States, Stats, and Shots: The Omnibus Theoretical Value of Vaccination Rates As Predictors of US Presidential Voting Patterns

States, Stats, and Shots: The Omnibus Theoretical Value of Vaccination Rates As Predictors of US Presidential Voting Patterns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article States, Stats, and Shots: The Omnibus Theoretical Value of Vaccination Rates As Predictors of US Presidential Voting Patterns Bryan Pesta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6228815/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the predictors of U.S. presidential voting patterns, focusing on the interplay between psychological, sociocultural, and health-related factors. Using state-level data from seven presidential elections (2000–2024), the analysis evaluates the predictive power of intelligence (IQ), well-being indicators (e.g., education, income), Big Five personality traits, and COVID-19 vaccination rates. Among these, vaccination rates emerged as the strongest and most consistent predictor of state-level election outcomes, underscoring the polarization of health behaviors as a reflection of partisan identity. Additionally, suppression effects highlighted the complex interactions between demographic variables, such as racial composition and IQ, in enhancing predictive accuracy. While traditional predictors like well-being and personality traits remain relevant, the findings reveal that health-related behaviors encapsulate deeper ideological and cultural divides. By integrating established and novel predictors, this study advances the understanding of voting behavior in an increasingly polarized society and emphasizes the value of multidimensional approaches in electoral modeling. 1. Introduction The prediction of US presidential election outcomes is big business, and rests at the intersection of political strategy, social science, and media economics. Platforms like FiveThirtyEight generate substantial revenue through election forecasts, supported by partnerships with major networks such as ABC News (Silver, 2015 ). Similarly, polling and analytics firms like Gallup and Ipsos secure multi-million-dollar contracts with campaigns and organizations to conduct polling and analysis (Pew Research Center, 2021). Political campaigns themselves invest billions in predictive models to optimize resource allocation and voter outreach, particularly in pivotal battleground states (Issenberg, 2012 ). Despite the profitability of election forecasting, the field faces significant challenges, most notably the demand for accuracy. The 2016 presidential election highlighted the risks of prediction when polling forecasts underestimated Donald Trump’s likelihood of victory, thus undermining public confidence in traditional forecasting methodologies (Gelman & Azari, 2017 ). This failure emphasized the need for more nuanced and reliable predictive models, particularly as reliance on election forecasts by the public and institutions continues to grow. This study aims to evaluate the utility of well-researched variables in differential psychology as predictors of U.S. state-level voting patterns. Specifically, I explore the predictive power of state-level measures of intelligence (IQ), various indicators of human well-being (e.g., Income), and the Big Five personality traits. By analyzing data retroactively for all seven presidential elections of the 21st century, I assess the extent to which these psychological and sociological variables can explain voting patterns, with particular attention to the most recent election. Unexpectedly, preliminary analyses revealed that state-level COVID-19 vaccination rates emerged as the strongest and most robust predictor of election outcomes—not only in 2024 but across all elections this century. This surprising finding prompted further exploration into potential theoretical explanations for the effect, which are detailed in the literature review below. In sum, by investigating the interplay between differential psychology, vaccination behavior, and voting patterns, this study seeks to contribute new insights into the complex dynamics of American electoral politics. 1.1. State-level estimates of intelligence and voting patterns Understanding the relationship between state-level IQ and voting behavior requires an exploration of nuanced statistical interactions. For instance, Pesta and McDaniel ( 2014 ) reported that IQ estimates for the 50 U.S. states (derived by McDaniel, 2006 ) predicted whether a state voted Republican (Red) or Democratic (Blue), but only after accounting for the percent of state residents who identified as either Black or Hispanic (i.e., “percent Minority”). Specifically, in bivariate analyses, Pesta and McDaniel reported negligible correlations between state IQ or state percent Minority and election outcomes. However, when both variables were included in the same regression model, their predictive power increased substantially, illustrating mutual suppression effects (see Cohen & Cohen, 1983 ; Pandey & Elliot, 2010). For example, in predicting the percentage of votes cast for Obama in 2008, Pesta and McDaniel ( 2014 ) reported bivariate correlations of 0.15 (state IQ) and 0.09 (percent Minority). Yet, in a multiple regression model, these correlations increased to 0.33 and 0.30, respectively. This finding highlights how mutual suppression effects can enhance predictive accuracy when variables that initially appear weakly correlated are analyzed together. This pattern of results was even more pronounced when examining the 2016 presidential election. Pesta ( 2017 ) reported a bivariate correlation of only − 0.06 between state IQ and the percentage of votes for Trump, while the percentage of White residents in each state correlated moderately with Trump votes (r = 0.42). However, when both predictors were included in the same regression model, the standardized betas increased significantly to -0.65 (state IQ) and 0.87 (percent White). Together, these variables explained 41% of the variance in state-level votes for Trump in 2016. The unique role of suppression effects in these analyses is particularly noteworthy. Regression models typically involve redundancy situations where predictor variables share variance with each other and the criterion variable, thereby reducing individual predictive power. In contrast, mutual suppression effects, as identified by Pesta and McDaniel ( 2014 ) and Pesta ( 2017 ), enhance predictive power by increasing beta weights when both predictors are included in the same model. Such phenomena are of special interest because they often involve predictors that show little or no correlation with the criterion in bivariate analyses (Ludlow & Klein, 2014 ). Viewed through this framework, state IQ and racial composition act as opposing "forces" influencing presidential election results (Cohen & Cohen, 1983 ). Minority Americans, who are more likely to vote Democratic (Fraga, 2018 ; Highton & Wolfinger, 2001 ), tend to score lower on IQ tests—a finding that persists independently of causal interpretations (Roth, Bevier, Bobko, Switzer, & Tyler, 2001). These disparities are also reflected at the state level, where strong correlations exist between estimated state IQ and the percentage of minority residents (Pesta, McDaniel, & Bertsch, 2010). While these findings provide a compelling framework for understanding voting patterns, they must be interpreted with caution due to the influence of socioeconomic and structural factors beyond the scope of IQ measurements. Given these complexities, I propose the following hypotheses regarding IQ and percent Minority as predictors of voting outcomes across the seven presidential elections held this century, with a specific focus on the 2024 election: H1 : State-level IQ and percent Minority will show relatively weak bivariate correlations with election outcomes. H2 : When included together in regression models, IQ and percent Minority will exhibit mutual suppression effects, significantly improving each’s predictive power. H3 : Suppression effects will reveal relatively strong inverse correlations between both IQ and percent Minority as predictors of percent Republican votes. 