (16).

Furthermore, one of Camille’s friends insists that she will keep having children until she has a boy.

In a culture where women are regarded in such a negative way, it is evidently difficult for women to respect not only other women but also themselves.

In creating a town so steeped in traditional, sexist culture, Flynn is making a statement about the negative effects these sorts of environments can have and in what ways they destroy and corrupt the women who are raised in them.

In such an environment, it is not hard to see how Adora’s unique brand of “mothering” came about. Though Adora presents herself as a perfect mother, she, in fact, represents the institution of motherhood as it becomes corrupted through patriarchy. She is a mother of the worst form—a mother who, rather than nurture her children, harms them for her own self-gratification, in one case resulting in the death of one daughter, Marian.

Silvia Tubert portrays motherhood as a symptom of the patriarchy; thus, to an outsider growing up in a patriarchal society, Adora seems to be a “good

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mother,” or, in Andrea O’Reilly’s words, a “white, middle class, married, stay-at-home” woman (21).

Adora is white, married, and so wealthy that neither she nor her husband needs to work; in this institutional view of motherhood, Adora seems to be the perfect mother. However, Adora suffers from Munchausen by Proxy syndrome. In cases like these, mothers induce illnesses in their children so that, by rushing them to the hospital, the mother seems to be the hero in the children’s recovery (Rand and Feldman).

Thus, within the narrative, Adora functions as the “Mother,” a representative of the traditional society in which she grew up as well as a representation of how this society can twist and corrupt the Mother figure. Looking at just her biological daughters—Camille, Marian, and Amma—we see that her strain of mothering has served to corrupt them as well. Marian died at her hands; Camille was driven to deviancy, so much so that she ruins her feminine beauty by carving words into her skin; and Amma, who grows to be a similar Mother/leader figure under Adora’s tutelage, becomes a murderer in the name of keeping traditional values intact. Adora also has connections with the two murdered girls, Ann and Natalie; she tutored them and acted as a mother to them, and they not only rejected her feminine influence by being violent tomboys but also paid for this rejection with their lives due to Amma’s jealousy.

Amma is manipulative, twisted, and violent, and, as Camille points out, “[a] child weaned on poison [who] considers harm a comfort” (251).

Amma grew up fighting for Adora’s attention while being constantly ill, thanks to Adora’s need to poison her children. Amma continually feels threatened by the presence of Ann and Natalie; even Camille’s arrival makes her jealous. Amma (and Camille) grew up starving for Adora’s attention; however, unlike Camille, who turned her anger into her own destruction, Amma takes her jealousy out on other people. It is a common theme throughout the novel that a woman could never be capable of the kind of violence it would take to murder two young girls. However, Angela

5 Woollacott identifies violence as “foundational to patriarchy” (16), and though Woollacott is speaking specifically of male violence, the female violence portrayed in the novel can be seen as another way in which Flynn shows women becoming corrupted through traditional patriarchal values. Amma learns this violence through none other than her mother, Adora. The murderer, even before she is known, is described as “[a] woman who wanted ultimate control . . . whose nurturing instinct had gone awry . . . who resented strength in females, who saw it as vulgar,” which concisely describes both Adora’s and Amma’s mindsets in relation to other women (232-33). It also is significant that Adora is the first one arrested for the murders of Natalie and Ann; even though Amma is the real killer, she has learned her mannerisms from Adora. Like Adora, Amma represents the traditional values of Wind Gap and women who are corrupted by them.

“Just Because They Were a Little Different”: Representations of Deviance Camille is the novel’s primary example of deviance and how it is punished in a patriarchal society. As a child, she was the one who rejected her mother’s pills and concoctions, who rejected Adora’s brand of love and left it to be inflicted on her sister Marian. Rather than being directly punished by the society around her, Camille begins to destroy a symbol of her femininity: her beauty.

In reference to this, Camille observes, “Every time people said I was pretty, I thought of everything ugly swarming beneath my clothes” (156). Though she leaves her face untouched, by carving words into her body, she further rejects her mother, as well as the culture in which she grew up. Many of these words are related, in some way, to femininity: bodice, lipstick, catfight, and girl are among the over sixty references to Camille’s scars.

Furthermore, self-harm is not considered culturally accepted and is often dismissed as though it were not a real medical problem (Failler).

In some cases, it is described as “attention seeking”; this signifies that Camille may have been mirroring Amma’s desire for Adora’s undivided

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attention (Failler 14). However, she does not receive this attention from Adora; in fact, her mother often says how much she dislikes her. This may be linked to Camille’s frequent rejection of Adora’s medical treatments, the same that she inflicted on Marian and Amma. “‘I wanted to love you, Camille,’” Adora says to her, “‘[b]ut you were so hard. Marian, she was so easy’” (Flynn, Sharp Objects 238). Earlier, Camille observes that “Adora hated little girls who didn’t capitulate to her peculiar strain of mothering” (221). Thus, Camille represents deviancy within the novel. Because she is not able to be mothered by the Mother, she is rejected by her and the community around her; in turn, she rejects herself and her own femininity.

It is made clear throughout the narrative that Ann Nash and Natalie Keene were murdered because they were outspoken, sometimes violent tomboys who disliked femininity. Ann was “prettied up” before she died, and Natalie’s fingernails were painted—both are signs of the control that Amma wished to exhibit over them to make them more feminine. When the girls ultimately rejected this control, Amma had no choice but to kill them. However, their rejection by society did not begin with their deaths; Ann and Natalie were teased and tortured by Amma and her clique of pretty girls long before they were murdered. They were seen as outsiders, deviants of society’s expectations for little girls. Andrea Nicki, writing about the cultural rejection of those with mental illnesses, states that “a woman who displays aggression and ambition, and is not feminine, risks being labelled ‘mentally ill’” or, in this particular case, deviant (81). Nicki further describes “conventional female behavior” as including “quietness, self-effacement, and cautiousness” (90).

Ann and Natalie both clearly break from these traditional female roles. Ann is described as being smart and outspoken; at times, she is even violent: She is accused of killing a neighbor’s pet and stabs Natalie with a needle during a sewing project.

Natalie is also known for being violent: Her family is forced to move to Wind Gap after she injures another girl with scissors. However, both girls are extremely intelligent and outspoken, despite being

6 known as troublemakers. As Natalie’s brother reflects in an interview conducted by Camille on the murders, “‘It’s like they picked the two girls in Wind Gap who had minds of their own and killed them off’” (207). Thus, Ann and Natalie are killed

by Amma not only because she is jealous of the attention they are receiving from Adora, but also because the two girls represent a deviancy from the cultural expectations for women.

Conclusion: “To Refuse Has so Many More Consequences than Submitting” Flynn’s Sharp Objects contains some of her most destructive, unlikeable female characters to date. Adora represents what could be the perfect mother—white, wealthy, stay-at-home, and raised in and devoted to maintaining a traditional environment; however, this typically feminine and caring character has been corrupted by the impossible expectations society has set for her. Her youngest daughter, Amma, grows up to mirror her, carrying out punishments in the name of keeping traditions intact. Though Marian dies under Adora’s care, Camille’s rejection of Adora’s mothering leads her to, in some ways, punish herself for not conforming to tradition while also retroactively allowing her to see the dangers of following these traditions. Finally, Ann Nash and Natalie Keene fall victim to the concept of destructive motherhood: Because they did not conform to the town of Wind Gap’s cultural values, they were punished with death.

As Camille wryly observes, “‘Some women aren’t made to be mothers. And some women aren’t made to be daughters’” (112). In the context of the real world, Adora should have never been a mother: She is destructive, unloving, and utterly corrupt.

However, as a literary character, Adora serves as a warning for what our traditional cultural values can do. Without patriarchal values that define mothers as nothing more than caring, nurturing, and powerless above all, there would be no need to display the violence of women as something hidden just beneath a mother’s smile.

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Works Cited Burkeman, Oliver. “Gillian Flynn on Her Bestseller Gone Girl and Accusations of Misogyny.” Guardian. The Guardian, 1 May 2013. Web. 11 Nov. 2015.

Cosgrove, Lisa.

“Feminism, Postmodernism, and Psychological Research.” Hypatia 18.3 (2003): 85-112.

Project Muse. Web. 10 Nov. 2015.

Failler, Angela. “Narrative Skin Repair: Bearing Witness to Representations of Self-Harm.” English Studies in Canada 34.1 (2008): 11-28. Project Muse. Web. 10 Nov. 2015.

Flynn, Gillian. “I was not a nice little girl . . .” Gillian Flynn.

Gillian Flynn, n.d. Web. 11 Nov. 2015.

---. Sharp Objects. New York: Broadway, 2006. Print.

Nicki, Andrea. “The Abused Mind: Feminist Theory, Psychiatric Disability, and Trauma.” Hypatia 16.4 (2001): 80-104. Project Muse. Web. 9 Nov. 2015.

O’Reilly, Andrea. “Outlaw(ing) Motherhood: A Theory and Politic of Maternal Empowerment for the Twenty-first Century.” Hecate 36.1 (2010): 17-29. Literature Resource Center. Web. 11 Nov. 2015.

Rand, Deirdre C., and Marc D. Feldman. “An Exploratory Model for Munchausen by Proxy Abuse.” International Journal of Psychiatry in Medicine 31.2 (2001): 113-26.

Proquest. Web. 15 Nov. 2015.

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Tubert, Silvia. “The Deconstruction and Construction of Maternal Desire: Yerma and Die Frau ohne Schatten.”

Mosaic 26.3 (1993): 69-88. Literature Resource Center.

Web. 10 Nov. 2015.

Woollacott, Angela. “A Feminist History of Violence: History as a Weapon of Liberation?” Lilith: A Feminist History Journal 16 (2007): 1-16. Literature Resource Center. Web. 9 Nov. 2015.

Tara Fritz ('17) is an English major with minors in French, Women's Studies, and Social Responsibility. She is President of the Literary Club, works as a tutor at the Writing Center, and is actively involved in her sorority, Theta Phi Alpha.

She is also a member of Sigma Tau Delta, the English honors society. After graduation, she hopes to obtain an MFA in Creative Writing and one day become a published author.

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Media Coverage of 2016 Presidential Primary Debates: Information or Infotainment?

