Framing Effects on Support for Men's Issues: A Survey Experiment on College Students' Attitudes by DREW COLLINS-BURKE A THESIS Presented to the Department of Political Science and the Robert D. Clark Honors College in partial fulfillment of the requirements for the degree of Bachelor of Science June 2024 An Abstract of the Thesis of Drew Collins-Burke for the degree of Bachelor of Science in the Department of Political Science to be taken June 2024 Title: "Framing Effects on Support for Men's Issues: A Survey Experiment on College Students' Attitudes" Approved: Neil O’Brian Ph.D. Primary Thesis Advisor Men are generally attaining worse education outcomes than other gender groups and are more likely to commit suicide. Richard Reeves, formerly of the Brookings Institution and now President of the American Institute for Boys and Men (AIBM) referred to this—among other disparities disfavoring men—in early 2023 as being indicative of “male inequality”; however, some may suspect a cultural stigma with this phrase (Reeves, 2023a)This survey experiment evaluates the impact of the phrase “male inequality” on support for men’s issues, while using accompanying graphs of high school GPAs, bachelor’s degree attainment and suicide rates. I find there is support for men’s issues regardless of treatment and multiple statistically significant treatment effects that generally display the phrase “male inequality” leads to a reduction in support, especially for women and Democrats. Also, Republicans being shown treatment of just the graphs of male disparities—without the phrase—leads to a statistically significant 31.9% increase in support for feminist policies. This thesis indicates men’s issues may receive majority support from college students, that the phrase “male inequality” harms support for male policy issues for Democrats and women, and that Republicans may be more likely to support feminist policies when they feel male issues are being discussed. 2 Acknowledgements I’d like to thank Dr. Neil O’Brian and Dr. Trond Jacobsen for serving as my mentors throughout college, as well as being my Primary Advisor and Honors College Representative respectively. I learned so much from both of them. I’d also like to express gratitude to Dr. Daniel Tichenor, Dr. Ellen Herman, Rebecca Dinwoodie, and Dr. Allison Gash for their roles in the Wayne Morse Scholar program—all of these people encouraged me to work on a tenuous topic, and I may not have ever tried to talk about men’s issues publicly without them. Dr. Burke Hendrix, Dr. Jane Cramer, Dr. Lars Skalnes, Jessica Reanne, and Dr. Chandler James all generously agreed to distribute my survey through their classes, helping me tremendously with receiving responses. I’d like to thank my high school teachers, Robert Daniel O’Neill, Gail Greaney, and Matthew Bradley for inspiring me to be ok with taking risks or failing, to work hard for a good cause, and trying to show compassion to all people. Richard Reeves served as an influential figure for this survey experiment, and publishing Of Boys and Men was the catalyst to this thesis. Being able to intern with him at the American Institute for Boys and Men was such an unexpected and awesome experience, and I owe him greatly. Lastly, I would like to thank all members of my family, my mom, my stepdad, my dad, my stepmom, my sister, and our dogs for all of their support throughout college—I would never be publishing this without them. 3 Table of Contents Introduction 7 Literature Review 9 Hypothesis and Methods 14 Results 19 Limitations 32 Conclusion 35 Bibliography 67 Appendix: Survey Start and End Card 37 Appendix: Dataset 38 Appendix: R Data Analysis Code 54 4 List of Figures Figure 1: Gender Composition of High School GPA Rank (Deciles). Richard Reeves, Of Boys and Men, 2022. 16 Figure 2: College Enrollment by Gender, 1972-2022, Sarah Reber and Ember Smith, “College Enrollment Disparities”, 2023. 16 Figure 3: John Elflein, “Death rate for suicide in the U.S. 1950-2019, by gender”, 2022. 17 5 List of Tables Table 1a: Mean of Dependent Variables for Treatment Groups and Control Groups 20 Table 1b: Average Support by Political Party and Gender (Using Only Control) 22 Table 2: The Effects of Treatment Groups on Dependent Variable Support 24 Table 3: The Effects of Treatment Groups on Dependent Variable Support (Among Genders) 26 Table 4: The Effects of Treatment Groups on Dependent Variable Support (Among Political Parties) 29 6 Introduction On average, boys are significantly more likely to fail reading, mathematics and science than girls in school in OECD countries (Encinas-Martín and Cherian, 2023). Males also develop cognitive abilities more slowly than females (Adani and Cepanec, 2019). In Reeves’ 2022 book Of Boys and Men, he argues that the developmental delay for brains that boys have in comparison to girls is one key factor that leads to worse educational attainment. He also suggests that men’s issues and disparities need to be discussed and addressed. This issue also intersects with factors like race—Black men in particular are especially likely to commit suicide and struggle in education in comparison to white men and Black women (Disparities in Suicide, 2023) (Reeves, 2022, p. 68). Movements designed to promote the advancement of men are often associated with anti- feminist perspectives. For instance, masculinist movements are frequently criticized by pro- feminist scholars for promoting the status quo of patriarchal dominance. However, Reeves has argued that men are systemically disadvantaged in the education system. He asserts that he does not want to diminish the prevalence of misogyny in society today, but that he believes there needs to be additional attention placed towards men in order to address their unique issues. In a 2023 YouTube video published by BigThink, Reeves refers to the disparities in the education system as being indicative of “male inequality,” and argues that men’s issues in general are underdiscussed because authors fear social consequences (Reeves, 2023a). This survey experiment focuses on the phrase “male inequality”, which he uses to refer primarily to the deficit in education between male and female students. Although movements that center around advocating for men often have strong right-wing and anti-feminist associations, Reeves’ approach is different: he states that the feminist movement was justified in 7 interrogating patriarchal structures (Reeves, 2023a). In other words, his argument is explicitly pro-feminist. For the history of the phrase, I found numerous examples of “male inequality” being used as far back as 1748 in academic contexts prior to Reeves’ usages (Turner, 1951) (Gang and Yun, 2002) (Bellou, 2017) (Kahn, 1748). However, I could find no instances of it being used in a popular and public way before Reeves’. This indicates that prior to Reeves’ usage the term was oriented towards a technical, academic audience, and its public reception has not yet been thoroughly evaluated. Some commentators, such as political journalist and feminist activist Elizabeth Plank, have critiqued this phrase. In a podcast interview with Reeves, Plank criticized Reeves’ usage of the term inequality in particular, to which Reeves said, “I think that I’m just using gender inequality in a neutral sense there to describe any gap that can be seen between the two genders…” (Wayfarer Studios, 2023). Plank responded, “...to me, there is a difference between [a] gender gap and gender inequality… …to me that connotes sexism… … that connotes societal discrimination… women being barred from going to college in 1972 is different from men having difficulties [in education]...” (Wayfarer Studios, 2023). After further discussion, Reeves synthesized Plank’s critique, stating: …I think I’m using inequality in a neutral way, but what is heard is that inequality is related in an important way to an injustice. And that obviously gets people’s backs up if you’re suddenly talking about the gender inequality for boys and men in education, because that's not the result of an injustice… I need to sit with this for a little bit longer, but it’s incredibly helpful for me just to have heard that Liz, and to know that that word inequality is being received differently from the way I mean it… (Wayfarer Studios, 6:53-7:11). Plank’s concerns and Reeves’ response establish potential issues with “male inequality” as a phrase, indicating that it could cause difficulties in persuading a broader audience to support men’s issues. The survey was motivated by the idea that the phrase Reeves uses has strong 8 enough negative connotations in the public eye such that it may ultimately reduce support for an increased government and societal focus on issues facing men. Literature Review Although Reeves places