1.2. State-level well-being and voting patterns Moving from intelligence to “well-being” offers another layer of insight into voting behavior. Pesta and McDaniel ( 2014 ) identified moderate to strong mutual suppression effects when measures of well-being were combined with percent Minority in regression analyses. These authors relied on data from Pesta, McDaniel, and Bertsch (2010), who demonstrated that measures of crime, education, health, and income are highly intercorrelated at the state level. These relationships were so robust that a general factor of state "well-being" could be derived, with Pesta ( 2022 ) providing the most recent estimates. The well-being index predicted numerous political, social, and economic outcomes, including state-level U.S. presidential election results. Notably, Pesta and McDaniel ( 2014 ) showed that predictive relationships between the well-being variables and presidential election results were significantly larger when percent Minority was included as a covariate in the regression equations (Pesta & McDaniel, 2014 ). One particularly striking example comes from the 2000 U.S. presidential election. Here, state-level crime rates and percent Minority were used as predictors of votes cast for Al Gore. The bivariate correlations between these predictors and election outcomes were .01 (crime) and .28 (percent Minority). However, when both variables were included in the same regression model, the correlations increased to − .46 and .63, respectively. The same strong pattern emerged when predicting the 2016 presidential election. Pesta ( 2017 ) reported that the percentage of state residents voting for Trump correlated at .10 with crime rates and .42 with percent White in bivariate analyses. When both predictors were included in the same regression equation, these values increased to .59 and .79, respectively. The well-being indicators—crime, education, health, and income—may reflect underlying socioeconomic and structural factors that shape voting behavior. However, their predictive relationships with election outcomes become more meaningful when racial composition is included in the models. This interaction between demographic and structural variables underscores the complexity of electoral dynamics and warrants further exploration. Based on these findings, I propose the following hypotheses for all presidential elections this century, with a specific focus on the 2024 election: H4 : Bivariate correlations between voting outcomes and the well-being indicators (e.g., crime, education, health, income, and the global well-being index) will be relatively weak. H5 : When percent Minority is included in the regression models, mutual suppression effects will significantly enhance predictive betas for both variables. H6 : Suppression effects will reveal relatively strong inverse relationships between both the well-being indicators and percent Minority as predictors of percent Republican votes. 1.3. Big Five personality traits and voting patterns Big Five personality traits offer an additional psychological lens for understanding voting behavior. Research has shown that individual differences in personality aggregate into regional trends, which then influence political outcomes. Below, I provide a brief literature review of these relationships. Openness to Experience—a trait associated with creativity, intellectual curiosity, and a preference for novelty—emerges as a robust predictor of liberal voting behavior. Rentfrow et al. ( 2009 ) found that states with higher levels of Openness, such as California, exhibited stronger support for Democratic candidates in the 2000 and 2004 elections. Consistent with this, Caprara et al. ( 2006 ) linked Openness to liberal social values, including support for multiculturalism and social equality, central tenets of Democratic platforms. Conversely, Conscientiousness, which reflects a preference for order, tradition, and discipline, aligns with conservative ideologies. Gerber et al. ( 2011 ) demonstrated that conscientious individuals favor policies emphasizing personal responsibility and moral values, core components of Republican platforms. Next, Extroversion, while less consistent as a predictor of political preference, appears to relate more to candidate appeal than ideological alignment. Vecchione and Caprara (2009) observed that extraverts are drawn to candidates who project confidence and leadership, regardless of party affiliation. Agreeableness—describing individuals who prioritize compassion and social harmony—has been linked to liberal voting patterns. Rentfrow et al. ( 2009 ) found higher Agreeableness scores in regions favoring Democratic candidates, and Mondak et al. ( 2010 ) connected Agreeableness to support for social programs and egalitarianism. Lastly, Neuroticism, which captures emotional instability and anxiety, may influence voting behavior in times of uncertainty. Hatemi and McDermott (2012) reported that Neuroticism interacts with situational factors, such as economic instability, prompting support for candidates who promise security and stability. Rentfrow, Gosling, and Potter’s (2008) reliable state-level estimates of the Big Five traits provide a foundation for examining these relationships. Moreover, Rentfrow et al.’s ( 2008 ) estimates accounted for unique variance in voting patterns, even after adjusting for sociodemographic and political predictors. Based on the literature reviewed above, I propose the following hypotheses: H7 : Openness will predict votes cast for Democratic candidates. H8 : Conscientiousness will predict votes cast for Republican candidates. H9 : Agreeableness will predict votes cast for Democratic candidates. H10 : These traits will predict election outcomes even after controlling for sociodemographic variables. I make no specific predictions regarding Extraversion or Neuroticism due to the inconsistent findings in the literature, nor do I make predictions regarding potential suppression effects between personality traits and state racial composition, although I do present analyses addressing these issues in the results section below. 1.4. COVID-19 vaccination rates and voting patterns Preliminary analyses testing Hypotheses 1 to 10 revealed that state-level COVID-19 vaccination rates (as reported by Pesta, 2022 ) were strikingly robust predictors of U.S. presidential election outcomes throughout the 21st century. For instance, in the 2024 presidential election, vaccination rates exhibited a strong negative correlation (r = -0.87) with votes cast for Trump. Remarkably, vaccination rates vastly outperformed all other variables in predictive strength across the various regression models featured here. This finding raises a critical question: Why do vaccination rates align so closely with voting patterns? COVID-19 vaccination rates offer a powerful lens for examining partisan divides and election outcomes, reflecting underlying psychological, cultural, and ideological dynamics. Viewed through the interconnected frameworks of health policy, personality traits, and political identity, vaccination rates encapsulate many of the same factors that drive partisan preferences in U.S. presidential elections (Callaghan et al., 2021; Fridman et al., 2021 ). Below, I detail key pathways through which vaccination behavior and voting patterns might intersect. 1.4.1. State-level COVID-19 vaccination rates as a proxy for partisan behavior COVID-19 vaccination rates became a deeply polarizing issue in the United States, mirroring broader partisan attitudes toward government policies. Democratic-leaning states consistently reported higher vaccination rates, reflecting trust in government institutions, science, and public health mandates (Funk & Tyson, 2021 ). These states tend to be more urbanized, educated, and diverse—factors strongly associated with Democratic voting patterns (Pew Research Center, 2021b ). Conversely, Republican-leaning states, which reported lower vaccination rates, often prioritize values such as personal freedom and skepticism of federal mandates, core tenets of conservative ideology (Murphy et al., 2021). In this context, vaccination behavior emerged as a symbolic marker of political identity. Aligning with Democratic health policies, higher vaccination rates signal trust in centralized authority, while vaccine hesitancy or refusal aligns with Republican preferences for limited government intervention and individual autonomy (Callaghan et al., 2021). 1.4.2. The intersection of health policy and partisan identity The alignment between health behaviors and political identity further underscores the predictive power of vaccination rates. Resistance to vaccination in Republican-leaning states is consistent with long-standing cultural preferences for individualism and skepticism of authority—hallmarks of conservative ideology (Hornsey et al., 2018 ). This trend reflects the broader phenomenon of partisan sorting, in which health behaviors, including vaccination, become symbolic of political allegiance. Key factors in this alignment include trust in authority, as vaccinated individuals are more likely to trust scientific, medical, and governmental institutions, aligning with Democratic values (Funk & Tyson, 2021 ). Conversely, risk perception research suggests that unvaccinated individuals often downplayed the risks of COVID-19 while emphasizing potential vaccine side effects, reflecting Republican skepticism of public health mandates (Murphy et al., 2021). These ideological divides, as expressed through vaccination behavior, directly mirror partisan voting patterns, solidifying vaccination rates as a strong proxy for political identity (Callaghan et al., 2021). 1.4.3. The role of personality traits Personality differences also provide a psychological foundation for the connection between vaccination behavior and political preferences. Vaccinated individuals often score higher on traits such as agreeableness, and openness to experience—traits associated with collectivism and progressive ideologies (Sutin et al., 2021 ). These personality traits align with Democratic values, including support for community health and interdependence. By contrast, unvaccinated individuals are more likely to exhibit higher levels of reactance—a psychological resistance to perceived threats to autonomy—and lower levels of agreeableness. These traits are consistent with Republican values emphasizing personal freedom and skepticism of external control (Hornsey et al., 2018 ). These personality-driven behaviors offer an additional layer of explanation for the strong correlation between vaccination rates and partisan divides. In sum, COVID-19 vaccination rates are more than just a public health statistic; they serve as a multifaceted indicator of partisan identity, psychological traits, and ideological alignment. By reflecting deeper cultural and behavioral patterns, vaccination rates emerge as a uniquely powerful predictor of voting behavior in U.S. presidential elections. The analyses presented below delve further into these relationships. 2. Methods 2.1. Population The population consisted of the 50 U.S. states. Inferential tests are presented as aids to interpreting the results rather than for generalization. The primary dependent variable was the percentage of state-level votes cast for Republican candidates (“percent Red”), averaged across the seven U.S. presidential elections since 2000. Additionally, given the recency of the 2024 election, I separately analyzed the percentage of votes cast for Trump (“percent Trump”). 2.2. Measures 2.2.1. Election results Election outcomes were coded from the United States Federal Election Commission ( 2024 ). Values include the percentage of state residents voting for Trump in 2024 and the average percentage of Red votes across all seven presidential elections this century. Note that I don’t report analyses regarding percent Blue, as these correlated − .991 with percent Red. 2.2.2. State racial composition Percent Minority data were retrieved from the U.S. Census (2000, 2010, 2020). Values included the percentage of Black plus Hispanic residents within states, averaged across the three census cycles (minimum correlation = .98). 2.2.3. State IQ estimates State IQ estimates were sourced from Pesta’s ( 2022 ) most recent estimates. Across states, the mean IQ was 99.59 ( SD = 2.00). 2.2.4. State well-being estimates Well-being data, also sourced from Pesta ( 2022 ), included four sub-domains: Crime, Education, Health, and Income. These variables were standardized as Z scores and derived via Principal Component Analyses (PCA) of relevant state-level indicators. For instance, Income combined employment rates, poverty rates, household income, and median home values. Additionally, a global well-being measure was created via higher-order PCA of the four sub-domains (see Pesta, 2022 , for details), which I also include in analyses reported below. 2.2.5. State personality estimates Big 5 data were sourced from Rentfrow et al. ( 2008 ), who based their estimates on large-scale Internet surveys conducted between 1999 and 2005. Values for each trait are reported as Z scores. My reported analyses here focused on Openness, Extroversion, and Conscientiousness, as neither Neuroticism nor Agreeableness displayed significant associations with voting preferences in preliminary tests, nor were these two traits included in my hypotheses. 2.2.6. State Covid-19 vaccination rates COVID-19 vaccination rates were coded from Pesta ( 2022 ) and represent the percentage of state residents fully vaccinated against the virus as of October 1, 2021. 2.3. Analyses Analyses began with bivariate correlations between all study variables. To test for suppression effects, two-variable regression models were run, predicting either percent Trump or percent Red. Independent variables included percent Minority alongside either IQ, a well-being sub-domain, or the global well-being measure. Next, a series of multiple regressions were conducted to identify best-fitting models for predicting percent Trump and percent Red. Finally, I report results from Principal Component Analysis (PCA) for all variables as an aid to interpreting the complex relationships reported below. 3. Results 3.1. Hypothesis tests regarding state IQ estimates H1 : State-level IQ and percent Minority will show relatively weak bivariate correlations with election outcomes. H2 : When included together in regression models, IQ and percent Minority will exhibit mutual suppression effects, significantly improving each’s predictive power. H3 : Suppression effects will reveal relatively strong inverse correlations between both IQ and percent Minority as predictors of percent Republican votes. Table 1 presents a correlation matrix of all variables used in this study. Regarding H1 , the table shows non-significant correlations between percent Minority and either percent Trump (-.19) or percent Red (-.17). The IQ correlations were likewise non-significant (-.25 and − .19, respectively). Whether these correlations are relatively weak depends on the analyses presented in Table 2 where both percent Minority and IQ appear in the same regression equations. Table 1 Correlation matrix for the key study variables Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 1. Trump 2024 (%) -- .97 − .19 − .25 .34 − .70 − .50 − .60 − .62 − .53 .24 .13 − .87 2. Red Votes (%) -- − .17 − .19 .37 − .67 − .40 − .54 − .58 − .50 .30 .13 − .88 3. Minority (%) -- − .67 .49 − .25 − .07 − .22 − .28 .33 .36 .03 .10 4. IQ ( Z ) -- − .74 .74 .57 .71 .80 − .02 − .26 .04 .27 5. Crime ( Z ) -- − .73 − .39 − .57 − .76 − .06 .40 .04 − .46 6. Education ( Z ) -- .64 .80 .92 .28 − .40 − .15 .72 7. Health ( Z ) -- .87 .84 .13 − .17 − .16 .43 8. Income ( Z ) -- .94 .20 − .36 − .15 .57 9. Well-being ( Z ) -- .20 − .38 − .15 .63 10. Openness ( Z ) -- .05 − .12 .46 11. Conscientiousness ( Z ) -- .67 − .27 12. Agreeableness ( Z ) -- − .18 13. Covid Vaccine (%) -- Table 2 The mutual suppression of state IQ and percent minority predicting Republican votes Variable B SE B Β R 2 Trump % IQ (-.25) -3.47 .841 − .685 -- % Minority (-.19) − .527 .134 − .652 -- .29 Red % IQ (-.19) -2.58 .819 − .557 -- % Minority (-.17) − .399 .131 − .541 -- .20 Note . Values in parentheses represent the standardized Beta weight for the variable when it alone is entered to predict percent Trump or percent Red. These values mirror the correlations reported in Table 1 . In Table 2 , relationships between percent Trump and percent Minority increase from the simple correlation of − .19 to a Beta weight of − .652. The IQ estimates also increase from the simple correlation of − .25 to a Beta weight of − .685. Likewise in Table 2 , the Beta weight between percent Red and percent Minority increases from the simple correlation of − .17 to a Beta weight of − .541. Moreover, the IQ estimates also increase from the simple correlation of − .19 to a Beta weight of − .557. Hence, the results provide strong support for H1 and H2 , together with H3 (i.e., regarding the direction of the effects revealed in Table 2 ). 3.2. Hypotheses tests regarding state well-being estimates H4 : Bivariate correlations between voting outcomes and the well-being indicators (e.g., crime, education, health, income, and the global well-being index) will be relatively weak. H5 : When percent Minority is included in the regression models, mutual suppression effects will significantly enhance predictive betas for both variables. H6 : Suppression effects will reveal relatively strong inverse relationships between both the well-being indicators and percent Minority as predictors of percent Republican votes. Turning back to the correlation matrix in Table 1 , moderate to very strong correlations exist between percent Trump and the well-being variables. Values ranged from .34 (Crime) to − .70 (Education). The identical pattern occurred with Red Votes, as the lowest well-being correlation occurred with Crime (.37) and the highest occurred with Education (-.67). Again, however, whether these correlations are relatively weak ( H4 ) depends on the analyses presented in Tables 3 and 4 , where both percent Minority and each of the well-being variables (separately) appear in the same equation. Table 3 The mutual suppression of percent minority and well-being variables predicting percent Trump Well-being Variable B SE B Β R 2 Crime % Minority (-.19) − .378 .115 − .469 -- Crime (.34) 5.74 1.44 .568 -- 28% Education % Minority (-.19) − .310 .075 − .384 -- Education (-.70) − .532 .062 − .789 -- 62% Health % Minority (-.19) − .181 .099 − .224 -- Health (-.50) − .343 .083 − .510 -- 30% Income % Minority (-.19) − .273 .088 − .338 -- Income (-.60) − .457 .073 − .678 -- 47% Well-being % Minority (-.19) − .323 .084 − .400 -- Well-being (-.62) -7.45 1.05 − .737 -- 54% Note . Values in parentheses represent the standardized beta weight for the variable when it alone is entered to predict percent Trump. These values mirror the correlations reported in Table 1 . Table 4 The mutual suppression of race and well-being variables predicting percent Red Well-being Variable B SE B Β R 2 Crime % Minority (-.17) − .337 .104 − .457 -- Crime (.37) 5.50 1.30 .595 -- 30% Education % Minority (-.17) − .259 .073 − .351 -- Education (-.67) − .466 .061 − .756 -- 57% Health % Minority (-.17) − .142 .097 − .192 -- Health (-.40) − .252 .081 − .409 -- 19% Income % Minority (-.17) − .218 .087 − .296 -- Income (-.54) − .370 .073 − .601 -- 37% Well-being % Minority (-.17) − .264 .083 − .358 -- Well-being (-.58) -6.26 1.04 − .678 -- 45% Note . Values in parentheses represent the standardized beta weight for the variable when it alone is entered to predict percent Red Votes. These values mirror the correlations reported in Table 1 . The first regression in Table 3 features Crime and percent Minority as predictors of votes cast for percent Trump. Consistent with prior research, strong mutual suppression effects exist. Values between percent Trump and percent Minority increase from a simple correlation of − .19 to a beta weight of − .469. Likewise, the values for Crime increase from a simple correlation of .34 to a Beta weight of .568. Suppression effects also existed regarding Education and percent Minority as predictors of percent Trump. Here, the values between percent Trump and percent Minority increase from a simple correlation of − .19 to a beta weight of − .384. Likewise, the values for Education increase from a simple correlation of − .70 to a Beta weight of − .789. Conversely, although suppression effects technically exist regarding Health and percent Trump, the effects were nominal: r = − .19, Beta = − .224 (percent Minority); and r = -50; Beta = − .510 (Health). Next, Income and percent Minority also produced mutual suppression effects as the before / after correlations (Beta weights) for these variables when predicting percent Trump were: r = − .19, Beta = − .338 (percent Minority); and − .60 and − .678 (Income). Finally, similar-sized suppression effects also occurred when percent Minority and the Global Well-being variable appeared in the same equation: r = − .19; Beta = − .40 (percent Minority), and r = − .62; Beta = − .737 (Well-being). What’s striking about the values displayed in Table 3 is that ten different before (bivariate correlation) and after (Beta weight after controlling for another variable) comparisons are presented. Yet in all these, the bivariate correlation is always (at least nominally, but sometimes substantially) weaker than the Beta values resulting from the five pairwise analyses between percent Minority and one of the well-being variables. Table 4 can be seen as a replication of Table 3 , in that the latter features percent Red, which captures all seven presidential elections occurring in this century (note, the conclusions reported here did not change when re-coding percent Red to exclude the 2024 election, results of which were just presented above). In all ten of the before (bivariate correlation) and after (Beta weight after controlling for another variable) comparisons, effects were at least nominally and sometimes substantially larger in the “after” analyses. The mutual suppression effects here ranged in magnitude from the analysis focusing on Health (i.e., percent Minority increases from r = − .17 to Beta = − .192; Health increases from r = − .40 to Beta = − .409), to the analysis focusing on Crime (i.e., percent Minority increases from r = − .17 to Beta = − .457; Crime increases from r = .37 to Beta = .595). In sum, H4 and H5 seem strongly supported by the evidence. In every comparison, bivariate correlations were (at least nominally) smaller than the Beta weights resulting by including a second predictor in the regression equation. Moreover, every result featured in Tables 3 and 4 shows inverse relationships between both percent Minority and Well-being as predictors of votes cast for Republicans, thus supporting H6 . 3.3. Hypotheses tests regarding state personality estimates H7 : Openness will predict votes cast for Democratic candidates. H8 : Conscientiousness will predict votes cast for Republican candidates. H9 : Agreeableness will predict votes cast for Democratic candidates. H10 : These traits will predict election outcomes even after controlling for sociodemographic variables. Although I made no predictions on the issue, I first tested for suppression effects between either O, C, or A (entered separately) and percent Minority as predictors of election outcomes. The analyses revealed null effects for both O and A (results not reported here), but an unexpected and reasonably large suppression effect with C. Specifically, the before and after comparisons for Conscientiousness and percent Minority predicting percent Trump were: r = − .19; Beta = − .318 (percent Minority), and r = .24; Beta = .353 (Conscientiousness). Likewise when predicting percent Red, these values were: r = − .17; Beta = − .315 (percent Minority), and r = .30; Beta = .414 (Conscientiousness). Turning next to the hypotheses, Table 1 shows that Openness was indeed negatively (and strongly) correlated both with both percent Trump (-.53) and percent Red (-.50), thus supporting H7 . However, correlations between Conscientiousness and election outcomes were marginal (.24 with percent Trump, and .30 with percent Red), although the direction of the effects were as predicted, showing partial support for H8 [1] . Next, H9 was not supported, as only weak correlations existed between Agreeableness and election outcomes ( r = .13 for both percent Trump and percent Red); moreover, the direction of the effects was opposite from that predicted (i.e., the predicted signs should be negative here; but see the hierarchical regressions presented below). Tables 5 (predicting percent Trump) and 6 (predicting percent Red) display results relevant to H10 . In each table, Step 1 includes percent Minority, IQ, and the four well-being variables as “controls,” whereas Step 2 also includes O, C, and A. As an aside, several things are noteworthy about the Step 1 values in these tables. First, these variables alone explained almost 70% of the variance when predicting votes cast for Republicans. Second, Step 1 produced very large beta-weights for both Education (-.931 and − 1.01) and IQ (.580 and .825) in Tables 5 and 6 , respectively. Table 5 Hierarchical regressions predicting percent Trump Variable B SE B Β R 2 Step 1: % Minority − .059 .121 − .073 IQ 2.93 1.13 .580 Crime − .638 1.46 − .063 Education − .628 .128 − .931 Health .037 .124 .055 Income − .247 .154 − .366 .691 Step 2: % Minority .070 .138 .087 IQ 3.46 1.26 .683 Crime − .579 1.41 − .057 Education − .588 .133 − .872 Health − .054 .128 − .080 Income − .193 .160 − .286 Openness -2.92 1.10 − .258 Conscientiousness .501 1.60 .050 Agreeableness -1.58 1.34 − .150 .744 Step 3: % Minority − .066 .113 − .081 IQ .355 1.19 .070 Crime -1.861 1.15 − .184 Education − .174 .135 − .257 Health − .075 .102 − .112 Income − .038 .131 − .056 Openness -1.88 .908 − .166 Conscientiousness 1.74 1.30 .173 Agreeableness -1.82 1.07 − .173 % Vaccinated − .707 .144 − .609 .842 Table 6 Hierarchical regressions predicting percent Red Variable B SE B Β R 2 Step 1: % Minority .029 .111 .039 IQ 3.82 1.04 .825 Crime .602 1.35 .065 Education − .619 .118 -1.01 Health .079 .115 .128 Income − .233 .142 − .379 .685 Step 2: % Minority .125 .127 .169 IQ 4.19 1.17 .907 Crime .517 1.30 .056 Education − .579 .123 − .939 Health − .024 .119 − .039 Income − .160 .148 − .259 Openness -2.56 1.03 − .248 Conscientiousness 1.19 1.48 .130 Agreeableness -2.09 1.24 − .218 .738 Step 3: % Minority − .001 .105 − .002 IQ 1.32 1.10 .285 Crime − .672 1.06 − .073 Education − .195 .125 − .317 Health − .044 .095 − .071 Income − .016 .121 − .026 Openness -1.60 .839 − .155 Conscientiousness 2.34 1.20 .255 Agreeableness -2.31 .990 − .241 % Vaccinated − .655 .133 − .617 .838 Third, and rather curiously, the IQ effects in each Table reflected massive suppression effects for this variable (smaller suppression effects also existed with Education across these two tables). Specifically, bivariate correlations between IQ and percent Trump (-.25) and percent Red (-.19) were small and negative in sign (see Table 1 ). Yet at Step 1 in Tables 5 and 6 , the IQ Betas increased dramatically but they also flipped in direction from negative to positive (.580 and .825, respectively). Pesta ( 2017 ) reported similar results when both IQ and the well-being variables were entered as predictors of election results. Follow-up analyses by Pesta ( 2017 ) showed that these effects were likely due to large collinearity between IQ and well-being. In fact, Pesta et al.’s (2010) original global well-being index included the IQ estimates as one of its sub-domains to avoid the issue of collinearity. Likewise, Pesta and McDaniel opted for two-variable regressions when testing for suppression effects for this same reason. In the present analyses, for example, Variance Inflation Factors for IQ ranged from 6.92–13.54 in Tables 5 and 6 . My goal with these tables, however, was to test whether O, C, or A uniquely predicted election results when important controls were also included in the models. To address the collinearity issue, however, I also conducted additional regressions (not reported here), either by including IQ but not the well-being variables or vice versa. These regressions justified two important conclusions. First, the IQ Betas remained negative when predicting election results, once the well-being variables were removed. Second, neither of these additional regressions altered the conclusions I report next regarding O, C, and A. Turning back to H10 , Step 2 in Tables 5 – 6 show that Openness clearly predicted votes for Republicans even when various controls were also included in the equations. The effects, however, were attenuated relative to the bivariate correlations. Specifically in Table 1 , Openness correlated − .53 and − .50 with percent Trump and percent Red, respectively; whereas the Step 2 Betas (from Tables 5 – 6 ) were − .258 and − .248, respectively. Next, Conscientiousness failed to emerge as an important predictor of either percent Trump (Beta = .050) or percent Red (Beta = .130) in these analyses. Results regarding Agreeableness are interesting in that, relative to the bivariate correlations (.13 for both percent Trump and percent Red), the signs flipped when control variables were also included in the models (Betas = − .150 and − .218, respectfully). Though the resulting Betas are relatively weak here, they are consistent with H9 (i.e., that Agreeableness will predict votes cast for Democratic candidates), but only marginally consistent with H10 (as these Betas were greater than zero, but not significant). In sum, the analyses reported here reveal mixed support regarding tests of hypotheses related to personality traits as predictors of election outcomes. 3.4. Covid-19 vaccination rates and the best-fitting model Tables 5 and 6 (at Step 3) also show the robustness of Covid-19 vaccination rates as predictors of votes cast for Republicans. Regarding percent Trump (Table 5 ), vaccination rates displayed the biggest Beta weight by far (-.609, with a next highest Beta of -257 for Education). However, the only other variable emerging as a significant predictor of percent Trump was Openness (Beta = − .166). Likewise, when predicting percent Red (Table 6 ), vaccination rates again emerged as the best predictor, with a Beta weight of − .617 (wherein again Education produced the next highest Beta at − .317). Here, however, all three personality traits hovered around p = .05 when predicting percent Red: Openness (Beta = − .155, p = .064), Conscientiousness (Beta = .255, p = .058), and Agreeableness (Beta = − .241, p = .025). Note also that that at least in these analyses, the directions (i.e., positive or negative) of the effects for O, C, and A are consistent with H7 , H8 , and H9 , respectively. Although theoretical explanations for all effects reported above are of paramount importance scientifically, significant practical interest also exists in terms of establishing mere predictive power (e.g., over 80% of the variance is predicted at Step 3 in both Tables 5 and 6 ). Table 7 therefore provides a “dustbowl empiricism” test of all measures used in this study. Specifically, and disregarding theory, Table 7 presents a stepwise regression aimed at finding the best-fitting models for predicting either percent Trump or percent Red. Table 7 Best fitting predictive models via stepwise regression Variable B SE B B R 2 Percent Trump: % Vaccinated − .836 .095 − .720 Openness -2.02 .840 − .179 Health − .108 .049 − .161 .801 Percent Red: % Vaccinated − .932 .073 − .878 .772 From Table 7 , Covid-19 vaccination rates very strongly predicted percent Trump (Beta = − .720). Significant but much weaker effects also appeared for both Openness (Beta = − .179) and the Health sub-domain of well-being (Beta = − .161). Table 7 thus shows that these three variables explain fully 80% of the variance when predicting percent Trump. Interestingly, however, Table 7 reveals that only vaccination rates uniquely predicted percent Red (Beta = − .878, which is the value of the bivariate correlation presented in Table 1 ). The next strongest Beta weight was only − .229, p = .788 for Openness. In sum, vaccination rates alone explained 77% of the variance when predicting percent Red. Moreover, the prediction here was “robust” given that the effect emerged even after numerous controls were included in the regression equation. 3.5. Principal Component Analyses Given the complex pattern of results reported above, I sought to simplify explanations by conducting two separate PCA’s, one including percent Trump and all other variables except percent Red, and vice versa for the other analysis. Results appear in Table 8 . Table 8 Principal Components Analysis of all variables with either percent Trump or percent Red Variable Component 1 Component 2 Component 3 Percent Trump Trump % − .731 -- -- Minority % -- .834 -- IQ .759 -- -- Crime − .756 -- -- Education .942 -- -- Health .747 -- -- Income .900 -- -- Openness -- .687 -- Conscientiousness -- -- .762 Agreeableness -- -- .894 Vaccinated % .749 -- -- (Variance Explained %) (45.4) (19.8 / 65.2) (13.8 / 79.0) Percent Red Red % − .702 -- -- Minority % -- .821 -- IQ .757 -- -- Crime − .767 -- -- Education .942 -- -- Health .733 -- -- Income .892 -- -- Openness -- .688 -- Conscientiousness -- -- .770 Agreeableness -- -- .881 Vaccinated % .750 -- -- (Variance Explained %) (45.0) (19.6 / 64.6) (13.8 / 78.4) Note . Loadings less than |.60| are not reported here. Regarding percent Trump, three significant components emerged, and each seem readily interpretable. Component 1 clearly features relationships between IQ and the well-being variables (together with vaccination rates) as covariates of votes cast for Trump. Moreover, the directions for all these loadings are consistent with the hypotheses tested above. Component 2 seems to capture a strong positive relationship between percent Minority and Openness, while Component 3 reflects the strong positive relationship between Conscientiousness and Agreeableness ( r = .67 from Table 1 , with Rentfrow et al., 2008 , providing the original data). In terms of variable loadings, the PCA featuring percent Red exactly mirrored the PCA featuring percent Trump. In sum, findings from the PCA analyses here are consistent with the main results reported above: (1) once untangled, percent Minority, IQ, well-being, and vaccination rates all covary strongly with Republican votes; whereas (2) no overly strong relationships appear with the personality variables and election outcomes. 4. Discussion This study provides novel insights into the predictors of U.S. presidential voting patterns, emphasizing the unexpected strength of COVID-19 vaccination rates as a correlate of Republican voting outcomes. Below, I contextualize these findings, discuss their implications, and identify avenues for future research. 4.1. Key findings and interpretations The most striking result of this study is the robust negative correlation between state-level COVID-19 vaccination rates and votes cast for Republican candidates. This relationship persisted across multiple elections and statistical models, highlighting the extent to which health behaviors have become a marker of partisan identity in the U.S. Democratic-leaning states, characterized by higher vaccination rates, likely reflect greater trust in science, government, and public health policies. Conversely, Republican-leaning states, with lower vaccination rates, embody cultural values prioritizing individualism and skepticism of authority, consistent with conservative ideology (e.g., Callaghan et al., 2021; Murphy et al., 2021). Mutual suppression effects further reveal the complexity of voting behavior. Variables such as state IQ and well-being indices gained predictive power when analyzed alongside racial composition. These results underscore the importance of multivariate approaches to understanding the interplay of demographic, psychological, and sociocultural factors. For example, while state-level IQ and minority population percentages exhibited weak bivariate correlations with voting outcomes, their combined analysis illuminated strong and significant relationships. This finding aligns with prior research on suppression effects in political science and psychology (Pesta & McDaniel, 2014 ). In addition, the Big Five personality trait of Openness to Experience emerged as a consistent predictor of Democratic voting. This finding supports existing literature linking Openness to liberal social and political values, such as a preference for diversity and progressive change (Rentfrow et al., 2008 ). However, other traits, such as Conscientiousness and Agreeableness, showed weaker and less consistent associations, warranting further investigation. 4.2. Implications for electoral modeling The results of this study highlight the need to incorporate non-traditional variables, such as vaccination rates, into electoral prediction models. These findings suggest that health behaviors may serve as powerful proxies for underlying ideological and cultural divides, offering new avenues for understanding voter preferences. Traditional predictors, including income and education, remain important but may need to be contextualized within emerging societal trends. The significant role of suppression effects also underscores the limitations of simple bivariate analyses in explaining complex phenomena. Future research and electoral models should prioritize multivariate approaches to better capture the nuanced relationships between variables like racial composition, intelligence, and well-being. 4.3. Limitations and future directions While the study provides robust statistical evidence, several limitations must be acknowledged. The use of aggregate state-level data risks ecological fallacy, where relationships observed at the group level may not apply to individuals. Future research should validate these findings with individual-level data to strengthen their generalizability. Additionally, the theoretical basis for the link between vaccination rates and voting patterns requires further exploration. While the study identifies a strong empirical relationship, understanding the psychological and sociocultural mechanisms driving this connection is crucial. Expanding the scope to include related factors, such as media consumption and misinformation, could provide deeper insights. Finally, while this study focuses on the U.S. context, its broader applicability remains uncertain. Cross-national studies examining similar variables in other democracies could illuminate whether these patterns are unique to the U.S. or reflect broader global trends. 4.4. Conclusion This study contributes to the growing body of literature on the psychological and sociocultural determinants of voting behavior, offering a novel perspective on the role of health behaviors in shaping political identity. The findings highlight the importance of adopting multidimensional and dynamic models in electoral studies. By integrating traditional and non-traditional predictors, researchers can develop a more comprehensive understanding of voter behavior in an increasingly polarized society. Declarations Notes. This research was not funded. Clinical trial number: not applicable. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable Author Contribution All of it. Data Availability All data appear in the manuscript. References Callaghan, Timothy, Ali Moghtaderi, Jennifer A. Lueck, Peter Hotez, Ulrich Strych, Avi Dor, Erika Franklin Fowler, and Matt Motta. 2020. "Correlates and Disparities of COVID-19 Vaccine Hesitancy." SSRN . https://ssrn.com/abstract=3667971. Caprara, Gian Vittorio, Shalom Schwartz, Claudio Capanna, Michele Vecchione, and Claudio Barbaranelli. 2006. "Personality and Politics: Values, Traits, and Political Choice." Political Psychology 27(1): 1–28. https://doi.org/10.1111/j.1467-9221.2006.00447.x. Cohen, Jacob and Patricia Cohen. 1983. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences . 2nd ed. Erlbaum. Fraga, Bernard. 2018. The Turnout Gap: Race, Ethnicity, and Political Inequality in a Diversifying America*. Cambridge University Press. https://doi.org/10.1017/9781108564815. Fridman, Alex, Rachel Gershon, and Ayelet Gneezy. 2021. "COVID-19 and Vaccine Hesitancy: A Longitudinal Study." PLoS ONE 16(4): e0250123. https://doi.org/10.1371/journal.pone.0250123. Funk, C., & Tyson, A. (2021). Growing share of Americans say they are open to getting COVID-19 vaccine – but few see vaccination as a community responsibility. Pew Research Center . Retrieved from https://www.pewresearch.org Gelman, Andrew, and Julia R. Azari. 2017. "19 Things We Learned from the 2016 Election." Statistics and Public Policy 4(1): 1–10. https://doi.org/10.1080/2330443X.2017.1356775. Gerber, Alan S., Gregory A. Huber, David Doherty, and Conor M. Dowling. 2011. "The Big Five Personality Traits in the Political Arena." Annual Review of Political Science 14(1): 265–87. https://doi.org/10.1146/annurev-polisci-051010-111659. Hatemi, Peter K., and Rose McDermott. 2012. "The Genetics of Politics: Discovery, Challenges, and Progress." Trends in Genetics 28(10): 525–33. https://doi.org/10.1016/j.tig.2012.07.004. Highton, Benjamin, and Raymond E. Wolfinger. 2001. "The First Seven Years of the Political Life Cycle." American Journal of Political Science 45(1): 202–9. https://doi.org/10.2307/2669364. Hornsey, Matthew J., Emily A. Harris, and Kelly S. Fielding. 2018. "The Psychological Roots of Anti-Vaccination Attitudes: A 24-Nation Investigation." Health Psychology 37(4): 307–15. https://doi.org/10.1037/hea0000586. Issenberg, Sasha. 2012. The Victory Lab: The Secret Science of Winning Campaigns . Public Opinion Quarterly 78: 363–64. https://doi.org/10.1093/poq/nft048. Ludlow, Larry H., and Kimberly M. Klein. 2014. "Suppressor Variables: The Difference Between 'Is' Versus 'Acting As.'" Journal of Statistics Education 22(2). https://doi.org/10.1080/10691898.2014.11889703. McDaniel, Michael A. 2006. "Estimating State IQ: Measurement Challenges and Solutions." Intelligence 34(6): 607–19. https://doi.org/10.1016/j.intell.2006.06.003. Mondak, Jeffery J., Damarys Canache, Mitchell A. Seligson, and Mary V. Hibbing. 2010. "The Participatory Personality: Evidence from Latin America." British Journal of Political Science 41(1): 211–21. https://doi.org/10.1017/S000712341000027X. Pandey, Shanta, and William Elliott. 2010. "Suppressor Variables in Social Work Research: Ways to Identify in Multiple Regression Models." Journal of the Society for Social Work and Research 1(1): 28–40. https://doi.org/10.5243/jsswr.2010.2. Pesta, Bryan. 2017. "A U.S. State-Level Analysis of the Presidential Election in 2016: IQ, Race, and Well-Being Emerge as Mutually Suppressed Predictors." Open Differential Psychology . ISSN: 2446-3884. Pesta, Bryan. 2022. "Updated IQ and Well-Being Scores for the 50 U.S. States." Journal of Intelligence 10. https://doi.org/10.3390/jintelligence10010015. Pesta, Bryan J., and Michael A. McDaniel. 2014. "State IQ, Well-Being, and Racial Composition as Predictors of U.S. Presidential Election Outcomes." Intelligence 42: 107–14. https://doi.org/10.1016/j.intell.2013.11.006. Pesta, Bryan J., Michael A. McDaniel, and Sharon Bertsch. 2010. "Toward an Index of Well-Being for the Fifty U.S. States." Intelligence 38(2): 160–68. https://doi.org/10.1016/j.intell.2009.09.006. Pew Research Center. 2021a. "Public Trust in Government: 1958–2021." https://www.pewresearch.org/politics/2024/06/24/public-trust-in-government-1958-2024/. Pew Research Center. 2021b. "Political Polarization in the American Public." https://www.pewresearch.org. Rentfrow, Peter J., Samuel D. Gosling, and Jeff Potter. 2008. "A Theory of the Emergence, Persistence, and Expression of Geographic Variation in Psychological Characteristics." Perspectives on Psychological Science 3(5): 339–69. https://doi.org/10.1111/j.1745-6924.2008.00084.x. Rentfrow, Peter J., John T. Jost, Samuel D. Gosling, and Jeff Potter. 2009. "Statewide Differences in Personality Predict Voting Patterns in 2000–2004 U.S. Presidential Elections." Social and Psychological Personality Science 1(1): 97–205. https://doi.org/10.1177/1948550609352782. Roth, Philip L., Christine A. Bevier, Philip Bobko, Frederick S. Switzer, and Peggy Tyler. 2001. "Ethnic Group Differences in Cognitive Ability Tests: A Meta-Analysis." Personnel Psychology 54(2): 297–330. https://doi.org/10.1111/j.1744-6570.2001.tb00196.x. Silver, Nate. 2015. The Signal and the Noise: Why So Many Predictions Fail—but Some Don't . Penguin Books. Sutin, Angelina R., Yannick Stephan, and Antonio Terracciano. 2021. "Personality Traits and Susceptibility to COVID-19 Infection and Vaccine Hesitancy." Personality and Individual Differences 177: 110817. https://doi.org/10.1016/j.paid.2021.110817. U.S. Census Bureau. 2000, 2010, 2020. "State Racial Composition Data." https://www.census.gov/topics/population/race.html. United States Federal Election Commission. 2024. "Election Results and Voting Information." https://www.fec.gov/data/candidates/president/presidential-map/. Vecchione, Michele, and Gian Vittorio Caprara. 2009. "Personality Determinants of Political Participation: The Contribution of Traits and Self-Efficacy Beliefs." Personality and Individual Differences 46(4): 487–92. https://doi.org/10.1016/j.paid.2008.11.021. Footnotes However, one could argue that H8 was fully supported here even though the correlation between Conscientiousness and percent Trump (.24) was not “significant.” This is because statistical significance is irrelevant when dealing with populations (i.e., the 50 US states) versus samples, and because even a correlation of .24 has predictive value. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eThe prediction of US presidential election outcomes is big business, and rests at the intersection of political strategy, social science, and media economics. Platforms like \u003cem\u003eFiveThirtyEight\u003c/em\u003e generate substantial revenue through election forecasts, supported by partnerships with major networks such as ABC News (Silver, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, polling and analytics firms like Gallup and Ipsos secure multi-million-dollar contracts with campaigns and organizations to conduct polling and analysis (Pew Research Center, 2021). Political campaigns themselves invest billions in predictive models to optimize resource allocation and voter outreach, particularly in pivotal battleground states (Issenberg, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the profitability of election forecasting, the field faces significant challenges, most notably the demand for accuracy. The 2016 presidential election highlighted the risks of prediction when polling forecasts underestimated Donald Trump\u0026rsquo;s likelihood of victory, thus undermining public confidence in traditional forecasting methodologies (Gelman \u0026amp; Azari, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This failure emphasized the need for more nuanced and reliable predictive models, particularly as reliance on election forecasts by the public and institutions continues to grow.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the utility of well-researched variables in differential psychology as predictors of U.S. state-level voting patterns. Specifically, I explore the predictive power of state-level measures of intelligence (IQ), various indicators of human well-being (e.g., Income), and the Big Five personality traits. By analyzing data retroactively for all seven presidential elections of the 21st century, I assess the extent to which these psychological and sociological variables can explain voting patterns, with particular attention to the most recent election.\u003c/p\u003e \u003cp\u003eUnexpectedly, preliminary analyses revealed that state-level COVID-19 vaccination rates emerged as the strongest and most robust predictor of election outcomes\u0026mdash;not only in 2024 but across all elections this century. This surprising finding prompted further exploration into potential theoretical explanations for the effect, which are detailed in the literature review below. In sum, by investigating the interplay between differential psychology, vaccination behavior, and voting patterns, this study seeks to contribute new insights into the complex dynamics of American electoral politics.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. State-level estimates of intelligence and voting patterns\u003c/h2\u003e \u003cp\u003eUnderstanding the relationship between state-level IQ and voting behavior requires an exploration of nuanced statistical interactions. For instance, Pesta and McDaniel (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported that IQ estimates for the 50 U.S. states (derived by McDaniel, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) predicted whether a state voted Republican (Red) or Democratic (Blue), but only after accounting for the percent of state residents who identified as either Black or Hispanic (i.e., \u0026ldquo;percent Minority\u0026rdquo;). Specifically, in bivariate analyses, Pesta and McDaniel reported negligible correlations between state IQ or state percent Minority and election outcomes. However, when both variables were included in the same regression model, their predictive power increased substantially, illustrating mutual suppression effects (see Cohen \u0026amp; Cohen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Pandey \u0026amp; Elliot, 2010).\u003c/p\u003e \u003cp\u003eFor example, in predicting the percentage of votes cast for Obama in 2008, Pesta and McDaniel (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported bivariate correlations of 0.15 (state IQ) and 0.09 (percent Minority). Yet, in a multiple regression model, these correlations increased to 0.33 and 0.30, respectively. This finding highlights how mutual suppression effects can enhance predictive accuracy when variables that initially appear weakly correlated are analyzed together.\u003c/p\u003e \u003cp\u003eThis pattern of results was even more pronounced when examining the 2016 presidential election. Pesta (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported a bivariate correlation of only \u0026minus;\u0026thinsp;0.06 between state IQ and the percentage of votes for Trump, while the percentage of White residents in each state correlated moderately with Trump votes (r\u0026thinsp;=\u0026thinsp;0.42). However, when both predictors were included in the same regression model, the standardized betas increased significantly to -0.65 (state IQ) and 0.87 (percent White). Together, these variables explained 41% of the variance in state-level votes for Trump in 2016.\u003c/p\u003e \u003cp\u003eThe unique role of suppression effects in these analyses is particularly noteworthy. Regression models typically involve redundancy situations where predictor variables share variance with each other and the criterion variable, thereby reducing individual predictive power. In contrast, mutual suppression effects, as identified by Pesta and McDaniel (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Pesta (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), enhance predictive power by increasing beta weights when both predictors are included in the same model. Such phenomena are of special interest because they often involve predictors that show little or no correlation with the criterion in bivariate analyses (Ludlow \u0026amp; Klein, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eViewed through this framework, state IQ and racial composition act as opposing \"forces\" influencing presidential election results (Cohen \u0026amp; Cohen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Minority Americans, who are more likely to vote Democratic (Fraga, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Highton \u0026amp; Wolfinger, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), tend to score lower on IQ tests\u0026mdash;a finding that persists independently of causal interpretations (Roth, Bevier, Bobko, Switzer, \u0026amp; Tyler, 2001). These disparities are also reflected at the state level, where strong correlations exist between estimated state IQ and the percentage of minority residents (Pesta, McDaniel, \u0026amp; Bertsch, 2010). While these findings provide a compelling framework for understanding voting patterns, they must be interpreted with caution due to the influence of socioeconomic and structural factors beyond the scope of IQ measurements.\u003c/p\u003e \u003cp\u003eGiven these complexities, I propose the following hypotheses regarding IQ and percent Minority as predictors of voting outcomes across the seven presidential elections held this century, with a specific focus on the 2024 election:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH1\u003c/b\u003e: State-level IQ and percent Minority will show relatively weak bivariate correlations with election outcomes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH2\u003c/b\u003e: When included together in regression models, IQ and percent Minority will exhibit mutual suppression effects, significantly improving each\u0026rsquo;s predictive power.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH3\u003c/b\u003e: Suppression effects will reveal relatively strong inverse correlations between both IQ and percent Minority as predictors of percent Republican votes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. State-level well-being and voting patterns\u003c/h2\u003e \u003cp\u003eMoving from intelligence to \u0026ldquo;well-being\u0026rdquo; offers another layer of insight into voting behavior. Pesta and McDaniel (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identified moderate to strong mutual suppression effects when measures of well-being were combined with percent Minority in regression analyses. These authors relied on data from Pesta, McDaniel, and Bertsch (2010), who demonstrated that measures of crime, education, health, and income are highly intercorrelated at the state level. These relationships were so robust that a general factor of state \"well-being\" could be derived, with Pesta (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) providing the most recent estimates. The well-being index predicted numerous political, social, and economic outcomes, including state-level U.S. presidential election results. Notably, Pesta and McDaniel (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) showed that predictive relationships between the well-being variables and presidential election results were significantly larger when percent Minority was included as a covariate in the regression equations (Pesta \u0026amp; McDaniel, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne particularly striking example comes from the 2000 U.S. presidential election. Here, state-level crime rates and percent Minority were used as predictors of votes cast for Al Gore. The bivariate correlations between these predictors and election outcomes were .01 (crime) and .28 (percent Minority). However, when both variables were included in the same regression model, the correlations increased to \u0026minus;\u0026thinsp;.46 and .63, respectively. The same strong pattern emerged when predicting the 2016 presidential election. Pesta (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that the percentage of state residents voting for Trump correlated at .10 with crime rates and .42 with percent White in bivariate analyses. When both predictors were included in the same regression equation, these values increased to .59 and .79, respectively.\u003c/p\u003e \u003cp\u003eThe well-being indicators\u0026mdash;crime, education, health, and income\u0026mdash;may reflect underlying socioeconomic and structural factors that shape voting behavior. However, their predictive relationships with election outcomes become more meaningful when racial composition is included in the models. This interaction between demographic and structural variables underscores the complexity of electoral dynamics and warrants further exploration.\u003c/p\u003e \u003cp\u003eBased on these findings, I propose the following hypotheses for all presidential elections this century, with a specific focus on the 2024 election:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH4\u003c/b\u003e: Bivariate correlations between voting outcomes and the well-being indicators (e.g., crime, education, health, income, and the global well-being index) will be relatively weak.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH5\u003c/b\u003e: When percent Minority is included in the regression models, mutual suppression effects will significantly enhance predictive betas for both variables.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH6\u003c/b\u003e: Suppression effects will reveal relatively strong inverse relationships between both the well-being indicators and percent Minority as predictors of percent Republican votes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Big Five personality traits and voting patterns\u003c/h2\u003e \u003cp\u003eBig Five personality traits offer an additional psychological lens for understanding voting behavior. Research has shown that individual differences in personality aggregate into regional trends, which then influence political outcomes. Below, I provide a brief literature review of these relationships.\u003c/p\u003e \u003cp\u003eOpenness to Experience\u0026mdash;a trait associated with creativity, intellectual curiosity, and a preference for novelty\u0026mdash;emerges as a robust predictor of liberal voting behavior. Rentfrow et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that states with higher levels of Openness, such as California, exhibited stronger support for Democratic candidates in the 2000 and 2004 elections. Consistent with this, Caprara et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) linked Openness to liberal social values, including support for multiculturalism and social equality, central tenets of Democratic platforms.\u003c/p\u003e \u003cp\u003eConversely, Conscientiousness, which reflects a preference for order, tradition, and discipline, aligns with conservative ideologies. Gerber et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) demonstrated that conscientious individuals favor policies emphasizing personal responsibility and moral values, core components of Republican platforms. Next, Extroversion, while less consistent as a predictor of political preference, appears to relate more to candidate appeal than ideological alignment. Vecchione and Caprara (2009) observed that extraverts are drawn to candidates who project confidence and leadership, regardless of party affiliation.\u003c/p\u003e \u003cp\u003eAgreeableness\u0026mdash;describing individuals who prioritize compassion and social harmony\u0026mdash;has been linked to liberal voting patterns. Rentfrow et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found higher Agreeableness scores in regions favoring Democratic candidates, and Mondak et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) connected Agreeableness to support for social programs and egalitarianism. Lastly, Neuroticism, which captures emotional instability and anxiety, may influence voting behavior in times of uncertainty. Hatemi and McDermott (2012) reported that Neuroticism interacts with situational factors, such as economic instability, prompting support for candidates who promise security and stability.\u003c/p\u003e \u003cp\u003eRentfrow, Gosling, and Potter\u0026rsquo;s (2008) reliable state-level estimates of the Big Five traits provide a foundation for examining these relationships. Moreover, Rentfrow et al.\u0026rsquo;s (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) estimates accounted for unique variance in voting patterns, even after adjusting for sociodemographic and political predictors. Based on the literature reviewed above, I propose the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH7\u003c/b\u003e: Openness will predict votes cast for Democratic candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH8\u003c/b\u003e: Conscientiousness will predict votes cast for Republican candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH9\u003c/b\u003e: Agreeableness will predict votes cast for Democratic candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH10\u003c/b\u003e: These traits will predict election outcomes even after controlling for sociodemographic variables.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eI make no specific predictions regarding Extraversion or Neuroticism due to the inconsistent findings in the literature, nor do I make predictions regarding potential suppression effects between personality traits and state racial composition, although I do present analyses addressing these issues in the results section below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. COVID-19 vaccination rates and voting patterns\u003c/h2\u003e \u003cp\u003ePreliminary analyses testing Hypotheses 1 to 10 revealed that state-level COVID-19 vaccination rates (as reported by Pesta, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were strikingly robust predictors of U.S. presidential election outcomes throughout the 21st century. For instance, in the 2024 presidential election, vaccination rates exhibited a strong negative correlation (r = -0.87) with votes cast for Trump. Remarkably, vaccination rates vastly outperformed all other variables in predictive strength across the various regression models featured here. This finding raises a critical question: Why do vaccination rates align so closely with voting patterns?\u003c/p\u003e \u003cp\u003eCOVID-19 vaccination rates offer a powerful lens for examining partisan divides and election outcomes, reflecting underlying psychological, cultural, and ideological dynamics. Viewed through the interconnected frameworks of health policy, personality traits, and political identity, vaccination rates encapsulate many of the same factors that drive partisan preferences in U.S. presidential elections (Callaghan et al., 2021; Fridman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Below, I detail key pathways through which vaccination behavior and voting patterns might intersect.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e1.4.1. State-level COVID-19 vaccination rates as a proxy for partisan behavior\u003c/h2\u003e \u003cp\u003eCOVID-19 vaccination rates became a deeply polarizing issue in the United States, mirroring broader partisan attitudes toward government policies. