QRM: Observational Between-Subjects Non-Experimental Design

1 Evaluate Quantitative Research Methods for Potential Studies Part 1 Scenario 1 – Observational Between-Subjects Non-Experimental Design The first scenario is about understanding a phenomenon that involves the behavior of two groups of students. From the onset, the researcher would seek to observe the behaviors, and they would be time-related. The developed research question to allow one to proceed with the research was as follows. Do undergraduate college psychology students watch more TV than psychology graduate students? As such, there are notable variables to consider, including the college course group and the time spent watching TV.

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2 The options available to conduct the research include manipulating the TV-watching behavior, observing the changes, finding a way to observe the students in a natural, non-manipulated setting, and then observing for subsequent analysis. The second option appears ethical, and it suffices in this scenario. The college course factor allows for separating the two types of students and forming the research groups. Observational within-subjects studies have allowed researchers to evaluate the effects of e-cigarettes on nicotine delivery based on tobacco smoke biomarkers as the measures utilizing urine samples (Goniewicz et al., 2017). In this example, which is an inspiration for the proposed approach for scenario 1, there was a collection of data before and after the introduction of pen-style e-cigarettes. The common feature is using the same subjects and observing the same biomarkers across the research period.

The study will not be an experiment since there is no manipulation of a variable to check the before and after effects. Additionally, it is an observational study, where the researcher groups participants based on a factor, the college course, and observes them based on another factor, like the time spent watching TV (Lu, 2009). It is also a quantitative study using minutes and a between-subjects design since the students taking the psychology course are different from those taking psychology at the graduate level. The observations can take place concurrently. The study qualifies as a cross-sectional design since the group of participants relies on the characteristic course level, and the time spent watching TV measures take place within each group. Therefore, the study period allows the definition of a cohort to apply to the studied participants. The advantage of using the cross-sectional design is capturing data over a short period, eliminating many threats to internal validity. The study is unlikely to suffer any participant attrition, testing effects, and regression toward the mean (Privitera, 2020). If the exploration takes a month, then the short time compared to the years it takes to complete a course

3 lowers the likelihood of individuals shifting their behavior of watching TV midway through the study.

Methodology The study will be a systematic observation because there are specific times and conditions within the study period when the behavior of watching TV takes place. The researcher can either observe the student or ask the student to keep a journal and record the minutes spent watching TV. The journals could be daily reflections with the students reporting the number of times they watched TV and the time spent in each session. An additional prompt could engage the student on the type of TV content watched as it may help increase the recall and improve the accuracy of the reported time spent watching.

The following are the hypotheses.

H0: There is no significant difference in TV-watching habits between undergraduate college psychology students and psychology graduate students.

H1: Undergraduate college psychology students watch more TV than psychology graduate students.

The rationale for choosing the observational quantitative between-subjects design is to allow for generalizability and to fit the circumstances of the study while avoiding ethical bottlenecks associated with working with human participants. The present study cannot occur in a laboratory setting with fixed and controlled conditions, and the researcher cannot randomize participants without interfering with the study's objectives. The research relies on pre-existing groups rather than groups formed specifically for the study. The practicality and reliance on existing data further make an actual experiment with manipulation impossible to carry out.

4 Lastly, there will be a need for informed choice from the student participants, and random assignment will interfere with the ability to control their voluntary participation in the study.

In light of the above, the following would be additional study parameters. Its population will include all undergraduate college psychology students and psychology graduate students from the selected institution. The sampling will be random selection. Here, the researcher will first establish the study population, seek voluntary participants, and then select the participants at random from the provided population. This step will allow for the generalization of the findings to the rest of the student population.

Data Collection and Analysis The data collection strategy will use diaries or journals capturing the daily TV-watching activity, relying on the self-reported measure. The researcher will trust that the student will be truthful with the information and will accurately recall the day's TV-watching activity.

Lastly, the data analysis will use independent samples unpaired t-test statistical analysis to reveal any significant differences based on the assumption that the two groups have no relationship (Routhu et al., 2017). This approach will be similar to Mahmoudi and Ozkan (2015), whose observational study had two groups of participants and sought to determine whether there were differences in the perception of PDP from the perspective of novice and experienced language teachers using a t-test analysis through SPSS software. Having only two variables with one independent variable makes the t-test analysis an appropriate choice.

Conducting the study in scenario 1 can be problematic if the environment is not controlled for other conditions. For instance, there might be incentives for watching TV when the researcher wants to collect data. Students might be following a significant event, which leads them to watch TV longer and more often than expected. However, the researcher will observe

5 the two groups simultaneously rather than having one group after another. The approach should ensure no order effects weaken the study's internal validity (Privitera, 2020). Thus, the time limit will be the constraint, ensuring that all participants answer their respective journals daily and present them for evaluation before the deadline. The researcher may also consider participants who have not participated in a similar study to avoid any carryover effects jeopardizing the internal validity. Even though it would be difficult to affix every participant to the same situation, these constraints should create a uniform environment for the observations (Privitera, 2020). However, an additional challenge is to keep a few constraints to avoid jeopardizing the generalizability of the study's findings.

Ethical Considerations Students must give explicit consent before participating in the study, and the researcher must take measures to avoid or minimize participant fatigue. When students are reporting the day's activities on their diaries, they should do so in a non-intrusive way. The study should only gather quantitative data without any means of identifying students and using their data for any malicious, non-research purposes. The students should be aware of the type of data being collected and the reasons for collecting it. Observational studies can be intrusive since the researcher may have a chance to collect more than the intended data. Therefore, there should be explanations, and student participants must consent to the data collection method.

Scenario 2 – A Within-Subjects Quasi-Experiment Interrupted Time Series Design In the second scenario, the study aims to discover and describe the relationship's existence. The scenario from available literature shows a need to identify if a relationship between individuals with high cholesterol and depression exists so that it would be possible to come up with additional related risk factors to inform health interventions. A developed research

6 question to guide this study is as follows. What is the relationship, if any, between high cholesterol and depression?

The choice between an experiment and a non-experiment will be determined by an ability to manipulate variables, control the manipulation, and observe the outcomes. In the given scenario, an option for randomization since the factor of interest is whether high cholesterol persons show any relationship with depressed persons regarding their characteristics. The study will be relying on high cholesterol levels and depression, and they are both non-individual traits.

High cholesterol is a medical condition that can vary across individuals and can change over time. The depression trait also displays similar features, varying across individuals and likely to change over time. Therefore, their observation, manipulation, or study in any other way must consider the observed conditions' dynamic nature.

In light of the above conditions, an appropriate design would be a quasi-experiment with an interrupted time-series design. The aim is to find out the relationship of the two variables. The selected method is appropriate since there is no possibility for true randomization. The method captures trends over time in which the researcher can tell the strength of the reported association.

The interrupted time series is appropriate since the researcher cannot manipulate the time when the independent variable occurs. According to Alshamsan et al. (2012), the time series analysis was an appropriate method to use when one independent variable is associated with the observed variations in the dependent variable, and the use of this approach would help researchers to find out whether the independent variable would attenuate the observed disparities in the dependent variable across participants over time. An advantage of the approach is that it allows the high cholesterol variable to be observed in the early onset of the depression variables and across the episodes of depression (Privitera, 2020). The challenge with the selected method is that it relies

7 on more than one group, denying the study an experimental opportunity to have control (Privitera, 2020). The following would be the hypotheses for the study.

H0: There is no significant relationship between high cholesterol and depression over time.

H1: High cholesterol is associated with increased depression symptoms over time.

In the past, studies have approached the need to determine if an intervention in healthcare was associated with a health outcome. For instance, Wherry and Miller (2016) asked if the state Medicaid expansions were related to modifications in insurance coverage, access to and use of health care, and self-reported health. Similar to the current scenario, there was a need to adjust for time-invariant differences in characteristics.

The population for the study could be patients in a hospital. They will likely be outpatients or inpatients, and their medical records about the high cholesterol situation will be available for use in the study. The researcher will target the data and seek to associate it with the depression. The research can use anonymous medical records and then go back in time to capture the depression status of the patient. If such records are unavailable, there could be a designated period for the study in which the patients identified with high cholesterol are recruited. Then, their depression status will be monitored during the study. Therefore, the population will be persons with high cholesterol, identified to be at risk of or already showing depressive symptoms.

Participants observed for high cholesterol will be the same ones observed for depression.

Therefore, this will be a within-subjects design. The sampling will be non-probability. The researcher started with identifying persons with high cholesterol for the study and then proceeded to monitor depression states. The data collection will be based on medication. The

8 patients in the high cholesterol group are expected to use medication to manage the condition.

When they have depression, their medication should also reveal the same. Thus, the researcher will observe and measure the medication use during the study period. The patients will report the medication they used across weeks of the study in quantities and type. The researcher should then code the results according to the severity of the high cholesterol and depression conditions.

The resulting data will be as follows. The independent variable will be high cholesterol, measured by different dosages for the respective patients, and the dependent variable will be depression, also measured by different dosages of medication prescribed for the patient. Patients will have a diary to fill out every week until the study ends, and this will be the primary data collection instrument.

Data analysis will be based on Pearson's correlational statistical analysis to establish if an association exists, and then the use of regression analysis to determine to what extent the high cholesterol predicts depression. The association is the target in the former analysis, while the latter will reveal the causality. A correlation test is appropriate because it can reveal the strength of the association and the direction. For instance, it may show whether high cholesterol levels are associated with high depression indicators or the opposite. The regression analysis will tell whether the independent variable influences the dependent variable at a significant level, and the threshold of influence, or the extent to which the measured independent variable is responsible for the observed changes in the dependent variable. Similar to Alshamsan et al. (2012), the regression model should help estimate the effects of the independent variable in the studied population.

Using two analyses helps narrow down the observed association and possible effects on the observed variables. Other factors outside the research will likely be responsible for the

9 observations made. The regression analysis should reveal the extent to which unobserved factors are likely to influence the outcomes. Irrespective of the indicated level of influence or association from the two statistical tests, the results will matter only when the significance level is lower than the specified threshold; otherwise, researchers must neglect the findings or explain them as insignificant. Nevertheless, when done correctly, the process leading to the end of the study should be robust enough to inform the next move after making inferences. In the present scenario, the next step after disseminating the results might be to inform practitioners of what they must do regarding high cholesterol and depression.

Ethical Considerations The ethical considerations for scenario two include access to the patient's medical records and safeguarding the patient's privacy. When patients share their prescriptions and behaviors when taking them, they expose their dependence on the medication. Such information is sensitive, and any authorized access to it violates privacy. Another challenge would be preventing perceptions of depression from becoming a basis for judging the collected data as appropriate for the study. Creating a reliable measure is necessary and should apply to all participants without bias.

Part 2 - Reflection Thee course content structure is ideal for my progress in learning. Significant topics are broken down into specific units that can be covered in a week, and assignments at the end of the week allow reflection and practice using real-world conditions. It was easier to grasp complex ideas. The activities, including readings, discussions, and SPSS exercises, provided a wellrounded learning experience. Sometimes I would fall behind, but the ease of the class textbook reference made everything much easier than I would imagine if I were using a physical textbook.

10 Additionally, finding related resources on the web, including peer-reviewed journals for use in week six, was relatively easy after understanding research types' motivations for the study and going through the progression of quantitative study designs. The knowledge facilitated the perusal of abstracts and titles to get an appropriate article for the respective assignments.

One of the most significant takeaways from the early weeks of the course was the introduction to various research designs and their practical applications. The new information for me was factorial design and the different kinds of studies that would utilize this approach. I realized many studies are already using factorial designs. Notably, follow-up studies use part of the results from the previous study in their statistical analyses. Initially, I knew about the concept but needed more understanding. Taking the course meant that I could interact with it, and I even tried it out when analyzing data from a typical study. There are more lessons from the SPSS in Focus exercise that I will continue using. It was a smooth transition from the familiar concepts to the unfamiliar or new ones.

At first, experiments were familiar, but quasi-experiments and non-experiment quantitative studies were new. Later on, issues like order effects as threats to internal validity were surprising but also revealing. There were individual traits and the designation of betweensubjects and within-subjects, which seemed evident until I realized I was barely scratching the surface. They contained far more detail than I had anticipated. I can confidently pick a design, method, and approach to analyze data, which I would not have in the past. As the course progresses, I expect to become familiar with additional techniques and concepts. I was already familiar with correlations and regression on the data analysis side. The factorial design presented new approaches to analyzing data, and I was impressed and excited to do the factorial ANOVA analysis.

11 I was also well aware of research ethics, especially the challenges of observing people and collecting data within ethical boundaries. I should have paid more attention to the issue of participant fatigue, especially when using within-subject methods and going through various routines to collect data.

The practical application of research methods through SPSS exercises was the highlight of the course for me. Working with real data and applying statistical techniques enhanced my comprehension of quantitative research. It made the theoretical aspects more tangible and showed me how research methods are used in practice.

While I appreciated the depth of the course content, I occasionally found specific statistical analyses challenging. For example, interpreting the results of ANOVA or understanding the nuances of factorial designs required more effort. I did not enjoy these the least, but I found them challenging, which made the week tough. Nonetheless, I am glad I paid more attention and dedicated more time overall than I would when I was already familiar with the subject or activities. I am more competent than before due to these challenges.

I also address preconceived notions I had about the course. Before, I had a somewhat simplistic view of quantitative research methods. Theyy are tools for collecting and analyzing data. I should have simplified everything into a straightforward process without considering motivation and the different paths that lead to the outcome. I realized something important about data when doing the factorial design. The factorial ANOVA done with within-subjects revealed the same outcome as that done for the between-subjects design because the data was the same, only the simulated notion differed. I realized many existing methods seek to ensure the correct data interpretation and reduce errors. Nevertheless, it is essential for me to not only identify the

12 right research method and study but also understand the principles behind arranging data in a specific structure to facilitate analysis.

Lastly, I would be more comfortable handling the experiments and non-experiments. I will need more practice in the quasi-experiments. I am adept at handling quantitative research\ after going through its philosophies and various occasions and their presentations as challenges or opportunities for using different types of these studies. My research chart is now a handy reference tool for understanding and picking the right methods for a given scenario, while also serving as a personalized summary of the course material on quantitative research studies.

References Alshamsan, R., Lee, J. T., Majeed, A., Netuveli, G., & Millett, C. (2012). Effect of a UK payfor-performance program on ethnic disparities in diabetes outcomes: interrupted time series analysis. The Annals of Family Medicine, 10(3), 228-234.

Goniewicz, M. L., Gawron, M., Smith, D. M., Peng, M., Jacob III, P., & Benowitz, N. L. (2017).

Exposure to nicotine and selected toxicants in cigarette smokers who switched to electronic cigarettes: a longitudinal within-subjects observational study. Nicotine & Tobacco Research, 19(2), 160-167.

Lu, C. Y. (2009). Observational studies: a review of study designs, challenges and strategies to reduce confounding. International Journal of Clinical Practice, 63(5), 691–697.

Mahmoudi, F., & Özkan, Y. (2015). Exploring experienced and novice teachers’ perceptions about professional development activities. Procedia-Social and Behavioral Sciences, 199, 57-64.

Privitera, G. J. (2020). Research methods for the behavioral sciences (3rd ed.). SAGE Publications, Inc.

13 Routhu, M., Safka, V., Routhu, S. K., Fejfar, T., Jirkovsky, V., Krajina, A., & Cermakova, E. (2017). Observational cohort study of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt (TIPS). Annals of Hepatology, 16(1), 140-146.

Wherry, L. R., & Miller, S. (2016). Early coverage, access, utilization, and health effects associated with the Affordable Care Act Medicaid expansions: a quasi-experimental study. Annals of Internal Medicine, 164(12), 795–803.