unique attention upon male disparities, many—or perhaps even most—other academic authors writing on gender disparities focus on gender discrimination against women. For instance, Joseph Cimpian, writing for the Brookings Institution, focuses on the biases that girls face in the education system. Cimpian argues that teachers generally underestimate the mathematical ability of girls in particular. He adds that women in STEM fields face discrimination that causes many female college-students to avoid joining those areas of study. For instance, Cimpian asserts that many women in STEM academia positions feel less intelligent than their peers, are judged for their looks, generally do more unrecognized service in faculty positions, and receive proportionally less credit for their scholarship. The author focuses on societal and cultural forces that cause this sexism to exist within educators themsleves and suggests that educators must critically evaluate themselves for bias (Cimpian, 2018). Although some may believe Reeves and Cimpian contradict each other, the perspectives are logically compatible. However, Cimpian’s argument neglects to discuss ways that men are disadvantaged in the education system. Cimpian discusses that in math tests scores, “a gap of nearly 0.25 standard deviations developed in favor of the boys by around second or third grade.” However, Cimpian does not discuss that women do significantly better than men in reading test scores. According to Reeves, “Girls are about a year ahead of boys in terms of reading ability in OECD nations, in contrast to a wafer-thin and shrinking advantage for boys in math” (Reeves, 2022b, 18). Reeves’ evidence— in contrast with Cimpian’s article on worse outcomes for women in education—show that some 9 authors focus on women’s inequitable education outcomes without discussing the statistical realities for men. Cimpian’s article uses the phrase “gender equity” as a goal for the education system, but it implies that achieving equity is only created by improving education outcomes for women with sentences like, “...the overall picture related to gender equity is of an education system that devalues young women’s contributions and underestimates young women’s intellectual abilities more broadly.” Cimpian’s perspective here asserts that the “overall picture” is a system that disfavors women, but this author’s perspective implicitly contradicts the statistics Reeves’ points out. This is not to suggest that inequality in the education system does not exist for women, but to claim that articles like this one may underplay the disparities males face in education. To assist with these disparities, Reeves’ primary policy proposals are to universally delay the enrollment of boys in education systems by a year and to encourage more men into what he calls “HEAL” roles: health, education, administration, and literacy positions. Reeves points out that these roles all are predominantly held by women (Reeves, 2023a). In particular, introducing more men into education—especially early childhood education—may be crucial for boys, who academics Joyce Matwasa and Lwazi Sibanda suggest need masculine role models and to see what it means to be a man. They argue that male educators for children younger than eight in particular suffer from stigmas, are shamed, and sometimes perceived as threatening to children. They suggest this may be due to beliefs that women are better equipped to be teachers of young children. This indicates that masculine role models are important to the development of young boys, but that social stigmas exist for male educators in early childhood. Reeves’ views on encouraging men into education sectors has been suggested by other authors, like Elizabeth Heubeck writing for Education Week. Heubeck points out that men made 10 up 24% of the teachers in the K-12 system during the 2017-2018 school year, and that men who belong to racial minority groups face intersectional challenges. For instance, one statistic Heubeck discusses is that only 2% of teachers are Black men. According to the author, in spite of the over-representation of women as teachers, men are vastly over-represented in superintendent positions, with men taking up 85% of those roles (Heubeck, 2021). This demonstrates that men are overrepresented in administrative authority roles in the education system but underrepresented in teaching roles. Heubeck suggests that most of the male teachers they interviewed were often asked when they planned to move into an administrative rule, suggesting that there may be cultural expectations within the education system that men should be leading teachers, not working with children. The concept of men having unique expectations in education that can intersect with race is reinforced by a 2022 qualitative assessment of Black male teachers in special education. The authors note that these teachers are often pressured into coaching and discipline roles in particular. They also suggest that colleagues underestimate Black male teachers’ potential for effective school teaching, which the authors argue is due to racial inequity and role socialization theory (Cormier et al., 2022). Some using a feminist framework may suggest this is an example of how patriarchal structures hurt men; boys benefit from having masculine role models in classrooms, but schools generally have women as teachers. Additionally, the men who do teach may have pressure to leave classrooms or be seen as only fitting in certain roles, which can intersect with race. This demonstrates that aspects of Reeves’ perspective have been promoted by other experts. However, discussing disparities in male education outcomes may be negatively perceived by many. Political movements focusing on men often have anti-feminist connotations. One example of this is the men’s rights movement. Joel Rose writing for NPR discusses the 11 movement, suggesting, “Now, men's rights advocates argue, feminism is more concerned with promoting the interests of women—often at the expense of men”. The perspective of these groups contradicts Reeves’ views. Others have also criticized this movement for enabling misogyny and being associated with extremists, Rose notes. Arthur Goldwag writing for the Southern Law and Poverty Center furthers this association by writing about a men’s movement leader who self-immolated and publicized a statement criticizing feminism for creating laws that were, in his view, overly strict in defining domestic abuse. The author suggests that extremists like this create negative societal perceptions of men’s movements (Goldwag, 2012). Overall, the phrase “male inequality” may have strong connotations towards misogynistic actions because of anti-feminist men’s movement participants, like Rose and Goldwag discuss. These connotations may ultimately hurt the advancement of Reeves’ policy suggestions. In 2023, Reeves left Brookings and began AIBM to start pursuing further governmental attention towards men’s issues. Since the publication and general positive reception of Of Boys and Men, more prominent voices from prestigious institutions have focused on boys, men and masculinity in particular. Some notable intellectuals who wrote on this issue and generally support Reeves’ perspectives include Professor of Marketing at the New York University Stern School of Business Scott Galloway, social psychologist and Professor of Ethical Leadership at the Stern School of Business Jonathan Haidt, and Atlantic staff writer and author of “Rethinking Sex: A Provocation” Christine Emba (Galloway, 2023b) (Emba, 2023b) (Haidt, 2023b). The Washington state legislature even considered creating the country’s first Commission on Boys and Men, although this effort has not passed (Reeves, 2024). However, this new concern about boys and men has not led to significant legislation yet, even if worries seem to be picking up steam. 12 This has been occurring while an increasing international political gap has been opening between men and women. According to John Burn-Murdoch writing for the Financial Times, “In countries on every continent, an ideological gap has opened up between young men and women.” The author points out that 18-30 year-old women are 30% more liberal than men of the same age in the U.S., a pattern repeated in countries like South Korea, Germany, England, China, Tunisia, the U.K., and Poland (Burn-Murdoch, 2024). He also discusses that political opinions are likely to solidify in youth, indicating that this divide may continue to affect the generation for years to come, across the world. This is a concerning trend, as this divide seems likely to lead to significant tensions in personal and political lives by gender. In discussing this trend for AIBM, Reeves suggests that many men have begun to see feminism as a movement against women, whilst issues of male mental health and education outcomes worsen—and many young men feel those issues are not being acknowledged by governmental and societal institutions, especially ones associated with the left. Reeves suggests that right-leaning politicians will pick up on these issues if the left doesn’t, creating an increasingly polarized culture war. He ends this article emphasizing the potential harms of having political parties increasingly divided by gender (Reeves, 2022). Burn-Murdoch and Reeves’ perspectives demonstrate that the political differences and divide between men and women is becoming increasingly prominent and harmful, creating a need for potential methods to bridge the gap between them. Overall, these sources show perceptions of men’s movements are complex. With the associations that men’s movements may have with hateful actions, some may be uncomfortable with the phrase “male inequality.” Although statistics in education and suicide rates indicate that maleness can lead to poorer life outcomes in some instances, the discussion of men’s issues is possibly also associated with misogyny instead of the pro-feminist perspective Reeves takes. 13 This study is designed to explore attitudes towards the phrase “male inequality” and men’s issues. Hypothesis and Methods Because of potential connotations to controversial movements, I hypothesized that the phrase “male inequality” would lead to a decrease in support in comparison to the control. Because the graphs display legitimate statistics on poor education and life outcomes for men, I also hypothesized that respondents in the treatment group without the phrase would be the most likely to support novel policies and an increased attention for male disparities. In order to receive data, I used an IRB approved version of a modified Qualtrics survey from a similar survey experiment in 2023, obtaining responses from several different convenience samples at the University of Oregon as well as an online Prolific sample of college students. Altogether, there were 553 usable responses for analysis. All samples from the University of Oregon were in Winter 2024 courses in political science, specifically: PS 201: US Politics, PS 275: Legal Process, PS 311: Sovereignty and Revolution, PS 326: US Foreign Policy, PS 340: International Political Economy, and PS 384: Nuclear Politics of the Middle East. In all classes except PS 275, in-class surveys were used, where respondents accessed the survey through a QR code or Canvas announcement. In the online course PS 275, respondents received a Canvas announcement link to access the survey. Altogether, this sample accounts for 247 responses; none warranted exclusion either. For the Prolific sample, respondents were restricted to respondents in their 1st, 2nd, 3rd, or 4th year of undergraduate study, in order to receive a demographically comparable sample to the University of Oregon convenience sample. These respondents saw a study on the Prolific platform titled: “Perceptions on Societal Beliefs” with this description: “In this study, I will ask 14 about your beliefs on various political challenges our country faces.” (The title and description were purposefully vague to avoid any confounders, such as the Hawthorne effect.) This sample received 305 responses, and like the University of Oregon sample, all responses were valid— none were excluded. For the survey’s makeup and flow, respondents saw informed consent text, which can be seen in the appendix. Then, for the first question, respondents were asked for their gender. They could select “Man”, “Woman”, “Non-binary/third gender” or “Prefer not to say”. Then, they were asked for their political affiliation and could select “Democrat” “Republican” “Independent” or “No affiliation”. After moving to the next page, respondents were randomly assigned to one of three groups. The first group received a graph (Figure 1) titled “MALE INEQUALITY: Disparities in high school GPA by gender”. This group will be referred to in future tables as “Graphs with Phrase ‘Male Inequality’”. Respondents could not advance any treatment page until 10 seconds had passed. In order to increase engagement with the visual, they were asked “Are you surprised?” and then could state their support from “Definitely not”, “Probably not”, “Might or might not”, “Probably yes”, to “Definitely yes”. (The timer and this question were repeated for the next two graphs, but question answers were not used as a data point for analysis—it was only a method to increase engagement.) Then, respondents moved to a page with a graph (Figure 2) titled “MALE INEQUALITY: Disparities in bachelors degree attainment rates by gender” with the same engagement question. Next, respondents received a third graph (Figure 3) titled, “MALE INEQUALITY: Disparities in suicide rates by gender” with the engagement question. Then, respondents moved on to the dependent variable questions, which will be elaborated on shortly. 15 Figure 1: Richard Reeves, Of Boys and Men, 2022 Figure 2: Sarah Reber and Ember Smith, “College Enrollment Disparities”, 2023 16 Figure 3: John Elflein, “Death rate for suicide in the U.S. 1950-2019, by gender”, 2022 For the second group, the graphs, prompts and questions were identical to the first group—except for the fact that the phrase “MALE INEQUALITY:” was removed. This will be referred to in future tables as “Graphs Without Phrase”. For the third group, no treatment was shown. This will be referred to as “Control”. Lastly, all respondents received the three dependent variable questions. The first was, “Some have proposed policies designed to reduce disparities in education and mental health outcomes that disproportionately impact men. Would you support this?”. Respondents could answer “Definitely not”, “Probably not”, “Might or might not”, “Probably yes”, and “Definitely yes”. The second dependent variable question was, “How likely would you be to support an increased government focus on men's issues?”. The responses available were, “Extremely unlikely”, “Somewhat unlikely”, “Neither likely nor unlikely”, “Somewhat likely”, “Extremely likely”. Both the first and second evaluate similar concepts but with slightly different phrasing in 17 order to determine the impact of framing. The third and final dependent variable question was: “How likely are you to support feminist policies designed to address gender discrimination against women?”. Respondents could choose options that were the same as the second dependent variable. This question was designed to see if there would be a treatment effect for supporting feminist policies, due to a possibility that seeing either treatment may galvanize or reduce support. (For the survey end card designed to explain the survey’s academic purpose to comply with IRB regulations, see the appendix.) For ease of analysis, gender was re-coded to remove responses that were not “Man” or “Woman”. Political party was re-coded to remove non-partisan responses. All three dependent variables were re-coded into binary, with 0 representing a negative or neutral response and 1 representing a positive response (neutral responses were included with the negative responses to solely focus on supporting respondents, offering an evaluation of support vs. non- supporters). The two treatment groups and control group were each re-coded into binary variables where they could be compared to one another, with the excluded group being represented as “NA”. In other words, the two treatment groups were to be compared with one another, the first treatment group to be compared with the control, and the second treatment group to be compared with the control. Rationale for Survey Structure The two treatment groups and control all offer compelling pieces of information when directly compared to another. The treatment group featuring the phrase and the graphs provides insight into how the phrase impacts support for male-focused policies and feminism. When contrasted with the treatment group without the phrase, the impact of “MALE INEQUALITY” can be isolated. When the treatment group with the phrase is compared to the control, 18 information on how both the phrase and graphs affect responses can be discerned—somewhat similar to how Reeves presents the issue, with both the phrase and quantitative data. Then, when the treatment group without the phrase is compared to the control, the impact of seeing just data featuring male disparities as opposed to the support without intervention can be seen. The control offers a baseline for support on male issues and feminist policies for this sample. This provides unique insight in and of itself on general attitudes regarding male issues. Because those in the control saw no treatment information, their responses can be seen as a poll of college students’ general beliefs on policies designed to benefit men. Results Overall, the survey demonstrates that the phrase “male inequality” appears to significantly impact support for men’s issues when analyzing treatment effects by gender and politically party (Tables 3 and 4). But, when looking at the overall support regardless of demographic, statistically significant treatment effects do not appear—although, there are two marginally significant ones (Table 2). This indicates that the presence of treatment effects are highly influenced by demographics, and suggests that strategies designed to increase support for men’s issues will need to account for these demographic preferences. Table 1a displays that there is generally support for all dependent variables, with a majority of respondents holding favorable views towards men’s issues and a vast majority holding favorable views towards women’s policy issues. This may indicate that support for men’s issues is generally present among college students. For political actors, this information may contradict beliefs that discussing male disparities is a politically untenable action. 19 Table 1a: Mean of Dependent Variables for Treatment Groups and Control Groups Graphs with Phrase “Male Graphs Without Control Inequality” Phrase Percent in Support of Reducing 54.64% 63.91% 64.29% Disparities for Men Percent in Support of Increasing 53.09% 50.89% 56.04% Government Focus on Men’s Issues Percent in Support of Feminist Policies 78.87% 82.25% 80.77% N Value N=194 N=169 N=182 Notes: These results are the means of dependent variable questions for different treatment groups. Each dependent variable initially evaluated support on a scale of 1-5. 1 was least supportive, while 5 was most supportive. These results were then coded to be either 0 (for negative and neutral responses) and 1 (for positive responses), to evaluate those who supported the policies vs. those who did not. 20 Table 1b displays the average support of respondents in exclusively the control by political party and gender. Since these respondents received no treatment, this can be seen as a poll of respondent’s beliefs without interference. These results demonstrate generally high support for the first two dependent variables on men’s issues among all respondents, with 11 out of 16 groups showing overall support. Additionally, for those same dependent variables, there is no group with average support among any demographic below 37.50%. This demonstrates that, on average, the college students surveyed do support men’s issues. Still, evaluating these results demonstrates that men are on average, more likely to support the first two dependent variables on men’s issues than women. This indicates that advocates for men’s issues likely need to focus their persuasive rhetoric towards women to achieve greater overall support from the populace. Even though they test for very similar opinions, this sample displays high variation by party and gender between the first and second dependent variables. For instance, 42.86% of Republican women responded in support of reducing disparities for men, but 71.43% responded favorably towards increasing government focus on men’s issues. These differences underscore the importance of framing on men’s issues. In sum, this table provides insight into different demographics of college students’ baseline support for men’s issues and feminist policies. 21 Table 1b: Average Support by Political Party and Gender (Using Only Control) Gender and Demo- Demo- Republi- Republi- Indepen Indepen-Political crat crat can Men can -dent dent Unaffiliated Unaffiliated Party Men Women Women Men Women Men Women Percent in Support of Reducing 80.85% 61.40% 75.00% 42.86% 61.11% 25.00% 61.53% 70.00% Disparities for Men Percent in Support of Increasing Government 61.70% 47.37% 83.33% 71.43% 61.11% 37.50% 69.23% 40.00% Focus on Men’s Issues Percent in Support of Feminist 87.23% 94.74% 8.33% 42.86% 66.67% 81.25% 92.31% 90.00% Policies N Value N=47 N=57 N=12 N=7 N=18 N=16 N=13 N=10 Notes: These results are the means of dependent variable questions for various demographics. Each dependent variable initially evaluated support on a scale of 1-5. 1 was least supportive, while 5 was most supportive. These results were then coded to be either 0 (for negative and neutral responses) and 1 (for positive responses), to evaluate those who supported the policies vs. those who did not. 22 Table 2 displays that both the treatments with and without the phrase generally have a higher support in comparison to the control group—in other words, being shown either treatment generally leads to an increase in support. Therefore, showing people visual representations of statistical disparities for men may be an effective way to garner public support. There is a marginally significant treatment effect of a slight increase in support (9.27%) for those shown the graphs with the phrase “Male Inequality” compared to the group who received the graphs without the phrase. This may indicate that the phrase “Male Inequality” can even increase overall support for reducing male disparities, but because it is only marginally significant, this should not be considered a certainty. Similarly, there is a marginally significant treatment effect of a 9.6% decrease in support when shown the graphs with the phrase in comparison to the control. 23 Table 2: The Effects of Treatment Groups on Dependent Variable Support Graphs with Phrase Control vs. Graphs Control vs. "Male Inequality" vs. with Phrase "Male Graphs Without Graphs Without Phrase Inequality" Phrase Percent Difference in Support 9.27% -9.65% -.38% of Reducing Disparities for (0.05167) . (0.05059) . (0.051390) Men Percent Difference in Support -2.21% -2.95% -5.16% of Increasing Government (0.05270) (0.05150) (0.05336) Focus on Men’s Issues Percent Difference in Support 3.38% -1.90% 1.48% of Feminist Policies (0.04181) (0.04154) (0.04161) N Value N=363 N=376 N=351 Note: These were difference-in-means tests. Although coded into binary for data analysis, each dependent variable initially evaluated support on a scale of 1-5. 1 was least supportive, while 5 was most supportive. Respondents who gave a neutral response were coded as 0—with the negative responses. Mean values in standard text. Standard errors in parentheses. N values represent the total number of respondents that are included in the analysis (for instance, the N value of the second column accounts for the combined number of respondents who received both the treatment graphs with and without the phrase “male inequality.”) 24 Table 3 demonstrates that three statistically significant treatment effects exist when evaluating—separated by sex—the first dependent variable question on reducing disparities for men. For men, there appears to be a statistically significant decrease in support by 15.64% when shown the control compared to the graphs without the phrase; in other words, being shown the graphs led to an increase in support. A marginally significant effect of a decrease in support among men by 11.97% when shown the control in comparison to graphs with the phrase also exists. This demonstrates that men appear to be persuaded by both treatment groups to an extent, but being shown the graphs without the phrase seems to be more effective. For women, two significant treatment effects exist on the first dependent variable question. The first is a 17.73% increase in support when shown graphs without the phrase as opposed to being shown graphs with the phrase, indicating that the phrase “male inequality” does lead to a reduction in support among women. The 17.73% increase in support may be due to the aforementioned negative associations with the phrase “male inequality”, which appear to be particularly salient for women. This may be due to the belief that the phrase is associated with anti-feminist values, causing a negative backlash towards supporting male-focused policies. The second is a 15.32% increase in support when shown the graphs without the phrase in comparison to the control, demonstrating that women’s likelihood of support towards reducing disparities for men increases when shown relevant statistical information. This is likely due to the lack of elements that could trigger a negative backlash, since there is no clear implication that these graphs invalidate women’s issues. No other statistically significant or marginally significant treatment effects among gender were found. 25 Table 3: The Effects of Treatment Groups on Dependent Variable Support (Among Genders) Graphs Control vs. Control vs. Graphs with Control vs. Control vs. with Graphs Graphs Phrase "Male Graphs with Graphs Without Phrase with Phrase Without Phrase Inequality" vs. Phrase "Male Phrase "Male "Male Graphs Inequality" Inequality Inequality" Without Phrase " vs. Graphs Without Phrase Men Men Men Women Women Women Percent -3.67% -11.97% -15.64% 17.73% -2.40% 15.32% Difference (0.07671) (0.07008) . (0.07268) * (0.07148) * (0.07322) (0.07272) * in Support of Reducing Disparities for Men Percent -7.08% -0.78% -7.86% 0.71% -1.77% -2.48% Difference (0.07595) (0.071828) (0.07533) (0.073841) (0.07311) (0.07549) in Support of Increasing Governme nt Focus on Men’s Issues Percent -2.07% .53% -1.54% 7.02% -4.10% 2.92% Difference (0.06953) (0.066460) (0.06939) (0.04976) (0.05139) (0.04701) in Support of Feminist Policies N value N=166 N=178 N=168 N=184 N=188 N=176 26 Note: These were difference-in-means tests. Although coded into binary for data analysis, each dependent variable initially evaluated support on a scale of 1-5. 1 was least supportive, while 5 was most supportive. Respondents who gave a neutral response were coded as 0—with the negative responses. Mean values in standard text. Standard errors in parentheses. N values represent the total number of respondents that are included in the analysis (for instance, the N value of the second column accounts for the combined number of respondents who reported themselves as men that received both the treatment graphs with and without the phrase “male inequality.”) For the second, third and fourth column, only respondents who reported themselves as men were included. For the fifth, sixth and seventh column, only respondents who reported themselves as women were included. 27 For Table 4, which splits between political parties, two statistically significant treatment effects exist along with one marginally significant effect. For Democrats, being shown the graphs with the phrase lead to a decrease in support for reducing male disparities by 13.94%, indicating that the phrase is likely to reduce the support of liberals. This may be due to Democratic respondents perceiving the phrase as associated with right-leaning groups, causing a decrease in support. A marginally significant increase in support of 11.71% when shown the graphs without the phrase also exists for Democrats, furthering the idea that “male inequality” is likely to reduce the support of Democrats. For Republicans, one statistically significant treatment effect exists: an increase in support by 31.89% for feminist issues when shown the graph without the phrase in comparison to the control. One reason for this may be that Republicans are more likely to support feminist policies in this survey because they feel that male issues were being directly addressed by the treatment. This offers potential opportunities to encourage Republican support for feminist issues: although it could seem counterintuitive, a powerful way to do so may be by addressing male issues. In general, Republican sample sizes are much smaller than Democratic sample sizes for this survey, which may have concealed other treatment effects that would exist with a larger Republican sample. Still, this treatment effect offers intriguing possibilities to create more feminist and Republican agreement. 28 Table 4: The Effects of Treatment Groups on Dependent Variable Support (Among Political Parties) Graphs Control vs. Control Graphs with Control vs. Control vs. with "Male Graphs vs. Graphs "Male Graphs with Graphs Inequality with Without Inequality "Male Without Phrase" vs. "Male Phrase Phrase" vs. Inequality Phrase Graphs Inequality Graphs Phrase" Without Phrase" Without Phrase Phrase Democrats Democrats Democrats Republicans Republicans Republicans Percent 11.71% -13.94% -2.31% 18.41% -10.98% 7.43% Difference (0.06614) . (0.06538) * (0.06454) (0.1580) (0.1562) (0.1614) in Support of Reducing Disparities for Men Percent -3.24% -2.06% -5.30% -12.28% -13.73% -26.01% Difference (0.06854) (0.06829) (0.06973) (0.1595) (0.14186) (0.1557) in Support of Increasing Government Focus on Men’s Issues Percent 3.92% -2.06% 1.86% 5.12% 26.77% 31.89% Difference (0.03884) (0.04051) (0.03728) (0.16387) (0.14621) (0.1557) * in Support of Feminist Policies N value N=215 N=216 N=207 N=40 N=42 N=36 29 Note: These were difference-in-means tests. Although coded into binary for data analysis, each dependent variable initially evaluated support on a scale of 1-5. 1 was least supportive, while 5 was most supportive. Respondents who gave a neutral response were coded as 0—with the negative responses. Mean values in standard text. Standard errors in parentheses. N values represent the total number of respondents that are included in the analysis (for instance, the N value of the second column accounts for the combined number of Democrat respondents who received both the treatment graphs with and without the phrase "male inequality.") For the second, third and fourth column, only Democrat respondents were included. For the fifth, sixth and seventh column, only Republican respondents were included. 30 Internal validity should be overall high but is potentially somewhat confounded by the possibilities of a lack of engagement with the treatment, respondents viewing the surveys of others, respondents who received the survey over Canvas announcement discussing the survey with others who hadn’t already taken it and the shifts in sample population across the different college courses and Prolific survey. First, it is possible respondents who received treatment did not engage with the treatment strongly, although the “Are you surprised?” questions should mitigate that. Second, it’s possible some students who took the survey near others looked at the different responses or treatments of others and had their own responses influenced. Third, because students in PS 275: Legal Process took the survey as part of a Canvas announcement as opposed to an in-class setting, they may have had numerous opportunities to discuss the survey with their classmates—and if someone who hadn’t yet taken the survey heard such discussion, for instance, the results may have been influenced. Fourth, the different samples from different classes may have influenced the results. For instance, the political knowledge of students in the introductory course PS 201: US Politics may have differed significantly from the higher-level (300-level) courses. However, none of these potential issues are highly harmful to the internal validity of the sample—overall, the research methods seem likely to create internal validity in spite of these potential minor confounders. The external validity of this survey has one major confounder: sample demographics. The University of Oregon sample demographics are very different from the public at large because of the usage of a convenience sample from university political science courses. Although there is benefit to seeing the opinions of college students who are learning about politics for a survey related to education and political goals, it also is a powerful confounder for the results. Secondly, the combination of this sample with the Prolific sample may have created demographic 31 differences, since the University of Oregon sample is highly location based, but the Prolific sample pulled from nationwide respondents. Overall, these two issues may create some issues with external validity, but the treatment effect and data observed still has value and may indicate that treatment effects would exist in samples that included broader demographics. One threat to inference may be a feeling of respondents that they are expected to support the dependent variable questions on male disparities and government focus—in other words, a demand effect. This is especially likely for the treatment groups. If respondents believed the goal of the graphs was to increase their support on those two dependent variable questions, they may have been influenced by a Hawthorne effect. A Hawthorne effect may also exist for the control group if they felt they were expected to answer the first two dependent variables in a certain way, but it is comparatively less likely to be significant. Balance checks appear to indicate the first two dependent variables have a similar gender ratio (Treatment group 1 has 88 men and 98 women; treatment group 2 has 78 men and 86 women, and the control group has 90 men and 90 women.) For political party, treatment group 1 has 112 Democrats and 23 Republicans, treatment group 2 has 103 Democrats and 17 Republicans, and the control group has 104 Democrats and 19 Republicans. This presents an issue with evaluating support and treatment effects for those identify as Republican. Limitations Some limitations of this survey include the sample demographics, a self-selection bias for the University of Oregon sample, question wording and order, a potential social desirability bias, and long-term effects. First, the samples were only of college students, and therefore any treatment effects cannot be assuredly inferred to apply to the population at large—in other words, the sample demographics somewhat harm the external validity of the survey. 32 Second, this issue with sample demographics could be magnified by the differences between the University of Oregon sample and the Prolific sample. When coupled with the fact that the University of Oregon sample took responses only from Political Science classes, the potential demographic and political differences as opposed who was surveyed in the Prolific sample—which was restricted to American college students of any major—could confound the results. Third, the question wording and order of the survey could have impacted the results. The two dependent variable questions on male issues had different phrasing, even though they are very similar conceptually. For reference, the first was: “Some have proposed policies designed to reduce disparities in education and mental health outcomes that disproportionately impact men. Would you support this?”. The second was: “How likely would you be to support an increased government focus on men's issues?”. The wording of these two questions did seem to impact the results—support was generally lower for the second dependent variable question, Table 1b demonstrates notable differences in support for the two questions by political party as well as gender, and statistically significant treatment effects were only found for the first dependent variable question. Also, the order of the dependent variable question could have impacted the results for the third dependent variable question on feminist policies in particular. For reference, that was: “How likely are you to support feminist policies designed to address gender discrimination against women?”. Respondents may have felt obligated to support these feminist policies if they were in favor of the first two dependent variables, out of a sense of fairness or equitability. Fourth, a social desirability bias could have impacted the results. This could be impactful for all dependent variables. On one hand, for the first two, respondents may have believed they 33 need to respond in favor of male policies due to social pressure. This is most likely to have been impactful for respondents in either treatment group. On the other hand, some respondents may have believed they needed to answer negatively or neutrally towards supporting male policies because they believed that it was socially unacceptable to state they supported them. This is most likely to have been impactful for respondents in the control group. Additionally, responses for the third dependent variable could have been impacted by a social desirability bias if they believed supporting feminist policies was socially desirable. This is likely to have been impactful regardless of whether they saw either treatment or the control. Fifth, this survey cannot evaluate the long-term effects of treatment. Because students only took the survey once and no identifiable data was recorded, seeing if either treatment leads to differences in opinion beyond the timeframe of the survey is impossible. This could be reconciled in a future study with follow-up questions, but evaluating any long-term effects is outside the scope of this paper. Overall, these limitations present potential challenges for applying the survey results to a larger audience; but, the survey still offers value in understanding the opinions of college students, their baseline support for men’s issues, and how both treatment groups impacted their opinions. Because possible policy interventions for these gaps would primarily take place among young people within the school system, this offers insight into a relevant demographic. The opinions of college students on educational issues has importance in determining how those within the educational system perceive gaps in male educational outcomes and policy designed to address those gaps. Additionally, there is a possibility that these results could be applied to a larger population, although more research should be done with a nationally representative sample in order to make conclusive determinations. 34 Conclusion This study found that men’s issues may receive majority support from college students, that the phrase “male inequality” harms support for male policy issues, and that Republicans may be more likely to support feminist policies when they feel male issues are being discussed. The overall support for the first two dependent variables on men’s issues may indicate discussing the topic is more as politically tenable than many may believe. When separating by gender, a statistically significant decrease in support for men by 15.64% on reducing male disparities is seen when shown the control compared to the graphs without the phrase. For women, there is an increase by 17.73% in support when shown graphs without the phrase in comparison to being shown graphs with the phrase. There is also a 15.32% increase in support when shown the graphs without the phrase in comparison to the control. Taken altogether, this demonstrates that the phrase “male inequality” somewhat decreases support among respondents when separating by gender, and that being shown graphs featuring male disparities may increase support. When separating by political party, the negative impact of “male inequality” on support for reducing gender, disparities for Democrats is 13.94%—reinforcing the idea that “male inequality” can harm support. For Republicans, seeing the graphs featuring male disparities appears to counterintuitively increase support for feminist policies by 31.89%. This should present opportunities to create agreement among Republicans and feminists. Politicians and political actors can utilize this relationship in order to create further collaboration on policy while reducing political and gender polarization. For future research, more varied demographics would be helpful to evaluate a higher variety of perspectives from different geographic areas, ages, and races. Exploring alternatives to the phrase “male inequality” may be compelling to see if they lead to an increase in support. 35 Adding a treatment group featuring graphs of gender discrimination against women may also uncover if respondents support for men’s issues is impacted by discussing women’s issues. In order to uncover ways to increase support for men’s issues among women, future research could attempt a stronger treatment by asking respondents to watch a more in-depth video discussing the topic; alternatively, it may be effective to show respondents graphs discussing male and female societal disparities to evaluate if doing so would be more persuasive for women. Further attention could also be placed to the relationship between Republican and feminist support after seeing the phrase with qualitative research specifically geared toward discovering why this relationship exists on a psychological level—to do so, interviews with political psychologists could be helpful. 36 Appendix: Survey Start and End Card For the survey’s makeup and flow, respondents saw this informed consent text before the survey: This survey is being used for research purposes. This research is interested in your attitudes and opinions towards various issues facing society today and will ask you to review sample graphs and fill out relevant responses. Not all respondents will receive all questions. Participation is voluntary. You may contact the researcher, Drew Collins- Burke, at 541-977-7350 or dcollin7@uoregon.edu. By completing the survey, you agree to participate in this research. You must be at least 18 years old to participate in this study. Respondents saw this informed consent text after the survey: This survey is being used for research purposes. This research is interested in your attitudes and opinions towards various issues facing society today and will ask you to review sample graphs and fill out relevant responses. Not all respondents will receive all questions. Participation is voluntary. You may contact the researcher, Drew Collins- Burke, at 541-977-7350 or dcollin7@uoregon.edu. By completing the survey, you agree to participate in this research. You must be at least 18 years old to participate in this study. 37 Appendix: Dataset Dataset for all Original Tables Sta En St IP Pr Durati Fi Reco Resrt Ad og nis pon Recipi Recipi Reci Exter entFir pient nalRe Locat Locati Distrib User G Lan en Pol Q23_ Q23_ Q23_ Q23_ Page. Click. Q Q24_ Q24_ Q24_ Q24_ Q Q26_ Q26_ Q26_ Q26_ Q Q27_ Q27_ Q27_ Q27_ Q De De DepV T Create.New.Fiel Da dD at dre res on..in.s he rded seI entLas stNam Emai ferenc ionLa onLon utio nC guag de iPa First. Last. Subm Coun 1 First. Last. Page. Click. Page. Click. Page. Click. pV pV arFe r ate us econds. Date tName titude gitude hannel rty Click Click 6 Click Click Subm Coun 1 First. Last. 8 Click Click Subm Coun 2 First. Last. 2 d.or.Choose.Fro te ss s d d e l e e r it t it t it t 0 Click Click Subm Coun ar ar mSu e it t 1 Q1 Q2 p at m.Dropdow n... 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Ag 2 03- 03- 5.1 2024- 10 03- m 37, - ano 8 02 18: 02 18: 0 28. 0 67 1 02 18:4 Md 389 122, 083 nym EN 1 3 5,14 15,7 16,7 6 81 34 4 1 5,04 5,04 24,6 1 1 61 1 1 5 15 14 1 43:44 44:52 12 4:53 J0 7 ous 8 NK 2 6V m R_ 6m 2024- 2024- 76.78. 2024- PIe2 03- 03- 0 13 10 54 1 03- dx 33, - ano 9 02 18: 02 18: 0 02 18:4 ki8 497 86,8 nym EN 2 3 3 12 14 3 8.5 46:53 47:48 6 7:49 wU 9 125 ous an A R_ 17 7Bl 2024- 2024- 2.5 2024- G 3 03- 03- 0 9.1 10 03- Mu 38, - ano 0 02 18: 02 18: 69. 0 82 1 02 18:4 Lq 740 90,3 nym EN 2 3 7,59 14,4 14,7 4 4 2,52 10,3 11,02 65 2 86 36 12 10 3 14 15 2 47:10 48:33 14 8:33 uA 8 37 ous 8 KqRp w R_ 6sj 2024- 2024- 24.24 2024- J1J3 03- 03- 10 03- 01 43, - ano 1 02 18: 02 18: 0 0.3 0 52 1 02 18:4 OO 068 89,4 nym EN 2 3 2 13 12 3 48:22 49:15 4.4 9:15 A2 4 293 ous 6 5Q r R_ 3H 2024- 2024- 98.46. 2024- ttv3 03- 03- 11 10 03- qG 44, - ano 2 02 18: 02 18: 0 0 23 1 02 18:5 ay 970 93,2 nym EN 2 1 5 14 14 3 50:14 50:38 2.1 0:39 CY 5 389 ous 1 op Wk R_ 17 6U 2024- 2024- 4.2 2024- N2 3 03- 03- 0 11. 10 03- Va 27, - ano 3 02 18: 02 18: 16 0 45 1 02 18:5 pz8 948 82,5 nym EN 1 3 4 14 14 3 50:16 51:02 1.8 1:02 L6j 2 036 ous 8 9Z L R_ 75. 3c 2024- 2024- 10 2024- EK 3 03- 03- 28, - ano 4 02 18: 02 18: 0 2.1 10 15 03- HP 32. 0 72 1 02 18:5 Pp 076 82,4 nym EN 1 2 4 15 13 3 25:41 51:54 21 1:54 9tB 8 46 ous 6 23 8B R_ 17 62 2024- 2024- 2.5 2024- ntS 3 03- 03- 8.1 10 11 03- Qq 42, - ano 1,91 21,6 9,39 14,4 15,2 5 02 18: 02 18: 0 24. 0 4 1 02 18:5 23j 357 83,2 nym EN 2 1 22,3 5 1 5 162 ous 8 7 8 49 01 2 2 4 14 14 1 51:01 52:55 10 2:56 do 9 HT j R_ 67. 16 2024- 2024- 18 2024- g8 - 3 03- 03- 2.2 10 03- 7r 41, 111, ano 22,7 22,7 23,9 3,70 3,70 14,7 6 02 18: 02 18: 0 12. 0 72 1 02 18:5 RP 271 988 nym EN 1 3 3 ous 4 4 85 1 3 5 5 63 1 1 3 13 11 1 52:20 53:33 22 3:34 0ff 1 7 bE Me R_ 5R 2024- 2024- 69. 2024- aS 3 03- 03- 1.1 S 34, - ano 02 18: 02 18: 0 95. 10 7 36 1 03- Wc 680 82,8 nym EN 2 3 3 12 14 3 53:18 53:55 16 0 02 18:5 3:55 Jg 5 231 ous 1 Yd Xx 75 R_ 7d 2024- 2024- 17 2024- uz 3 03- 03- 4.2 10 03- mc 28, - ano 0,82 10,1 11,2 0,72 10,8 11,9 8 02 18: 02 18: 0 40. 0 58 1 02 18:5 Jfk 634 81,6 nym EN 1 2 67. 4 221 ous 5 57 88 12 2 8 8 91 10 1 4 15 14 1 53:27 54:25 10 4:25 aAKl vb R_ 10 6C 2024- 2024- 4.3 2024- rJu 3 03- 03- 6.1 10 36, - ano 9 02 18: 02 18: 0 41. 0 54 1 03- yA 8,19 8,19 10,8 3,19 3,19 11,8 02 18:5 qC 133 82,4 nym EN 1 1 1 1 1 1 5 15 12 1 53:34 54:29 17 4:29 mu 2 122 ous 4 4 61 7 7 9 8 Hf SM 17 R_ 2024- 2024- 4.1 2024- 5V - 4 03- 03- 95. 10 03- 3na 34, 117, ano 11,6 13,0 14,1 1,40 9,13 10,6 0 02 18: 02 18: 0 19 0 75 1 02 18:5 r4g 006 443 nym EN 1 3 26 92 15 3 4 4 8 97 9 4 4 14 14 1 53:33 54:49 3.7 4:49 ttE 2 X6 2 ous 6 lb R_ 6J 2024- 2024- 24. 2024- W0 4 03- 03- 0 46. 10 64 1 03- C9 40, - ano 0,60 20,7 21,4 0,78 9,68 11,4 1 02 18: 02 18: 46. 0 02 18:5 RN 579 73,9 nym EN 2 3 HK 5 889 ous 1 55 78 6 3 4 4 25 5 10 5 12 15 2 58:26 59:31 39 9:31 U6 fK k 40 Sta En St IP Pr Durati Fi Reco Res Recipi Recipi Reci Exter Locat Locati Distrib User G Pol Q23_ Q23_ Q23_ Q23_ Q Q24_ Q24_ Q24_ Q24_rt Q Q26_ Q26_ Q26_ Q26_ Q27_ Q27_ De De DepV T dD at Ad og on..in.s nis rded pon entLas entFir pient nalRe ionLa onLon utio nC Lan en iPa First. 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R_ 1 2024- 2024- 12 2024- 32r - 9 03- 03- 5.1 10 03- KZ 44, 122, ano 28,3 28,3 29,7 4,73 4,73 14,3 5 03 02: 03 02: 0 68. rbd 5310 80 1 03 02:4 886 nym EN 1 1 67 67 52 1 2 7 7 69 1 9 3 13 14 2 39:01 40:21 13 0:22 7pf 9 5 ous TU 3 UN 51 Sta En St IP Pr Durati Fi Reco Res Recipi Recipi Reci Exter Locat Locati Distrib User G Pol Q23_ Q23_ Q23_ Q23_ Q Q24_ Q24_ Q24_ Q24_ Q Q26_ Q26_ Q26_ Q26_rt Ad og nis pon entFir pient nalRe Q Q27_ Q27_ Q27_ Q27_ Q De De DepV T Create.New.Fiel Da dD at dre res on..in.s he rded seI entLas stNam Emai ferenc ionLa onLon utio nC Lan en Page. Click. guag de iPa First. Last. Subm Coun 1 First. Last. Page. Click. Page. Click. Page. Click. pV pV arFe r Subm Coun 1 First. Last. Subm Coun 2 First. Last. Subm Coun 2 ar ar mSu e d.or.Choose.Fro te ate us ss s econds. d Date d tName e l e titude gitude hannel e r rty Click Click it t 6 Click Click it t 8 Click Click it t 0 Click Click it t 1 Q1 Q2 p at m.Dropdow n... R_ 47. 6v 1 2024- 2024- 22 2024- 2rF - 9 03- 03- 9.2 10 03- Tc 34, 118, ano 6 03 03: 03 03: 0 08. 0 33 1 03 03:0 LB 156 086 nym EN 1 2 4 15 13 3 07:45 08:18 15 8:18 br 3 8 ous 8 Ok eE R_ 67. 7z5 1 2024- 2024- 18 2024- wC03- 03- 3.1 10 03- gL 47, - 117, ano9 7 03 03: 03 03: 0 81. 0 50 1 03 03:0 5b 646 265 nym EN 1 3 2 14 12 3 07:35 08:25 21 8:26 qU 2 7 ous 3 Zg DT R_ 10 1M 1 2024- 2024- 7.1 2024- 57 38, - ano 9 03- 03- 10 03- NB 121, 18,0 36,5 37,2 1,38 9,09 10,7 8 03 03: 03 03: 0 27. 0 97 1 18. 03 03:2 92 635 455 nym EN 2 3 06 15 87 4 1 7 4 02 5 1 5 14 14 1 22:44 24:22 4:22 Ce 3 8 ous 45 hil oy R_ 6I4 1 2024- 2024- 50. 2024- tKz - 9 03- 03- 15 10 36, ano 03 03: 03 03: 0 8.1 0 94 1 03- Wb 115, 03 03:2 k1 058 080 nym EN 1 1 5 14 15 3 9 28:24 29:58 76.28 9:59 Bt 6 3 ous P9 T R_ 1H 2 2024- 2024- 98. 2024- YK 0 03- 03- 0 20. 10 11 1 03- Uu 37, - ano 13,5 19,4 20,7 4,45 10,7 11,8 03 03: 03 03: 15 0 5 03 03:3 Ht 611 93,4 nym EN 2 1 11 44 69 4 4 7 13 55 5 12 3 12 14 2 0 29:37 31:33 3.7 1:33 XJi 2 14 ous Gy Rz 20 R_ 2 2024- 2024- 6.1 2024- 5O 0 03- 03- 73. 10 03- jDt 41, - ano 03 03: 03 03: 0 10 0 22 1 03 03:4 z2l 215 86,7 nym EN 1 1 4 15 14 3 1 48:36 48:58 6.2 8:58 Uv 8 77 ous 2 igqKt R_ 1D 2 2024- 2024- 74. 2024- wi C - ano 0 03- 03- 79. 10 03- 43, 6,15 10,6 11,7 1,75 11,0 2 03 03: 03 03: 0 24 0 54 1 03 03:5 W 075 76,9 nym EN 1 2 5 93 46 6 3 7 9,62 16 9 12 4 15 14 2 56:25 57:19 7.7 7:20 Bj 892 ous Pyt Gu hd R_ 6M 2 2024- 2024- 21 03- 03- 6.1 2024- 6Zr 10 03- 6bJ 40, - ano0 03 04: 03 04: 0 65. 0 43 1 03 04:0 LC 735 73,9 nym EN 1 1 4,73 6,74 11,0 1,28 2,33 12,7 3 95. 9 904 ous 9 3 68 3 1 1 4 28 3 10 3 13 14 2 01:33 02:16 84 2:16 T2BC w R_ 70. 1ch 2 2024- 2024- 16 2024- ZF03- 03- 1.1 10 03- Esl 36, - ano0 03 04: 03 04: 0 58. 0 60 1 03 04:1 0R 796 76,1 nym EN 2 1 4 14 15 3 4 15:03 16:03 10 6:04 0S 9 797 ous 9 Dw i R_ 5H 2 2024- 2024- 96. 03- 03- 23 2024- 6K 10 03- 1X 41, - ano0 0 8.2 73 1 713 71,3 nym EN 2 4 21,8 28,4 29,1 5 3 2,49 9,94 11,003 04: 03 04: 0 03 04:1 gK 15 62 9 9 2 4 9 10 4 14 14 2 5 17:56 19:10 6.52 9:10 35J 9 895 ous uD ol R_ 71. 6ni 2 2024- 2024- 21 2024- pL 28, - ano 0 03- 03- 4.1 10 03- LS 23,0 23,0 24,1 11,7 6 03 05: 03 05: 0 29. 0 66 1 03 05:1 eo 527 81,6 nym EN 1 3 51 51 82 1 4 8,59 8,59 45 1 11 2 12 11 2 14:01 15:08 19 5:08 FA 4 012 ous 2 jaS R R_ 71 2 2024- 2024- 72. 2024- Ly 0 03- 03- 23. 0 19 10 47 1 03- 2b 41, - ano 80,6 nym EN 1 1 17,1 17,1 18,603 05: 03 05: 0 03 05:3 kef 022 07 07 66 1 2 2,84 2,84 11,5 7 7 46 1 9 4 12 13 2 7 38:26 39:14 1.101 9:14 wE 659 ous a7l 1 R_ 3Ej 2 2024- 2024- 71. 19 2024- oa8 0 03- 03- 0.8 10 10 03- wO 40, - ano 12,9 14,5 18,3 5,20 5,20 12,2 03 05: 03 05: 0 0 1 1 03 05:5 vp 834 73,9 nym EN 1 3 2 1 1 1 4 15 13 1 8 79 44 68 2 2 26 49:12 50:53 8.228 0:53 uD 3 235 ous EK U R_ 17 6g 2 2024- 2024- 3.7 2024- Qw 0 03- 03- 7.2 10 03- u4 40, - ano 851 73,8 nym 0,85 9,65 11,5 14,5 18,0 18,803 06: 03 06: 0 28. 0 69 1 03 06:0 84 EN 1 1 3 7 56 6 2 35 49 24 2 11 1 14 14 2 9 08:01 09:11 13 9:11 L5r 8 705 ous 6 SB fH 52 Sta En St IP Pr Durati Fi Reco Res Recipi Recipi Reci Exter User G Q23_ Q23_ Q24_ Q24_ Q26_ Q26_ Q27_ Q27_ De De DepV T rt dD at Ad og on..in.s nis rded pon entLas entFir pient nalRe Locat Locati Distrib Pol Q23_ Q23_ Q Q24_ Q24_ Q Q26_ Q26_ Q Q27_ Q27_ Q Create.New.Fiel Da dre res he seI stNam Emai ferenc ionLa onLon utio nC Lan en iPa First. Last. Page. Click. Page. Click. Page. Click. Page. Click. pV pV arFe rguag de Subm Coun 1 First. Last. Subm Coun 1 First. Last. Subm Coun 2 First. Last. Subm Coun 2 ar ar mSu e d.or.Choose.Fro te ate us ss s econds. d Date d tName e l e titude gitude hannel e r rty Click Click it t 6 Click Click it t 8 Click Click it t 0 Click Click it t 1 Q1 Q2 p at m.Dropdow n... R_ 17 5R 2 2024- 2024- 4.2 2024- aX 1 03- 03- 0 08. 10 11 03- F0 38, - ano 53,0 54,1 7,20 8,07 0 03 06: 03 06: 96. 0 5 1 03 06:1 BZ 664 121, nym EN 1 3 1,31 8 4 14,6 3 4 4 14 12 1 16:37 18:32 10 8:33 lA 5 487 ous 31 01 1 2 3 5tQF n R_ 17 5g 2 2024- 2024- 2.7 2024- Ot 1 03- 03- 0 5.1 10 03- gh 34, - ano 4,08 7,05 11,8 5,24 30,1 45,0 03 06: 03 06: 39. 1 0 85 1 03 06:3 gg 156 79,3 nym EN 2 1 3 5 5 5 4 15 15 1 36:19 37:45 22 7:45 ga 2 898 ous 9 7 01 3 2 29 8 Qkus w R_ 3C 2 2024- 2024- 50. 2024- SN 1 03- 03- 23 10 10 03- Ljh 36, - ano 12,8 12,8 14,7 10,2 10,2 12,7 03 06: 03 06: 0 1.1 0 5 1 03 06:4 Pu 178 86,8 nym EN 2 1 93 93 49 1 1 83 83 19 1 1 3 13 14 1 2 39:11 40:56 53. 0:56 TF 9 035 ous 5 qR Al R_ 58 2 2024- 2024- 10 2024- 5N 1 03- 03- 0.3 0 3.8 10 6lp 40, - ano 03 06: 03 06: 0 30 1 03- 03 06:5 xr 763 73,7 nym EN 2 4 5 13 15 3 3 49:51 50:21 3.1 0:21 M 7 695 ous 47 RJ NY d R_ 65 2 2024- 2024- 76. 34. 2024- Gh 39, - ano 1 03- 03- 10 03- dg 1,41 13,0 13,6 1,91 12,9 13,003 06: 03 06: 0 82. 0 51 1 03 06:5 9m 991 83,0 nym EN 2 2 4 18 4 034 ous 6 51 63 5 4 5 98 17 6 2 3 12 13 1 49:56 50:47 0 0:48 SMI91 0L R_ 6m 2 2024- 2024- 68. 2024- XA 1 03- 03- 84. 0 22 10 11 1 03- F5 40, - ano 035 75,1 nym EN 1 1 23,5 23,5 36,4 1 2 15,2 15,2 16,403 06: 03 06: 0 6 03 06:5 FT 93 93 67 67 56 1 2 4 14 15 1 5 52:38 54:35 3.100 4:35 bE 6 763 ous Md tIL R_ 3Q 2 2024- 2024- 68. 03- 03- 60. 2024- KT 1 0 11 10 35 1 03- 0ab 42, - ano 03 07: 03 07: 0 03 07:0 kl 195 83,4 nym EN 2 3 5 14 15 3 6 00:00 00:36 1.12 0:36 Aa 5 894 ous 2b mx Showing 1 to 24 of 555 entries, 44 total columns This table holds every datapoint used for original analysis in this thesis. (Font size kept small for formatting purposes.) 53 Appendix: R Data Analysis Code This R code was used to find information for all original tables presented in this thesis. rm(list = ls());library(foreign); library(readstata13); library(dplyr); library(tidyr); library(tidyverse) df <- read.csv("/Users/drewcollinsburke/Desktop/mydata/Thesis/Master Data Thesis 4 3 24.csv",sep= ";") desired_value <- 040 df2 <- df #Setting gender to only men and women df2$Gender[df2$Gender >= 3] <- NA #Re-coding independent variables into binary df2$DVQ1Bn <- 0 df2$DVQ1Bn[df2$DepVarQ1 == 1] <- 0 54 df2$DVQ1Bn[df2$DepVarQ1 == 2] <- 0 df2$DVQ1Bn[df2$DepVarQ1 == 3] <- 0 df2$DVQ1Bn[df2$DepVarQ1 == 4] <- 1 df2$DVQ1Bn[df2$DepVarQ1 == 5] <- 1 df2$DVQ2Bn <- 0 df2$DVQ2Bn[df2$DepVarQ2 == 11] <- 0 df2$DVQ2Bn[df2$DepVarQ2 == 12] <- 0 df2$DVQ2Bn[df2$DepVarQ2 == 13] <- 0 df2$DVQ2Bn[df2$DepVarQ2 == 14] <- 1 df2$DVQ2Bn[df2$DepVarQ2 == 15] <- 1 df2$DVQ3Bn <- 0 df2$DVQ3Bn[df2$DepVarFemSup == 11] <- 0 df2$DVQ3Bn[df2$DepVarFemSup == 12] <- 0 55 df2$DVQ3Bn[df2$DepVarFemSup == 13] <- 0 df2$DVQ3Bn[df2$DepVarFemSup == 14] <- 1 df2$DVQ3Bn[df2$DepVarFemSup == 15] <- 1 #Setting up treatment groups without each other in it to regress. Using table command for N value's later df2$Treat1or2 <- NA df2$Treat1or2[df2$Treat == 1] <- 0 df2$Treat1or2[df2$Treat == 2] <- 1 table(df2$Treat1or2[na.rm=T]) df2$Treat1or3 <- NA df2$Treat1or3[df2$Treat == 1] <- 0 df2$Treat1or3[df2$Treat == 3] <- 1 table(df2$Treat1or3[na.rm=T]) 56 df2$Treat2or3 <- NA df2$Treat2or3[df2$Treat == 2] <- 0 df2$Treat2or3[df2$Treat == 3] <- 1 table(df2$Treat2or3[na.rm=T]) #Finding baseline support values (Table 1a) summary(df2$DVQ1Bn[df2$Treat1or2==0]) summary(df2$DVQ2Bn[df2$Treat1or2==0]) summary(df2$DVQ3Bn[df2$Treat1or2==0]) summary(df2$DVQ1Bn[df2$Treat1or2==1]) summary(df2$DVQ2Bn[df2$Treat1or2==1]) summary(df2$DVQ3Bn[df2$Treat1or2==1]) 57 summary(df2$DVQ1Bn[df2$Treat1or3==1]) summary(df2$DVQ2Bn[df2$Treat1or3==1]) summary(df2$DVQ3Bn[df2$Treat1or3==1]) #Finding baseline support for the control by gender and political party (Table 1b) df_control <- df2[df2$Treat == 3, ] table_control <- aggregate(cbind(DVQ1Bn, DVQ2Bn, DVQ3Bn) ~ Gender + PoliParty, data = df_control, FUN = mean, na.rm = TRUE) print(table_control) #Getting N-Values table(df_control$Gender, df_control$PoliParty) #Testing for treatment effect (Table 2) summary(lm(df2$DVQ1Bn~df2$Treat1or2, data=subset(df2))) 58 summary(lm(df2$DVQ2Bn~df2$Treat1or2, data=subset(df2))) summary(lm(df2$DVQ3Bn~df2$Treat1or2, data=subset(df2))) #Treat1or3 and Treat2or3 are multiplied by -1 to represent the difference between control and treatment. Otherwise the values represent the difference between treatment and control, which may be confusing. summary(lm((-1*df2$DVQ1Bn)~df2$Treat1or3, data=subset(df2))) summary(lm((-1*df2$DVQ2Bn)~df2$Treat1or3, data=subset(df2))) summary(lm((-1*df2$DVQ3Bn)~df2$Treat1or3, data=subset(df2))) summary(lm((-1*df2$DVQ1Bn)~df2$Treat2or3, data=subset(df2))) summary(lm((-1*df2$DVQ2Bn)~df2$Treat2or3, data=subset(df2))) summary(lm((-1*df2$DVQ3Bn)~df2$Treat2or3, data=subset(df2))) 59 #Finding N values for men and women table(df2$Treat1or2[df2$Treat1or2==0 & df2$Gender==1]) table(df2$Treat1or2[df2$Treat1or2==1 & df2$Gender==1]) table(df2$Treat1or3[df2$Treat1or3==1 & df2$Gender==1]) table(df2$Treat1or2[df2$Treat1or2==0 & df2$Gender==2]) table(df2$Treat1or2[df2$Treat1or2==1 & df2$Gender==2]) table(df2$Treat1or3[df2$Treat1or3==1 & df2$Gender==2]) #Evaluating treatment among women only (Table 3) summary(lm(df2$DVQ1Bn[Gender==2]~df2$Treat1or2[Gender==2], data=subset(df2))) summary(lm(df2$DVQ2Bn[Gender==2]~df2$Treat1or2[Gender==2], data=subset(df2))) summary(lm(df2$DVQ3Bn[Gender==2]~df2$Treat1or2[Gender==2], data=subset(df2))) 60 #Again, multiplying these by -1 to represent the difference between control and treatment. summary(lm((-1*df2$DVQ1Bn[Gender==2])~df2$Treat1or3[Gender==2], data=subset(df2))) summary(lm((-1*df2$DVQ2Bn[Gender==2])~df2$Treat1or3[Gender==2], data=subset(df2))) summary(lm((-1*df2$DVQ3Bn[Gender==2])~df2$Treat1or3[Gender==2], data=subset(df2))) summary(lm((-1*df2$DVQ1Bn[Gender==2])~df2$Treat2or3[Gender==2], data=subset(df2))) summary(lm((-1*df2$DVQ2Bn[Gender==2])~df2$Treat2or3[Gender==2], data=subset(df2))) summary(lm((-1*df2$DVQ3Bn[Gender==2])~df2$Treat2or3[Gender==2], data=subset(df2))) 61 #Now men only (Table 3) summary(lm(df2$DVQ1Bn[Gender==1]~df2$Treat1or2[Gender==1], data=subset(df2))) summary(lm(df2$DVQ2Bn[Gender==1]~df2$Treat1or2[Gender==1], data=subset(df2))) summary(lm(df2$DVQ3Bn[Gender==1]~df2$Treat1or2[Gender==1], data=subset(df2))) ##Again, multiplying these by -1 to represent the difference between control and treatment. summary(lm((-1*df2$DVQ1Bn[Gender==1])~df2$Treat1or3[Gender==1], data=subset(df2))) summary(lm((-1*df2$DVQ2Bn[Gender==1])~df2$Treat1or3[Gender==1], data=subset(df2))) summary(lm((-1*df2$DVQ3Bn[Gender==1])~df2$Treat1or3[Gender==1], data=subset(df2))) summary(lm((-1*df2$DVQ1Bn[Gender==1])~df2$Treat2or3[Gender==1], data=subset(df2))) 62 summary(lm((-1*df2$DVQ2Bn[Gender==1])~df2$Treat2or3[Gender==1], data=subset(df2))) summary(lm((-1*df2$DVQ3Bn[Gender==1])~df2$Treat2or3[Gender==1], data=subset(df2))) #Checking PoliParty demographics table(df2$PoliParty) # Create binary variables for each political party df2$Democrat <- ifelse(df2$PoliParty == 1, 1, 0) df2$Republican <- ifelse(df2$PoliParty == 2, 1, 0) df2$Independent <- ifelse(df2$PoliParty == 3, 1, 0) df2$NoAffiliation <- ifelse(df2$PoliParty == 4, 1, 0) # Analyzing treatment effects by political party(Table 4) # Analyze treatment effects for Democrats summary(lm(df2$DVQ1Bn[df2$Democrat == 1] ~ df2$Treat1or2[df2$Democrat == 1], data = subset(df2))) 63 summary(lm(df2$DVQ2Bn[df2$Democrat == 1] ~ df2$Treat1or2[df2$Democrat == 1], data = subset(df2))) summary(lm(df2$DVQ3Bn[df2$Democrat == 1] ~ df2$Treat1or2[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ1Bn[df2$Democrat == 1]) ~ df2$Treat1or3[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ2Bn[df2$Democrat == 1]) ~ df2$Treat1or3[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ3Bn[df2$Democrat == 1]) ~ df2$Treat1or3[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ1Bn[df2$Democrat == 1]) ~ df2$Treat2or3[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ2Bn[df2$Democrat == 1]) ~ df2$Treat2or3[df2$Democrat == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ3Bn[df2$Democrat == 1]) ~ df2$Treat2or3[df2$Democrat == 1], data = subset(df2))) # Analyze treatment effects for Republicans summary(lm(df2$DVQ1Bn[df2$Republican == 1] ~ df2$Treat1or2[df2$Republican == 1], data = subset(df2))) 64 summary(lm(df2$DVQ2Bn[df2$Republican == 1] ~ df2$Treat1or2[df2$Republican == 1], data = subset(df2))) summary(lm(df2$DVQ3Bn[df2$Republican == 1] ~ df2$Treat1or2[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ1Bn[df2$Republican == 1]) ~ df2$Treat1or3[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ2Bn[df2$Republican == 1]) ~ df2$Treat1or3[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ3Bn[df2$Republican == 1]) ~ df2$Treat1or3[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ1Bn[df2$Republican == 1]) ~ df2$Treat2or3[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ2Bn[df2$Republican == 1]) ~ df2$Treat2or3[df2$Republican == 1], data = subset(df2))) summary(lm((-1 * df2$DVQ3Bn[df2$Republican == 1]) ~ df2$Treat2or3[df2$Republican == 1], data = subset(df2))) #Balance Checks 65 table(df2$Gender[df2$Treat1or2==0]) table(df2$Gender[df2$Treat1or2==1]) table(df2$Gender[df2$Treat1or3==1]) table(df2$PoliParty[df2$Treat1or2==0]) table(df2$PoliParty[df2$Treat1or2==1]) table(df2$PoliParty[df2$Treat1or3==1]) 66 Bibliography Adani, S., & Cepanec, M. 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