Amanda N. Schiavo Communications Department School of Arts & Letters ans122@francis.edu

Patrick G. Farabaugh Communications Department School of Arts & Letters pfarabaugh@francis.edu

After viewing the first Democratic debate and the third Republican debate of the 2016 U.S. presidential primary race, the author conducted a content analysis. The author analyzed the debate statements made by each of the parties’ two leading (in the polls and at the time) candidates, as well as media coverage of their debate statements, specifically coverage by The New York Times, the Washington Post and the USA Today in the 24 hours following these debates. The author sought to determine whether or not the immediate post-debate media coverage of these three major news outlets was reflective - in content and proportionality - of the candidates’ debate statements. The findings reveal that post-debate media coverage of the candidates’ debate statements is not proportionally reflective of the content of their statements.

Introduction A relatively new component of the U.S. presidency, presidential debates provide American voters with a chance to compare political party candidates. Porter (2012) claims that “the history of presidential debates might be brief, but it is packed with memorable moments: zingers and flubs, triumphs and flops, and tons of backroom dish” (p.

S.4).

While radio presidential debates date back to 1948, the first televised presidential debate took place in 1960 between John F. Kennedy and Richard Nixon. This important debate provides an example of the power of television compared to that of radio. Although most of the individuals listening to the broadcast over the radio thought Nixon won, Kennedy’s charismatic, attractive appearance won over the television viewers. “The instant collective wisdom in 1960 was that Nixon was undone by television. Polls showed that more than half the voters based their decision on the debates” (Porter, 2012, p. S.4). The visual of Kennedy standing confidently, next to a perspiring Nixon, was enough for Kennedy to capture the presidency. This might not have been possible without the televised presidential debate.

Presidential debates have dramatically transformed since that first televised debate in 1960. Online journalism is the latest “disruptive technology” that has affected debates between aspiring politicians. For many voters, most of what they learn about the presidential candidates comes from political debates or the media coverage following these events. "As messages running an hour or longer, debates offer a level of contact with candidates clearly unmatched in spot ads and news segments. The debates offer the most extensive and serious view of the candidates available to the electorate" (Jamieson in Benoit and Currie, 2001, p. 28). Each debate viewer interprets a different message from the candidates’ debate statements than another viewer. With the media coverage following the debates unable to capture significant details for every viewer, individuals who base their knowledge of the candidates on news coverage are susceptible to risk of disproportionate coverage of content.

Along with the millions of viewers watching presidential debates, millions of other voters seek information solely through the media coverage of debates. “A study of the first 1976 presidential

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debate revealed that, of the viewers who were surveyed immediately after the debate (without exposure to media commentary), about twice as many thought that (Jimmy) Carter had done a better job. Those who were surveyed after seeing postdebate media commentary thought, again by approximately a two-to-one ratio, that (Gerald) Ford had done the better job” (Lang and Lang in Benoit & Currie, 2001, p. 29). This study highlights the significance of media coverage as an influence on voters.

Identifying the significance of presidential debates and media coverage of them, the author conducts a content analysis of three major new outlets’ reporting on candidates’ debate statements.

The results reveal that post-debate media coverage of candidates’ debate statements is not proportionally reflective of the content of their statements. In terms of topic, the policy and the proportion of character comments in media reports on the debates were significantly higher than the actual proportion of policy and character comments in the debates.

Review of Literature Presidential debates have been analyzed in previous studies on the basis of “two key dimensions: functions (positive and negative or attack messages) and topic (issues or policy along with image or character)” (Benoit and Currie, 2001, p. 29). However, Benoit and Currie’s (2001) content analysis of the 1996 and 2000 presidential debates is used to provide a framework for this content analysis. The framework established by Benoit and Currie’s (2001) regarding methodology and findings was followed in the current author’s study and revealed two trends (theme and topic) in the history of media coverage of presidential debates.

Functions.

Benoit and Harthcock (1999) performed a content analysis on the functions within the first televised presidential debate between Nixon and Kennedy. The analysis concluded that there were 49% positive (acclaim) debate statements, 39% attack (negative) debate statements, and 12% defense debate statements (p.

9 341). However, Benoit and Harthcock did not examine whether media coverage of debate statements is proportionately reflective of the content of the debate statements made by candidates.

A content analysis performed by Reber and Benoit (2001) furthered the research of the accuracy of media coverage pertaining to presidential debates. The study revealed that media coverage of the 2000 presidential primary debates highlighted attack (negative) and defense statements disproportionately to the actual candidates’ debate statements. Reber and Benoit’s (2001) research on 25 presidential primary debates from 1948 to 2000 revealed that 58% of the debate statements were acclaims (positive), 31% were attacks (negative), and 12% were defenses. When compared to media coverage (newspaper), the authors revealed that attacks (negative) were disproportionately reported by over-representing the coverage, 45% to 31%. Defenses were also over-represented, 16% to 12%. Acclaims were under-represented, 40% to 58%. Reber and Benoit’s study suggested that further research be conducted on the media coverage of presidential debates.

Benoit and Currie’s (2001) content analysis furthered the research of Reber and Benoit. Benoit and Currie’s (2001) content analysis of the 1996 presidential debates revealed that 59% of debate statements were allocated to positive (acclaim) statements, 33% to attack debate statements, and 7% of the debate statements were defenses. The functions of the 2000 presidential debate content analysis found that positive (acclaim) debate statements totaled 74%, attack (negative) statements equaled 24%, and defense statements equaled 2% (p. 34). In the 1996 news coverage, Benoit and Currie (2001) found that while only 33% of debate statements were categorized as attacks, 54% of the news coverage focused on attacks. Defense statements, which made up 7% of the debate statements, were also over-represented by 4% in the news coverage (11% total in the news coverage) (p. 34).

By contrast, acclaims were under-represented, which is similar to the Reber and Benoit (2001)

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study, at 35% in the news coverage to 59% in debate statements. This trend was also evident in Benoit and Currie’s (2001) analysis of the 2000 presidential debates. Comparing the debate statements to newspaper feature stories, rather than news coverage, attack and debate statements were over-represented, 38% and 13% in the newspaper features, to 24% and 2% in the debates, respectfully. Acclaims, which accounted for 74% of debate statements, were under-represented (49%) in the newspaper features. Both, content analyses identify a consistency in media coverage to over-represent attack and defense statements, while under-representing acclaim statements.

Topics.

For the Nixon-Kennedy presidential debates, Benoit and Harthcock (1999) performed an analysis on the topics (policy and character) of the candidates’ debate statements (p. 341). This analysis revealed that 78% of the candidates’ statements were policy remarks, whereas 22% of the candidates’ statements were character remarks.

Another study, conducted by Benoit, Blaney, and Pier (1998), analyzed the topics in the Clinton-Dole presidential debates. The authors found that the candidates’ remarks featured a higher concern on policy (72%) compared to character remarks (28%). While the debates featured a trend in higher policy remarks compared to character remarks, the accuracy of media coverage was not explored in these analyses.

Reber and Benoit (2001) conducted a content analysis on the proportion of policy and character remarks reported in newspaper coverage on two of the 2000 presidential primary debates. Their analysis did not reveal a significant difference between the coverage on policy or character remarks.

The content analysis performed by Benoit and Currie (2001) supported their hypothesis that media coverage would report more on character remarks than policy remarks from the 1996 presidential debates. “Policy remarks accounted for 72% of the comments in the debate, but only 55% of reportage, whereas character constituted 28% of the remarks in the debate, but 45% of the reports on the debate” (p. 34). This same hypothesis was not supported for

10 the media coverage of the 2000 presidential debates. According to Benoit and Currie (2001), no significant difference in the reporting of the topics was apparent (p. 34).

Although there was not indisputable evidence that the proportion of character comments in media coverage is significantly higher than the actual proportion of policy and character comments in debates, the author did not disregard the 1996 findings of Benoit and Currie (2001). The author chose to explore the themes and topics as explored in Benoit and Currie’s (2001) content analysis in an attempt to reveal the evolution toward “infotainment” within the political journalism industry.

Purpose. Overall, presidential debates have been shown to have a significant impact on the opinions of American voters. However, until Benoit and Currie’s (2001) content analysis, the media coverage of presidential debates was underresearched regarding accuracy in reporting the debate substance (specifically, theme and topic of the candidates’ statements).

Using Benoit and Currie’s (2001) methodology, this study builds on the research of media coverage of presidential debates. The findings provide evidence of a trend in the media coverage focusing on the theme of candidates’ debate statements and provide evidence suggesting that character remarks are more heavily covered than policy remarks.

Hypotheses As noted above, the author utilized the methodology used by Benoit and Currie (2001).

Maintaining consistency with the content analyses of the 1996 and 2000 presidential debates, the author looks for a pattern in the media coverage of presidential debates throughout history. The author, therefore, also explores Benoit and Currie’s (2001) two hypotheses: “H1. The proportion of attacks and defenses will be higher in media reports than in debates; whereas, the proportion of acclaims will be lower in media reports than in debates” (p. 31); “H2. The proportion of character comments in media reports on the debates will be significantly

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higher (and policy comments lower) than the actual proportion of policy and character comments in the debates” (p. 31).

Methods The 2016 presidential primary race featured 17 debates – 11 between the Republican candidates and six among the Democratic Party challengers.

After viewing the first Democratic debate and the third Republican debate, the author performed a content analysis on the statements made by each of the leading (in the pools and at the time) candidates.

First, the author classified statements made by the parties’ two leading candidates. Their statements were categorized as acclaims, attacks or defenses (defined below). Benoit and Currie (2001) explain that together, they “serve as an informal form of cost-benefit analysis: acclaims stress a candidate’s own benefits, attacks identify opponents’ costs, and defenses attempt to refute alleged cost” (p. 32).

“Acclaims are utterances that portray the candidate favorably. Attacks are utterances that portray the opposing candidate unfavorably. Defenses are utterances that explicitly respond to prior attack on the candidate” (Benoit and Currie, 2001, p. 33).

An example of an acclaim in the first Democratic debate by the party’s leading candidate, Hillary Clinton, would be: “I have a long history of getting things done, rooted in the same values I’ve always had” (The New York Times, 2015, p. 4). Clinton’s statement portrays herself as a successful politician, whose values have been consistent throughout her career. Bernie Sanders, the second-place Democratic candidate, attacked Clinton when he said: “First of all, she is talking about, as I understand it, a no-fly zone in Syria, which I think is a very dangerous situation. Could lead to real problems” (The New York Times, 2015, p. 9). Sanders attacks Clinton on the basis of her statement supporting a plan to respond to Russian President Vladimir Putin’s military maneuvers in Syria. Clinton provides an example of a defense in

11 her statement, “I have been very consistent. Over the course of my entire life, I have always fought for the same values and principles, but … I do absorb new information” (The New York Times, 2015, p. 4). Clinton defends herself against an attack on her consistency in her values and principles by stating that her values and principles have not changed, but that she was provided with new information that called for a different reaction.

These three themes were then classified into two categories: policy and character.

“Policy remarks concern governmental actions and problems amendable to such action.

Character remarks address properties, abilities, or attributes of the candidates (or parties)” (Benoit and Currie, 2001, p. 33).

The examples provided below were taken from the “Transcript: Republican Presidential Debate (2015).” The Republican Party’s leading candidate, Donald Trump, said: “We’re reducing taxes to 15%. We’re bringing corporate taxes down, bringing money back in, corporate inversions.”

This is an example of a policy acclaim (p. 5).

Trump’s statement concerns governmental action through his proposed tax plan.

Ben Carson, the second leading Republican candidate at the time: “I do, however, believe in Reagan’s 11th commandment, and will not be engaging in awful things about my compatriots here” (p. 3). This is a character acclaim.

Not all of the candidates’ debate statements fell within the coding classifications of acclaims, attacks, and defenses. Sanders’ statement: “I think everybody is in agreement that we are a great entrepreneurial nation” (The New York Times, 2015, p. 2) is not coded because it simply states Sanders’ opinion. He is not stating this to portray himself favorably, unfavorably, or defend himself in any way.

A total word count was taken from the first Democratic debate (Las Vegas, Nevada; October 13, 2015) for Clinton and Sanders, as well as for Trump and Carson for the third Republican debate (Boulder, Colorado; October 28, 2015). Each

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candidates’ total word count was divided by his or her total number of words spoken within each theme (acclaims, attacks, and defenses). This number was then multiplied by 100 to determine a percentage of acclaims, attacks, and defenses. For example, Carson spoke a total of 1,478 words in the third Republican debate. One hundred and fiftyeight words of the total 1,478 words he spoke were defense statements. The author divided 158 by 1,478 and multiplied by 100 to conclude that Carson spent 11% of his talking time delivering defense statements. This method was replicated to achieve the percentage of total policy and character remarks for each candidate and policy and character remarks within each theme (acclaims, attacks, and defenses) for each candidate.

After the candidates’ debate statements were collected and classified, the author analyzed media coverage from three major U.S. news outlets following the debates – The New York Times, the Washington Post and the USA Today.

First, the statements within the news outlets’ articles discussing the parties’ two leading candidates’ comments were identified. The author only coded articles released from the three previously mentioned news outlets in the 24 hours following each debate. The author’s purpose of restricting her content analysis of media coverage to only the 24 hours following the debate was to analyze the debate statements the news outlets chose to present to voters right away.

Second, the author classified the candidates’ debate statements in the media articles into the three categories used for the candidates’ debate statements (acclaims, attacks, and defenses).

Third, the three categories (acclaims, attacks, and defenses) were categorized into the two topics (policy and character) - just as candidates’ debate statements were classified.

The researcher then classified the media coverage of the debate statements, including what percentage of themes or idea units occurred in the debates were reported in the media coverage. A total word count was taken for each of the articles from The New York Times, the Washington Post and the USA Today. The total word count for each article released in the 24 hours following the debate

12 was combined to determine the total number of words published by each of the three major news outlets respectfully. The total number of words by each news outlet was then parsed down to the total number of candidates’ statements printed within each theme (acclaims, attacks, and defenses). The word counts were then used to determine a percentage of media coverage on acclaims, attacks, and defenses for each candidate. For example, The New York Times released a total of 2,521 words in their three articles released in the 24 hours following the third Republican debate. Out of the 2,521 words, The New York Times spent 104 words (of 2,521 total words printed) reporting on Carson’s defense statements.

The author divided 104 by 2,521 and multiplied by 100 to conclude that The New York Times spent 4.13% of their total words reporting on Carson’s defense statements. This method was replicated to achieve the percentage of media coverage on the total policy and character remarks for each candidate and policy and character remarks within each theme (acclaims, attacks, and defenses) for each candidate. The author also determined the percentage of media coverage on total policy and character remarks for each candidate and policy and character remarks within each theme (acclaims, attacks, and defenses) for each candidate within each article published by each of the three news outlets.

Similar to the candidates’ debate statements, not all of the media’s statements within the news articles fell within the coding classifications (acclaims, attacks, and defenses). In The New York Times, “Republican candidates take sharp tone in third debate (2015)”: “Mr. Rubio, a first-term senator, had the best night of his campaign, showing political talent that many insiders had long seen in him” (p. 1) the author did not code this statement because Rubio was not one of the two Republican Party’s two leading candidates during the polls of the third Republican debate.

Similarly, statements such as the one below were not coded by the author because it offers the author’s opinion on Clinton’s “progressive” claim (p. 1). The statement is not one that Clinton made herself. “It was a practiced line – so practiced that

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13

she used it, somewhat awkwardly, a second time an hour later” (Bruni, 2015, p. 1).

For the first Democratic debate coverage, the author analyzed two articles released by The New York Times, three by the Washington Post, and one by the USA Today. For the third Republican debate coverage, the author analyzed three articles released by The New York Times, three released by the Washington Post, and six released by the USA Today.

The author was the only coder for this content analysis. She used printed versions of the debate transcripts and news stories to act as guides for consistency in coding alike statements.

Results Top Candidates Hillary Clinton Bernie Sanders

under-represented in the post-debate media coverage. The media coverage of acclaim debate statements was only 16%, compared to the actual 25% of acclaims spoken by the candidates.

Similarly, 8% of post-debate media coverage of the Democratic candidates’ statements was dedicated to defenses; the actual percentage of defense statements made by the Democratic candidates was 18%.

Top Candidates

Policy

Character

Total Words

Hillary Clinton

903

611

5,397

(17%)

(11%)

1,061

243

(23%)

(5%)

447

44

(20%)

(2%)

87

70

(7%)

(5%)

105

-

Bernie Sanders Acclaims

Attacks

Defenses

825

89

600

(15%)

(2%)

(11%)

905

94

305

(20%)

(2%)

(7%)

136

333

22

(6%)

(15%)

(1%)

128

-

29

Total Words

The New York Times

4,561

2,198

5,397 Washington Post 4,561 USA Today

1,304

565

(19%) The New York Times Washington Post USA Today

(10%) -

2,198

Table 2. Topics of the first Democratic debate of the 2016 U.S. Presidential primary race.

1,304

(2%) 76

29

(13%)

(5%)

565

Table 1. Functions of the first Democratic debate of the 2016 U.S. Presidential primary race.

The first hypothesis, that the proportion of attacks and defenses will be higher in media reports than in debates, was partially supported (H1 – partially supported). Referring to Table 1, the attack debate statements made by the two leading Democratic candidates in the debates totaled 4%; whereas, 28% of the media coverage of the candidates’ statements were attacks. This indicates an over-representation by the media of attacks.

Conversely, both acclaims and defenses were

The second hypothesis, that media coverage would report character comments in the debates significantly higher (and policy comments lower) than the actual proportion of policy and character comments in the debates, was not supported (H2 – not supported). Policy was over-represented in post-debate media coverage for the first Democratic presidential debate by the three news outlets (46% to 40%). The percentage of policy remarks reported in post-debate media coverage was 46%, compared to 7% of character remarks.

Table 2 shows that policy remarks were more highly reported than character remarks within each of the three news outlets: 20% to 2% for The New York Times, 7% to 5% for the Washington Post, and 19% to 0% for the USA Today. The amount of post-debate media coverage from the first

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Democratic debate was greater on governmental actions than on the properties or attributes of the candidates.

Top Candidates Donald Trump Ben Carson

The New York Times Washington Post USA Today

Acclaims

Attacks

Defenses

540

107

34

(26%)

(5%)

(2%)

117

-

158

(8%)

Total Words

153

(1%)

(6%)

133

46

(3%)

(1%)

11

-

104 (4%)

-

-

Top Candidates

Policy

Character

Total Words

Donald Trump

548

133

2,053

(27%)

(6%)

240

35

(16%)

(2%)

183

100

(7%)

(4%)

68

111

(2%)

(2%)

-

11

2,053

1,478 Ben Carson

(11%)

26

debate media coverage of the acclaims, attacks, and defenses made by the candidates in the debates is not proportionally reflective in the post-debate news coverage.

2,521

4,481

3,132

(.4%)

The New York Times

The Washington Post

The USA Today

1,478

2,521

4,481

3,132

(.4%)

Table 3. Functions of the third Republican debate of the 2016 U.S. Presidential primary race.

Table 4. Topics of the third Republican debate of the 2016 U.S. Presidential primary race.

The data collected identifies that media coverage of the first Democratic debate of the 2016 presidential primary season over-represents attacks, while under-representing acclaims and defenses.

The analysis also indicates that the topics of policy and character of the media coverage of this 2016 presidential debate focused more on policy remarks than character remarks.

Similarly, attack debate statements were overrepresented proportionally (7%) in the post-debate media coverage compared to the actual candidates’ attack debate statements (5%). However, acclaims and defenses were under-represented in the media coverage of the third Republican debate. Referring to Table 3, acclaims constituted 34% of the two leading Republican candidates’ statements, whereas only 4.4% of the candidates’ acclaim debate statements were reported in post-debate media coverage. Likewise, defenses constituted 13% of the candidates’ debate statements, whereas only 4% of the candidates’ defense debate statements were covered in the post-debate media coverage. Therefore, the findings reveal that post-

The second hypothesis, that media coverage would report character comments in the debates significantly higher (and policy comments lower) than the actual proportion of policy and character comments in the debates, was not supported.

Neither character nor policy remarks were overrepresented in post-debate media coverage by the three news outlets combined. However, total policy remarks were higher than character remarks. The total percentage of policy remarks reported in all three outlets’ post-debate stories was 9% compared to 6.4% character remarks (see Table 4). There was no specific indication that policy remarks were more highly reported than character remarks within each of the three news outlets, as evident in the topic (policy and character) of the media coverage of the first Democratic debate. Thus, the topic of post-debate media coverage of the third Republican debate was focused more on governmental actions and problems than the properties or attributes of the candidates, just it was in the first Democratic debate media coverage.

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Discussion The results partially supporting H1 reveal a trend in the over-representation of attacks in media coverage of presidential debates. Both Reber and Benoit’s (2001) study and Benoit and Currie’s (2001) study of the media coverage of functions (acclaims, attacks, and defenses) in presidential debates revealed an over-representation of attack statements made by candidates in coverage than actual attack statements made in the debates.

Therefore, results in this study partially support H1, which reveals a trend in the media coverage of presidential debates to over-represent attack statements made by candidates.

There are potential explanations as to why attack statements are over-represented in media coverage. First, attacks are more entertaining because of the “conflictual” aspect (Benoit and Currie, 2001, p. 36). Voters are intrigued by the excitement that attacks offer when two candidates disagree in presidential debates. Mindich (2005) states that “if their [journalists] information is boring, they will lose readers and viewers” (p. 41).

To add interest to their stories, journalists overrepresent these attacks between candidates.

Second, the 2016 primary season is one that included three nontraditional candidates. Sanders is a self-described socialist. Trump, a former host of the reality game show, “The Apprentice,” is president of the Trump Organization and founder of the Trump Entertainment Resorts (“Donald Trump biography,” p. 1). Carson is a retired neurosurgeon and author. Trump and Carson have no previous political experience. Clinton is the only “traditional politician” whose statements were collected for this content analysis.

The nontraditional candidates, especially Trump, could potentially create a higher reportage of infotainment. “The news media act as an arm of the state and help it maintain power by manipulating the nature of news to teach the public which events, people, and ideas will be rewarded or punished” (Lazaroiu, 2008, p. 106). Through the over-representation of attack statements made by candidates in the presidential debates, voters are able to decide whether or not the behavior (attack statement) is one to be rewarded or punished. For

15 example, Trump’s attack on Gov. John Kasich: “But then his poll numbers tanked. He has got – that is why he is on the end. And he got nasty. And he got nasty.” This could influence voters to vote for or against Trump based on his attack on the character of Kasich (“Transcript: Republican Presidential Debate (2015)”, 2015, p. 8). Therefore, the media coverage could be providing a method for voters to select favorable or unfavorable candidates through agreement or disagreement with the actions, statements, and behaviors of the candidates in the presidential debates.

Acclaim and defense statements were underrepresented by media coverage of the first Democratic and third Republican debate. However, both these themes of statements are important part of the process of selecting a candidate. Benoit and Currie (2001) claim that “we need to know the candidates’ strengths and their goals and proposals (campaign promises) to make informed decisions about the relative merits of presidential candidates” (p. 36). The information, less interesting than attack statements, falls under the category of positive and neutral statements. However, Lazaroiu (2008) states “positive or routine occurrences are rarely news because, if things are okay, there is no need to highlight them” (p. 106). Therefore, the media coverage could cover less of these themes because of their significance compared to attack statements.

As for the media coverage of the topics (policy and character) of candidates’ remarks, Benoit and Currie’s (2001) study of the 1996 presidential debates found that the media over-represented the coverage of character remarks compared to policy remarks. However, their findings from the 2000 presidential debate revealed that there was no difference in the proportion of policy and character remarks reported in the media coverage. Benoit and Currie (2001) state that the “findings are inconsistent with the finding of Study 1 on the 1996 debates, but consistent with Reber and Benoit’s (in press) finding on 2000 primary debates” (p. 36). Neither policy remarks nor character remarks were revealed to be overrepresented by the media coverage of the first Democratic and the third Republican debate of the 2016 presidential primary race.

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However, policy remarks were found in postdebate coverage to be reported at a higher proportion than character remarks. The total policy remarks reported for the first Democratic debate in the three new outlets was 28%, compared to 7% of total character remarks. The total percentage of policy remarks reported in all three post-debate media coverage for the third Republican debate was 9%, compared to 6.4% of character remarks.

Potential reasons why policy remarks or character remarks are more highlighted in media coverage of specific debates could be the debate questions asked by the different moderators in each debate and for the different political parties, the answering techniques of the candidates, or the latest issues the media is concerned with during specific presidential debates. Overall, there is not an identified pattern of which topic is reported in media coverage more often.

Conclusion The importance of presidential debates should not be underestimated. Presidential debates provide the electorate an opportunity to form perceptions on the political parties’ eligible candidates. And, the media covering the presidential debates is responsible for informing the American voters who were unable to watch the debates themselves. The articles provided by three major news outlets examined in this study: The New York Times, the Washington Post and the USA Today could have led to inaccurate perceptions of the themes discussed by the Democratic and Republican candidates for president.

Through the findings of this content analysis and the findings of previous analyses on the media coverage of presidential debates, voters cannot expect to receive proportionate coverage of the themes of candidates’ debate statements. In terms of topic, this study did not identify a higher proportion of character or policy remarks in media reports compared to the actual proportion of policy and character remarks in the debates. Clemons and Lang (2003) explain that the “reader expects accurate descriptions of events and credible commentaries and analyses” (p. 278). Journalists have a responsibility to accurately inform their

16 readers. However, as this analysis reveals, when it pertains to information on presidential debates, voters should watch the debates to receive a more accurate depiction of the themes of the candidates’ statements, and not trust news outlets’ reports on the debates.

Notes Articles examined from the three news outlets: The New York Times – “Donald Trump Offers Stark Contrast With Democrats on Guns.” Shear, Michael D. October 28, 2015.

– “John Kasich Rebukes Donald Trump and Ben Carson.”

Shear, Michael D. October 28, 2015.

– “Hillary Clinton’s Democratic Debate Magic.” Bruni, Frank. October 13, 2015.

– “Hillary Clinton Turns Up Heat on Bernie Sanders in a Sharp Debate.” Barbaro, Michael and Chozick Amy. October 13, 2015.

– “Republican Candidates Take Sharp Tone in Third Debate.”

Healy, Patrick and Martin Jonathan. October 28, 2015.

Washington Post – “GOP candidates tangle with one another – and CNBC – in a chaotic debate.” Rucker, Philip and Johnson Jenna.

October 28, 2015.

– “Republican debate: Rubio, Cruz, Christie deliver strong performances.” Fahrenthold, David A. and Phillip Abby.

October 28, 2015.

– “The candidate breaking through in the Democratic debate?

Bernie Sanders” Penzenstadler, Nick. October 13, 2015.

– “The Moment when Hillary Clinton Won the First Democratic Debate.” Stromberg, Stephen. October 13, 2015.

– “Winners and Losers from the First Democratic Presidential Debate.” Cillizza, Chris. October 13, 2015.

– “Winners and Losers from the Third Republican Presidential Debate.” Cillizza, Chris. October 28, 2015.

USA Today – “A look behind the scenes of tonight’s GOP debate.” Fox, Brooke. October 28, 2015.

– “Chris Christie not amused by fantasy football question.”

Penzenstadler, Nick. October 28, 2015.

– “Cruz and others blast CNBC, media over debate.”

Penzenstadler, Nick. October 28, 2015.

– “Long-shot GOP candidates debate economy in Colorado.”

Penzenstadler, Nick. October 28, 2015.

– “For the Record: Happy debate night, let’s drink.” Estepa, Jessica. October 28, 2015.

– “Sanders skips pleasantries, blasts Wall Street, campaign finance system.” Jacobs, Jennifer. October 13, 2015.

– “The most quotable moments from the third Republican debate.” Firozi, Paulina. October 28, 2015.

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Works Cited Benoit, W. L., & Currie, H. 2001. “Inaccuracies in media coverage of the 1996 and 2000 presidential debates.”

Argumentation and Advocacy, 38(1), 28-39.

Benoit, W. L., Blaney, J.R., & Pier, P.M. (1998). Campaign ’96: A functional analysis of acclaiming, attacking, and defending. Westport, CT: Praeger.

Benoit, W. L., & Harthcock, A. (1999). Functions of the great debates: Acclaims, attacks, and defenses in the 1960 presidential debates. Communication Monographs, 66(4), 341-357.

“Donald Trump biography.”(n.d.). http://www.biography.com/people/donald-trump-9511238 Clemons, E.K. and Lang, K.R. (2003), "The Decoupling of Value Creation from Revenue: A Strategic Analysis of the Markets for Pure Information Goods," Information Technology and Management 4(2-3): 278.

Lang, G. E., & Lang, K. (1980). The formation of public opinion: Direct and mediated effects of the first debate. In G. F. Bishop, R. G. Meadow, & M. Jackson-- Beeck, (Eds.), The presidential debates: Media, electoral, and policy perspectives (pp. 61- 80). New York: Praeger.

Lanoue, D. J., & Schrott, P. R (1991). The joint press conference: The history, impact, and prospects of American presidential debates. New York: Greenwood Press.

Lazaroiu, G. (2008). News Media, Infotainment, and the Decline of Reporting. Economics, Management and Financial Markets, 3(1), 104-109.

Mindich, D. (2005), Tuned Out: Why Americans Under 40 Don't Follow the News. New York: Oxford University Press, 41.

17 Porter, W. 2012. “Parry and thrust: A look at the history of U.S. presidential debates.” Denver Post, S.4.

Reber, B. H., & Benoit, W. L. (2001). Presidential debate stories accentuate the negative. Newspaper Research Journal, 22(3), 30-43.

The New York Times (2015). Full Transcript: Democratic Presidential Debate. http://www.nytimes.com/2015/10/14/us/politics/democratic -debate-transcript.html Transcript: Republican Presidential Debate. (2015). http://www.nytimes.com/2015/10/29/us/politics/transcriptrepublican-presidential-debate.html

Amanda Schiavo ('16) is a Communications major with a minor in political science. She is involved in the SFU community by being a member of the SFU's Women's Volleyball Team, a member of the Pre-Law Club, and the Editor-in-Chief of the Troubadour. Amanda was selected as the 2016 SFU nominee to attend The Republican National Convention, was selected to present her research paper at The Examined Life: An Undergraduate Conference in the Liberal Arts and National Council on Undergraduate Research, and is a member of the Student-Athlete Mentors. After graduation, Amanda will continue her education by obtaining her juris degree.

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18

Effects of Food Association on Color Preference [Research conducted for PSYC 202 (Research Methods and Statistics II)] Sarah E. Polito Occupational Therapy Department School of Health Sciences sep101@francis.edu

Alyson T. Pritts Occupational Therapy Department School of Health Sciences atp100@francis.edu

Marnie L. Moist, Ph.D. Psychology Department School of Arts & Letters mmoist@francis.edu

Color preferences are impacted by various object associations (Strauss, Schloss, & Palmer, 2013; Taylor & Franklin, 2012). Humans tend to change their color preference based on objects they like or dislike in relation to different colors.

Specifically, it was of interest if food association also has a significant impact on color preference for humans. This is important because it would suggest more about the relationship between certain colors and certain foods. Many studies have reported significant findings in understanding color preferences, specifically differences in gender color preferences (Granger, 1955; Guilford, 1940; Madden, Hewett, & Roth, 2000; Strauss et al., 2013; Taylor & Franklin, 2012; Taylor, Schloss, Palmer, & Franklin, 2013). Humans also tend to like colors based on their emotional connection to the colors (Palmer & Schloss, 2010). Previous studies have only shown color preferences changing specifically due to object-color associations (Strauss et al., 2013; Taylor & Franklin, 2012). This study did not compare gender differences and took object association a step further in comparison to previous studies by only looking at food association related to color preference.

Color preference is considered to be the tendency for an individual or a group to prefer some colors over others, including a favorite color.

Object association to color is when people

generally like colors to the degree that they like the objects associated to the color (Taylor & Franklin, 2012). Color association can be considered what a person might associate a color with, and this can impact overall color preference. For example, when he thinks of the color blue, he may associate it with the ocean and because he likes the ocean he likes the color blue. Food preference is similar to color preference, in that different people prefer different foods. Color emotion “is defined as feelings evoked by either colors or color combinations” (Palmer & Schloss, 2010, p. 2).

There is a great connection between color and food. Garber, Hyatt, and Starr (2000) conducted a study suggesting how significant color is when consuming food. This study discussed how color directly affected food choice and food liking rather than the taste of the food. Another discussion of color involves the ecological valence theory (EVT). The EVT “proposes that color preferences arise from affective responses to color associated objects: people like/dislike colors to the degree that they like/dislike the objects that are characteristically associated with that color” (Taylor et al., 2013, p. 916). Strauss et al., 2013 also supports the EVT as evidenced by findings.

Therefore, specifically, we believe that the more a person likes a specific food, the more he/she will also like the color associated with that food. The cone-contrast theory “posits that color preferences

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arise from hard wiring in early visual processing; the color-emotion theory suggests [color preference] arises from the emotional content of colors” (Strauss et al., 2013, p. 935). Also, the color emotion-theory “can be linked measuredly to color preferences if colors are preferred to the extent that viewing them produces positive emotions in the observer” (Palmer & Schloss, 2010, p.2). EVT and color-emotion are linked in certain ways but are also significantly different. For the ecological valence theory, a colored object is either liked or disliked which is followed by a person matching the liking or disliking of the object to their overall liking or disliking of the color itself.

Whereas, with the color-emotion theory, a person produces an emotion based off of the color shown, which then determines his liking or disliking of the color. There is debate over which theory is more influential over color preference, but both have a strong influence.

Taylor and Franklin (2012) aimed to understand sex differences in relation to color-object associations and to determine if the relationship between color preference and object association is equal for males and females. Males were compared to females while performing two tasks. The first task measured color preference on a 5-pt rated scale while, the second task, WAVE, measured object preference by having participants list objects associated with the different colors. Three different sets of colors were used for comparison: saturated, light, and dark. All males and females were presented with all colors and tasks. The results stated, “we found the association between the WAVE and color preference to be significantly weaker for females (45%) than for males (74%)” (p. 194). Overall, the researchers found some gender difference in color preference, and they also discovered that there is a negative relationship between the number of objects associated with a color and overall preference of the color. This study is important to the current study because it shows there could be a gender difference when comparing color preferences and could provide another explanation to the results of our study.

Similarly, this study allows for us to better

19 understand that the more objects associated with a color, the less the color is liked, beyond the influence of object association content.

Granger (1955) wanted to better understand why people like certain shades of colors more and why certain shades are more appealing than others to the human eye. Sixty different color sets, with roughly seven different shades of each color, with different balances of hue (offsets of normal colors), value (light or dark), and chroma (purity or intensity of color or strength) were presented to all fifty participants, half females and half males. All participants were asked to rank colors based on liking, regardless of their association with objects or people to the specific colors. Overall, this study found that color preference is not impacted by personal taste (relativistic meaning) but is impacted by object stimulus properties (qualitative meaning).

This study also showed that cultural influence could have a greater impact on color preferences than objective, biological influences. Finally, this study showed there was no difference between genders for color preference. From this study, object stimulus continued to have a great impact on color preference despite this specific study attempting to prevent its impact. Similarly, this study proves the importance of different shades of color and how different lighting can make a huge difference in overall preference of a specific color.

Strauss et al. (2013) wanted to determine if color preference can be changed when presented with colored-objects that are either liked or disliked. Forty-six participants were used in this experiment. All 46 were initially asked to rate their preference on thirty-seven colors. Then, the fortysix participants were divided into two groups. The first group saw ten positive green and ten negative red objects, while the second group was presented with ten positive red and ten negative green objects. Both groups were also shown the same twenty images of other-colored neutral objects.

After being presented with the objects, all forty-six participants once again rated their color preference to see if overall preference changed. Participants in the red positive, green negative group showed increased preference for red over green while the

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green positive, red negative group showed a decrease in preference of red over green. This study shows the effect of posttest minus pretest, as well as the importance of negative and positive objects associated with certain colors. Overall, Strauss et al. provided evidence to support the ecological valence theory. This is known because in the study, participants liked colors more after being presented with positive/liked objects (not involving emotion).

Similarly, those participants presented with colored objects that were disliked, they also disliked the color when asked to state their preference of the color. This study allows for future studies, like ours, to be completed in determining if specific themes of objects (like food) have an even greater impact on changing color preference.

Taylor et al. (2013) determined if adult color preferences begin early in life or if color preference is continuously changing throughout life. This study first experimented with infants to determine overall color preferences of infants to then compare to overall color preferences for adults. The adults were given the same eight colors as the infants (dark and light colors of red, yellow, green and blue), along with four different hues for each color to increase sensitivity. There were 123 participants who were assigned to one of three tasks, either the preferential-looking task, preferential-choice task, or the preferential-rating task. Adults looked and preferred different colors than the infants did, which shows that color preference is not innate and instead, changes throughout one’s lifetime.

However, both infants and adults prefer pink equally, in general. Also, the results for the adults showed that they look longer at colors they like than at colors they dislike, which did not occur for infants. This helps explain that color preference is not a fixed opinion but instead a flexible one that can change based on the situation. Overall, this study is important because it proves that adults can change their color preferences over time, making the cone-contrast theory an unlikely explanation.

This can then be related to our study of saying food can change color preference throughout the entire life-span and also in a short period of time such as pretest to posttest.

20 The EVT “proposes that color preferences arise from affective responses to color associated objects: people like/dislike colors to the degree that they like/dislike the objects that are characteristically associated with that color�

(Taylor et al., 2013, p. 916). When the participants initially rated the liking of colors on a 5-pt scale they were not thinking about the connection with food. However, once they were instructed to think of foods related to certain colors, their color liking rating should change. Along with this, we also wanted to better understand if color-emotion theory has an impact on color preference. In order to do this we asked participants to state what emotion they were feeling when shown each color; this was done prior to the food association task. In the current study to extend the results found by Strauss et al. (2011) we chose to focus on liking and disliking of food, rather than an inanimate object related to color.

Research explains object recognition related to color but our study is unique because we are focusing specifically on colored food recognition to determine changes in color preference over time.

Some studies (Taylor & Franklin, 2012) do not include object association but the current study does, similar to the experiment done by Strauss et al. (2013), and specifically we focus on only food objects related to color. Along with food preference impacting color preference, we also used previous research to help us question participants on their emotional response to colors (Palmer & Schloss, 2010). Palmer and Schloss (2010) used specific words to ask emotional connections to different shades of colors. For this study, we are allowing participants to write their own emotional connections to one shade of the four different colors to try to tease apart the EVT and coloremotion theories.

Color preferences are impacted by various object associations, one of which includes food association. Participants were divided into three different groups. One group was instructed to list one to three liked foods related to each color, one to three disliked foods related to each color, or instructed to simply list one to three foods related

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to each color. Before being asked to list foods, participants were shown one color at a time and asked to write the first emotion they felt when seeing the color. Then, participants were asked to rate color liking of four colors on a 5-point scale, rated 1 (highly disliked) to 5 (highly liked).

Following this, participants listed foods based on specific instructions. Then, participants were asked to rate liking of each food on a 5-pt scale, rated 1 (highly liked) to 5 (highly disliked). This was done in order to understand just how much each participant liked the food, as it could impact their posttest ratings of each color. Following this, participants were again asked to rate color liking of the same four colors used in the pretest and also for the food on a 5-point scale, rated 1 (highly disliked) to 5 (highly liked).

We expected to find some difference in posttest minus pretest for color-liking on a 5-pt scale between the three food association instruction groups. We expected this to happen because studies have shown that color preference changes with object association and/or color-emotion links (Granger, 1955; Strauss et al., 2013). Strauss et al.

(2013) showed that liked/disliked object association has a positive relationship with color preference. From this, we believed we would find the same results in our study even though we specifically used liked/disliked food association.

Similarly, Taylor et al. (2013) proved that color preference can change overtime, which also supports our hypothesis in that we believed there would be a change between posttest and pretest because color preference is influenced by other situations such as, in our experiment, acknowledging certain liked/disliked foods.

Methods Participants. A total of 31 participants were used in this study. There were 8 (26%) males and 22 (71%) females and one person did not identify as male or female. 31 (100%) of the participants were white/Caucasian. All participants were college students from a rural, Catholic Institution in Central Pennsylvania. All undergraduate students were invited to participate in this study via email

21 via the Blackboard site. 1,696 undergraduate students were invited and 32 people participated in the study resulting in a 2% response rate. The low response rate could be due to the study being an experiment, needing to be taken in person, unlike an online survey which is more convenient. 41% of the participants were from a convenient sample as they were offered extra credit in their Psyc101 class. The mean age of participants was 19 but the ages ranged from 18-22. There were 13 (42%) freshman, 7 (23%) sophomores, 10 (32%) juniors, 1 (3%) senior. Volunteers were obtained by their own free-will after receiving an email with details about the experiment and when the study would be taking place. We offered a chance in a raffle for a $10.00 Sheetz gift card if participated in our study.

All participants were not colorblind and had corrected vision or normal vision. We also indirectly inferred if participants have an eatingrelated disorder or are currently on a diet due to responses in the demographic survey. In order to avoid asking a sensitive dietary question, we asked all participants to guess about how many calories they consume per day. We then removed participant’s data that stated they consume -2 standard deviations below the mean calories eaten daily. For Females 19-30 the average calorie intake is 2,000 calories and for males 19-30 it is 2,400 calories. For females 31-50 it is 1,800 calories and for males it is 2,200 calories. Finally, for females 51+ it is 1,600 calories and for males it is 2,000 calories (How Many Can I Have?, 2011).

Materials. A self-created survey was used to measure color preference. See the complete survey in Appendix 1. The color squares used for the study were to give a visual stimulus to help with rating of colors to determine color preferences. We used four Munsell color chips (see Appendix 2), C16 green, F1 red, B12 yellow, and C7 orange (Berlin & Kay, 1969).We also used the Munsell color chip G gray for a background between colors (Berlin & Kay, 1969). We used a distractor task similar to “Color Flow� (Big Duck Games LLC, 2012) but instead of connecting colors, participants connected shapes so as not to be influenced by colors (see Appendix 3).

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This was used to prevent participants from becoming bored or tired during the study and to help participants forget earlier pretest color ratings to the highest extent possible. The email invitation to all participants was sent out via Novell GroupWise 12.0.2 (2013). We used DLP Optoma Projection System and a white projector screen with a height of 44.5 inches and width of 80 inches in order to show all colors to participants at the same time and in the same visual field. Participants sat no closer than 88 inches from the screen. We also used Windows PowerPoint (2014) to organize and automate the colors when showing the participants.

Due to there being little research and consistency in standard colors from the Munsell Color chart we conducted a pretest or pilot study to determine which colors represented the most widely accepted, true green, yellow, orange, and red. There were thirty-nine pretest participants obtained from various college students that did not later participate in the study. Twenty-five (64%) people liked C16 Green the best. Nine (23%) people liked D16 Green the best. Five (13%) people liked E16 Green the best. Zero (0%) people liked F16 Green the best. Thirty-five (90%) people liked B12 Yellow the best. Three (8%) people liked B11 Yellow the best. One (3%) person liked B10 Yellow the best. Zero (0%) people liked C11 Yellow the best. Fifteen (38%) people liked C8 Orange the best. Twenty-four people (62%) liked C7 Orange the best. Zero (0%) people liked D5 Orange the best. Zero (0%) people liked D8 Orange the best. Twenty-five (64%) people liked F1 Red the best. Seven (19%) people liked G1 Red the best. Four (10%) people liked E1 Red the best.

Three (8%) people liked H2 Red the best. Majority of participants, 64%, agreed that C16 Green was the best example of pure green. Majority of participants, 90%, agreed that B12 Yellow was the best example of pure yellow. Majority of participants, 62%, agreed that C7 Orange was the best example of pure orange. Majority of participants, 64%, agreed F1 Red was the best example of pure red. See Appendix 4 for the various rating scales used as well as a complete list

22 of results for pretest/pilot study. We chose the best color based on the majority of participants that chose it out of all the other color options. All color squares were projected on the screen with a height of 43 inches and a width of 75 inches.

Design and Procedure.

We used three different versions of the self-created survey. One of the three versions was randomly assigned to participants, each survey having different instructions (see Appendix 1 for complete survey).

We studied change in color preference on a 5-pt scale, rated 1 (highly disliked) to 5 (highly liked) between posttest minus pretest for all participants.

In order to test this there were three different instructions for listing of foods. Within our eight different times of offering the experiment, two groups were asked to list no more than three foods liked related to each color, three groups were asked to list no more than three foods disliked related to each color, and three groups were asked to simply list no more than three foods related to each color as controls. All foods were listed in relation to a visual color square of one specific color. We also studied color emotion by asking all participants to write down what emotion they were feeling when they saw each color individually on the screen.

This was the first question asked on the survey, before participants were asked about color liking or the food association. From this, we were able to look at whether the emotion they felt was positive or negative and how it related to their liking of the color in the posttest.

This was a survey experiment and was a between-subjects, randomized experiment design in order to allow there to be different instructions to determine if that influences color preference. We used random assignment to distribute participants to one of three different instruction surveys, using two testing sessions per condition. This way we were able to read the instructions out loud as well as them being available on the survey and participants were able to ask questions if they were uncertain while completing the study without possibly giving away that there were different instruction sets to the others participating.

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Participants received an email inviting them to participate in the study. The email stated where and when the study would be held. When participants arrived to one of the eight sessions, they were handed a packet. This packet included the demographic survey, informed consent, and one of three experimental surveys. They first answered the demographic survey questions. They then proceeded to sign the informed consent. After this, they began the experiment. They were first shown one color at a time and were asked to write down what emotion they were feeling as we were showing the color. Participants were given the following instructions, “When we show a color on the screen, please immediately write down the emotion you feel from seeing the color. Please answer as quickly and automatically as you can.

You will have fifteen seconds to write down one emotion for each color”. After all emotions were generated, they were then asked to rate the liking of four colors shown one by one on the projected screen based off of their preference of liking (1=highly disliked, 2= disliked, 3= neutral, 4=liked, 5=highly liked). Participants were given the following instructions: “Please rate your personal liking of the color shown on the screen. You will be shown one color at a time for ten seconds so carefully examine the color for the full ten seconds.

Then you will be asked to rate the color while the gray screen is shown for five seconds after each color. Please answer as quickly and automatically as you can. (1=highly disliked, 2= disliked, 3= neutral, 4=liked, 5=highly liked).”

We then showed them each color individually a third time using the projector, with a gray background in between each shown color to allow time to rate one to three foods each participant thought of. We asked them to list one to three, but no more than three foods related to the color shown on the screen (specific instructions based on each condition). There was a time limit of one minute on this and participants were encouraged to answer quickly and instinctively per research that proves this to be most effective (Taylor & Franklin, 2012).

For this part of the experiment the survey papers were folded in half and participants were told not to

23 unfold until instructed to do so. The left side of the paper was where the participants listed the foods.

Then, participants were asked to unfold the paper and were asked to rate how much they like each food on the right side of the paper (1=highly liked, 2=liked, 3=neutral, 4= disliked, 5=highly disliked) while the screen appeared gray. They completed this task for all four colors. For example, the color green was shown, which was when participants were instructed to list foods on the left side of the paper, then the gray screen appeared which was when participants were instructed to unfold the paper and rate liking of each food related to the color green. Then, a new color was shown with the same procedure previously explained until all four colors were shown. The rating of each food was used as a way to confirm participants followed the directions of what type of foods to list. Unfolding procedure was as not to initially inform the participants we would also be asking them to rate the foods and also in order for the participants to not try to work ahead during the study. Finally, participants were asked, again, to rate liking of the four colors one at a time, shown on the screen and rated on a 5pt scale. This completed the study.

All participants, after listing and rating two of the four colors, were instructed to complete the “Shape Flow” distractor game. The instructions given to the group asked to list no more than three liked foods related to each color were given the following instructions: “Please list 1-3 liked foods you think of when you see the color on the screen.

Do your best to list three liked foods. If you run out of time, do not worry and just prepare for the next portion of the survey. You will have one minute to list 1-3 foods. Please answer quickly and automatically”. The group asked to list no more than three disliked foods related to each color were given the same instructions except the word like was changed to dislike. The group asked to simply list foods related to each color were given the same instructions except the word like (or dislike) was not included. After this, all three groups received the instructions to: “Please unfold your response sheet now and rate your liking to the right of each

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food listed. You will have twenty-five seconds to rate each food. Please answer quickly and automatically. (1=highly liked, 2=liked, 3=neutral, 4= disliked, 5=highly disliked)�. All groups were given the same posttest instructions as the pretest: “Please rate your personal liking of the same colors as shown earlier in the study. However, we do not expect you to rate the colors the same as before. If you do that is okay but please respond quickly and automatically without trying too hard to recall your earlier answer. You will, again, have ten seconds of one color shown on the screen, followed by a five second break where you will rate each color previously shown with a gray color shown on the screen�. Refer to Appendix 1 for all information used on the survey.

The entire experiment was automated as we timed each slide on the PowerPoint and wrote down instructions to be read at each session in order to keep all eight sessions the same. The demographic survey took roughly three minutes, the color emotion section took about one minute (fifteen seconds for each color), the participants were given ten seconds to rate each color in the pretest and were given five seconds of gray screen between each color; therefore, the total pretest took roughly one minute. Color A was shown for ten seconds, followed by five seconds of gray screen.

Color B was shown for ten seconds, followed by five seconds of gray. And so on for colors C and D. The order of the colors was randomized. Then, participants were given one minute to list no more than three foods for each color and then were presented with a gray background for twenty-five seconds where they were told to rate liking of each food for each color. This took a total of five minutes. Again, participants were shown on the screen Color A for one minute which is when they listed the foods, and then shown the gray screen for twenty-five seconds which is when they rated their liking of Color A foods listed. This continued for Colors B, C, and D. The participants were, again, given ten seconds to rate each color in the posttest and were given five seconds of gray screen between each color; therefore, the total posttest took roughly one minute. The distractor task took

24 roughly three minutes and was done on the survey paper. During this time, a gray background was shown on the screen. The experiment took a total of 15-25 minutes to complete. This experiment took place in a classroom setting with enough spacing between each participant for comfort and privacy.

There were eight different times offered to complete the survey. Two of the settings were given the instructions to list no more than three liked foods, two of the settings were given instructions to list no more than three disliked foods, and two of the settings were given the instructions to simply list no more than three foods.

All eight settings were given a different order of which color to list the foods for first, second, third, and fourth. We randomized this in order to avoid order effects. We also counterbalanced the pretest and posttest order of colors so as to increase chance of forgetting pretest ratings. The lighting of the room was specifically determined to make sure the colors were visible in the appropriate way for all eight sessions. We had no lights on during the experiment and we closed all blinds in order to keep the colors as normal looking as possible.

Scoring. Due to wanting to determine if there is change over time in color preference when asked to list liked or disliked foods of various colors, we calculated the difference in scores of the pretest and posttest. Also, we wanted to analyze if emotion contributes to color preference. We scored the emotions by determining if it was positive or negative. For the disliked food group, we separately analyzed positive and negative emotions to colors to see if any different results occurred.

Then, we looked at the posttest ratings and analyzed the percentage decrease, if any, of the color rating. Similarly, for the liked food group, we separately analyzed positive and negative emotions.

This was done to tease apart the color-emotion theory and ecological valence theory or to determine they are both influential to color preference. Then, we looked at the posttest ratings and analyzed the percentage increase, if any, of the color rating. For the third group (listing any foods), we analyzed all four colors and their percentage

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25

change in relation to the emotions listed and the types of foods they listed.

Results All effects significant at p .05 were reported.

All post hoc tests were calculated using the Dunnetts T3 test because we had equal variances but it was a near trend. We calculated the means by adding up across the subjects and then dividing by the total number of subjects. To find the mean difference we took time 2 mean minus time 1 mean. We dropped one participant because they were colorblind and we did not want anyone who was colorblind to participate in our study.

A 1-way ANOVA was run on mean difference color rating score from time one to time two to compare the liked color instructions, the disliked color instructions, and the normal (control) color instructions. F(2,28) = 5.11, p=.013. The liked color instructions elicited a more positive mean difference color rating score, the disliked color instructions elicited a more negative mean difference color rating score, but compared to the control there was little difference of mean difference color rating scores. Comparing liked instructions to disliked instructions there was a big difference of color rating scores. ES = .27 which is a medium effect size. We found equal variances but a near trend for the Levine’s variance test. F(2,28) = 2.91, p = .071.

Food Instructions influenced mean difference color rating score from Time 2 to Time 1 (see Table 1). There was a greater mean difference in color rating score between the liked and disliked instructions and a small mean difference color rating score between the liked/disliked and control instructions.

Food Instructions Mean Difference

Control

Liked Instructions

Disliked Instructions

Mdiff

.03

.41

-.23

SEdiff

.058

.189

.137

Table 1. Mean Difference of Color Rating from Time 2 to Time 1 in Food Instructions

Other Results An ANCOVA was run on mean difference color rating score from Time 2 to Time 1 to tease apart EVT and color-emotion theory and to compare the liked color instructions, the disliked color instructions, and the normal (control) color instructions, while analyzing the covariate for color-emotion association rating at time 1. For the Overall model: F(3,27) = 3.29, p =.036. The overall model was significant. For the covariate (emotion): F(1,27) = .014, p = .906. The covariate of coloremotion association rating at time 1 was not significant. For the Main Effect: F(2,27) = 4.52, p = .020. The liked color instructions elicited a more positive mean difference color rating score, the disliked color instructions elicited a more negative mean difference color rating score, but compared to the control there was little mean difference of color rating scores. Comparing liked instructions to disliked instructions there was a big mean difference of color rating scores. ES = .268 which is a medium effect size.

Food Instructions influenced mean difference color rating score from Time one to Time two, even with color emotion association covaried out (see Table 2).

Food Instructions Mean Difference

Control

Liked Instructions

Disliked Instructions

Mdiff

.022

.41

-.23

SEdiff

.15

.15

.15

Table 2. Mean Difference of Color Rating from Time 2 to Time 1 in Food Instructions [Note: Covariate of coloremotion association at Tiime 1 was not significant.]

Discussion We were able to support our main hypothesis that the mean difference in color rating from Time 2 to Time 1 depends on food instructions (liked instructions, disliked instructions, or normal (control) instructions). We found significant difference for the mean difference color rating score between the liked instructions and disliked instructions. However, we found little significance for the mean difference color rating scores between

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the liked/disliked instructions each when compared to the control instructions. We also found that the covariate color emotion rating from time 1 does not significantly impact overall color rating. From these results, we can reject the color-emotion theory in that a person’s emotional response to color does not impact their overall color liking.

However, from our results, we can support the ecological valence theory (EVT) that color preference is affected by associated objects and color, specifically food as the object.

There are a few flaws with our study. The first flaw is that there were many distractions in the room due to noise from other people in the room and from outside. This could have impacted the participants’ responses because they were distracted with something else. Another flaw is that some participants appeared to not follow our directions. Our study was extremely particular in that we needed participants to respond quickly and automatically in order for their answers to be true, valid answers. Some participants did not follow the directions exactly as we stated them which could have negatively impact their results.

Garber et al. (2000) discovered how color directly affects food preference beyond the taste of the food. These results are inconsistent with our results. We found that color preference can temporarily change liking or disliking of food, based on thoughts, whereas the past study found that food preference depends on liking or disliking of color related to each food. Taylor and Franklin (2012) discovered that there is an inverse relationship to number of objects associated with a color and liking of the color. They also briefly discovered that people seem to generally like “colors to the degree that they like the objects associated with those colors” (p. 196). However, their results were extremely basic with minimal proof of object association relating to color preference. These results are consistent with our results as we also found, in a more concrete way, that object (food) association does relate to color preference.

Granger (1955) looked at color preference but tried to take away the option of associating color

26 with objects or people. However, he found that even telling someone not to think about objects or people the participants still related the colors to objects or people. This study, therefore, proved that it is impossible for people to not relate objects or people (or food) to colors. This study is consistent with our results because we also found that food (object) association does impact color preference.

Strauss et al. (2013) found that participants presented with red positive/green negative objects increased their preference for red over green, while the green positive/red negative objects decreased the participants preference for red over green. Their study directly supports the ecological valence theory that: being presented with something positive will increase liking of the color. These results are directly consistent with our results because we, too, found that when given instructions to list disliked food, participants’ overall mean difference color rating score decreased. Similarly, when given instructions to list liked foods, their overall mean difference color rating score increased. Our study is also able to support the ecological valence theory, similar to Strauss et al.

(2013).

Taylor et al. (2013) discovered color preference has the potential to change from childhood to adulthood. These results are consistent with our results because we also found that color preference could change from pretest to posttest. Both studies show that color preference is not fixed but instead flexible and able to change. Palmer and Schloss (2010) discovered that positive emotions towards a color increase overall color preference. Their results supported the color-emotion theory. Our results are inconsistent with their results in that we found that emotion towards a color does not impact overall color preference above and beyond foodcolor association. From this, we do not support the color-emotion theory.

Overall, our study is able to support the ecological valence theory and reject the coloremotion theory. As previously mentioned, our study was able to tease apart both theories which is able to help people better understand why our color preferences change. Also, none of the studies

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27

previously mentioned used food as the object associated with color. We are able to prove that food color impacts overall color preference. These results could be used to help explain why we prefer certain colors and why our color preferences change based on different situations. The results could also help people realize that what they eat could impact what colors they prefer.

In the future, it might be beneficial to have a posttest color emotion question, similar to the pretest to see if that would make a difference in overall color preference. Also, in the future it might be beneficial to choose colors other than, red, green, orange, and yellow to determine if the theories hold true for all colors.

Appendix 1 Question 1 When we show a color on the screen, please immediately write down the first emotion you feel from seeing the color.

Please answer as quickly and automatically as you can. You will have fifteen seconds to write down one emotion for each color.

Red: Orange: Green:

Question 3: Red Question 4: Orange Question 5: Green Question 6: Yellow IMPORTANT: Please keep paper folded so only the left half of the sheet is visible until you are told to look at the right half folded beneath.

Please list 1-3 foods you think of when you see the color on the screen. Do your best to list three foods. If you run out of time, do not worry and just prepare for the next portion of the survey. You will have one minute to list 1-3 foods. Please answer quickly and automatically.

Please rate your liking to the right of each food listed. You will have twenty-five seconds to rate the foods. Please answer quickly and automatically.

(1=highly liked, 2= liked, 3=neutral, 4=disliked, 5=highly disliked).

1.

1-------2-------3--------4-------5

2.

1-------2-------3--------4-------5

3.

1-------2-------3--------4-------5

Question 7 Please rate your personal liking the same colors as shown earlier in the study. However, we do not expect you to rate the colors the same as before. If you do, that is okay but please respond quickly and automatically without trying too hard to recall your earlier answer. You will, again, have ten seconds of one color shown on the screen. Followed by a five second break where you will rate the each color previously shown with a gray color shown on the screen.

Yellow:

Highly disliked

Question 2 Please rate your personal liking of the color shown on the screen. You will be shown one color at a time for ten seconds so carefully examine the color for the full ten seconds. Then you will be asked to rate the color while the gray screen is shown for five seconds after each color. Please answer as quickly and automatically as you can. (1=highly disliked, 2= disliked, 3= neutral, 4=liked, 5=highly liked).

Highly disliked

Highly liked

Red:

1-------2-------3--------4-------5

Orange:

1-------2-------3--------4-------5

Green:

1-------2-------3--------4-------5

Yellow:

1-------2-------3--------4-------5

Highly liked

Red:

1-------2-------3--------4-------5

Orange:

1-------2-------3--------4-------5

Green:

1-------2-------3--------4-------5

Yellow:

1-------2-------3--------4-------5

* Order of colors was counterbalanced within the study. ** Two other sets of instructions were used: “Please list 1-3 liked foods you think of when you see the color on the screen. Do your bed to list three foods. If you run out of time, do not worry and just prepare for the next portion of the survey. You will have one minute to list 1-3 foods. Please answer quickly and automatically.” And “Please list 1-3 disliked foods you think of when you see the color on the screen. Do your bed to list three foods. If you run out of time, do not worry and just prepare for the next portion of the survey. You will have one minute to list 1-3 foods. Please answer quickly and automatically.

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Appendix 2

28 Appendix 4 Please choose the color that best represents the true color yellow.

A. B. C. D.

Please choose the color that best represents the true color red.

A. B. C. D.

Please choose the color that best represents the true color orange.

A. B. C. D.

Appendix 3 Instructions: Draw a line to connect matching shapes, creating a flow. None of your lines are allowed to cross or overlap other lines. Pair all shapes, and cover the entire board with lines to solve the puzzle. You will have three minutes to work on this. It is okay if you do not finish it.

Please choose the color that best represents the true color green.

A. B. C. D.

Yellow Color A (B10)

Color B (B11)

Color C (B12)

Color D (C11)

1 (3%)

3(8%)

35 (90%)

0 (0%)

Red Color A (F1)

Color B (H2)

Color C (G1)

Color D (E1)

25 (64%)

3 (8%)

7 (19%)

4 (10%)

Orange Color A (C7)

Color B (C8)

Color C (D5)

Color D (D8)

24 (62%)

15 (38%)

0 (0%)

0 (0%)

Green Color A (C16)

Color B (D16)

Color C (E16)

Color D (F16)

25 (64%)

9 (23%)

5 (13%)

0 (0%)

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Works Cited Berlin, B. & Kay, P. (1969). Basic Color Terms. Berkeley, CA: University of California Press.

Big Duck Games LLC (2012). Flow Free (2.8) [mobile application software]. Retrieved on Dec. 4, 2014 from GOOGLEPLAY. Garber, L.L., Hyatt, E.M., & Starr, R.G. (2000). The Effects of Food Color on Perceived Flavor. Journal of Marketing Theory and Practice, 8(4), 59-72. Retrieved on Dec. 4, 2014 from PROQUEST database.

Granger, G.W. (1955). An Experimental Study of Colour Preferences. The Journal of General Psychology, 52(1), 320. Retrieved on Oct. 13, 2014 from ILLIAD database.

Guilford, J.P. (1940). There is System in Color Preference.

Journal of the Optical Society of America, 30(9), 455-459.

Retrieved on Oct. 13, 2014 from ILLIAD database.

How Many Can I Have? (2011). Retrieved Jan. 20, 2015 from http://www.phd5.idaho.gov/quickinfo/quickInfoHowManyCanIHave.php.

Madden, T. J., Hewett, K., & Roth, M. S. (2000). Managing images in different cultures: A cross-national study of color meanings and preferences. Journal of International Marketing, 8(4), 90-107. Retrieved on Oct. 13, 2014 from PROQUEST database.

Palmer, S., & Schloss, K. (2010). An Ecological Valence Theory of Human Color Preference. PNAS, 107(19).

Retrieved on Jan. 20, 2014 from PROQUEST database.

Strauss, E. D., Schloss, K. B., & Palmer, S. E. (2013). Color preferences change after experience with liked/disliked colored objects. Psychonomic Bulletin & Review, 20(5), 935-43. Retrieved on Oct. 13, 2014 from PROQUEST database.

Taylor, C., & Franklin, A. (2012). The relationship between color-object associations and color preference: Further investigation of ecological valence theory. Psychonomic

29 Bulletin & Review, 19(2), 190-7. Retrieved on Oct. 13, 2014 from PROQUEST database.

Taylor, C., Schloss, K., Palmer, S. E., & Franklin, A. (2013).

Color preferences in infants and adults are different.

Psychonomic Bulletin & Review, 20(5), 916-22. Retrieved on Oct. 13, 2014 from PROQUEST database.

Sarah Polito (’16) is an Occupational Therapy major with a minor in Psychology. She is a member of Phi Eta Sigma National Honor Society and Psi Chi International Honor Society in Psychology. Sarah worked as a Peer Minister for two years and Peer Minister Coordinator for one year. She is also a member of the Student Occupational Therapy Association. Sarah will graduate with her Master of Occupational Therapy Degree in 2017 Alyson Pritts (’16) is an Occupational Therapy major with a minor in Psychology. She has been actively involved in the Theta Phi Alpha sorority where she held the position of New Member Educator and held a spot on the SFU dance team during her freshman to sophomore years. Alyson also had fieldwork experiences at Camp Emerge in Millville, PA and at the Crichton Center in Johnstown, PA. After graduation in Alyson hopes to complete her Master’s Degree in Virginia where she will be completing her fieldwork experience.

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2015 Office of Student Research Awards for Research Excellence The goal of the Office of Student Research is to foster the culture of student research at Saint Francis University. Student research has become an important part of the education of an increasing number of Saint Francis University students. Each year the Office of Student Research recognizes students who have shown exemplary effort conducting research at the University. The following students were the recipients of the 2015 Office of Student Research Awards for Research Excellence, given out at the Fifth Annual Saint Francis University Research Day on November 19, 2015:

For research excellence in the School of Arts & Letters, Christie Olek, who conducted her research in Dr. Lori Woods’ research group, detailing the experiences of the first graduates of the "Woman's Medical College of Pennsylvania", the first college founded to offer the MD degree to women. For the project, she traveled to Philadelphia where she conducted research at Drexel University's "Legacy Center", a special collection, which houses the Woman's Medical College Archive. She was later awarded a School of Arts & Letters Intrepid Research grant that funded a second trip to Philadelphia, and the product of this research was a paper, "Opposing Obstacles and Overcoming Opposition: The First Graduates of the Woman's Medical College of Pennsylvania", which was published in the Fall 2015 issue of Spectrum. Christie presented her research in poster form at last year's SFU Research Day.

For research excellence in the School of Business, Jeremy Merich, who is a senior accounting and finance major. Jeremy has spent the last two years working on his honor's thesis exploring an investment strategy to achieve abnormal returns. Dr. Maggie Garcia and Dr. Ed Timmons advised his project. His strategy focuses upon buying stocks near the announcement of quarterly earnings. Using regression analysis and data for 1500 companies for the month of October 2015 (entered by hand into Excel), Jeremy found evidence of substantial abnormal returns for periods very close (1-day) and further away (30-days) for investors pursuing his strategy. Jeremy has presented his preliminary research at the Issues in Political Economy conference and plans to attend and present again in Spring 2016. He also plans to extend his research using additional data. Jeremy was hired by JP Morgan Chase in Delaware after his internship with the firm in the summer of 2015 and will begin working there immediately after graduation.

For research excellence in the School of Health Sciences, Brittany Swartzwelder, who conducted her research in Dr. Curt Kindel’s research group. Her project is titled "The effects of varying hip flexion and external rotational angles on the production of isometric hip external rotation torque in healthy adults." She presented her work recently at the PA Physical Therapy Association's Annual Conference at Seven Springs, PA. She was an integral part of the research team and is very deserving of this award.

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For research excellence in the School of Health Sciences, Jake DeMedal, who conducted his research in Dr. Bill Stodart’s research group. His project is titled “The relationship between surface electromyographical activity and force production of the infraspinatus muscle in shoulder rehabilitation exercises.” This was a challenging undertaking involving the acquisition and analysis of isometric strength and EMG recordings at 5 different positions for 3 different exercises. The process was technically demanding and time consuming, but Jake and his group displayed considerable amount of independence in carrying their research out. Jake’s study was accepted for platform presentation at PPTA Annual Conference, where it was subsequently awarded "Best Research 2015".

For research excellence in the School of Sciences, Cristina Marcillo, who started her research career in an unusual way. Before even setting foot in the research lab, she translated an article

by SFU professor Dr. Bill Strosnider for publication in the Chilean journal Avences en Ciencias e Ingeniería. Since then, Cristina has continued working with Dr. Strosnider focusing on cotreating acid mine drainage and municipal wastewater, ultimately presenting the results of her work at the 4th International WaTER Conference at the University of Oklahoma this past September. In addition, Cristina participated in the Research Experience for Undergraduates program at Clarkson University in upstate NY this past summer.

For research excellence in the School of Sciences, William Shee, who is a junior chemistry and mathematics major. He has been working on his research project in Dr. Balazs Hargittai’s research group for the past two and a half years, designing the synthesis of novel proline derivatives with basic side-chains. William has been a very active participant in the project, suggesting modifications to the methods used and focusing the direction of the project to use more contemporary and more environment-friendly procedures. William presented his research as posters at the past two Annual SFU Research Days, and gave a seminar detailing the project at the 2015 Research Day.

Call for papers Sub m ission G uid elines The purpose of SPECTRUM is not merely to disseminate new results, but also to inform and enlighten.

Our readership is a general and multidisciplinary audience who may not be an expert in your field of study. Consequently, please explain all pertinent concepts essential to understanding your article as well as any concepts that might not be common knowledge. Please submit your file in Microsoft Word format as an attachment to the following email address: spectrum@francis.edu. The text should be single spaced, using 12-point Times New Roman font. Please use italics, rather than underlining, for emphasis.

O r ganiz at ion of M anuscr ip t s SPECTRUM is an interdisciplinary journal accepting submissions from the natural sciences, the humanities as well as the professional schools (health sciences and business), therefore, the structure and style of each manuscript will differ from discipline to discipline. Regardless, all submissions must provide a cover sheet, a thorough introduction of the problem your research addresses, the conclusion(s), result(s) or findings of your research, as well as some form of bibliographic citation. Below are the general guidelines for these requirements, some of which may not apply to your area of research.

C ov er Sheet Title Names and departments of undergraduate researcher(s) and faculty advisor(s) Abstract (200 – 300 words) Six key words Int r od uct ion Include general background of the relevant field and the larger problem your research addresses as well as its relevance within the field. In addition, explain what prompted your investigation, a summary of previous findings related to your research problem and what contributions your project brings (or was expected to bring) to the issue.

M et hod s and M at er ials (If ap p licab le) Summarize important methods and materials used in your research.

R esult s/C onclusions Give detailed report of the results and or conclusions reached through your research.

Discussion Results should be evaluated in the context of general research problem, the implications of which should be explained with conclusions, predictions or suggestions (if applicable) for further study.

T ab les (if ap p licab le) Create tables in Microsoft Word format and insert into general text accompanied by a table legend. Each table needs a number based on its appearance in the paper, where it is referenced.

Figur es (if ap p licab le) Please submit figures at the end of the article, one image per page; we will fit these in as we organize the manuscript. Each figure needs a number (the figures shall be numbered consecutively in the order of their appearance in the paper) and a title. SPECTRUM will be printed black and white, but there will be an online version where figures submitted in color will appear in color.

R efer ences You may use any referencing style you choose so long as it is a standard format or your discipline (IEE, APA, ACS, PubMed) and that you use it consistently and to the appropriate bibliographical standards.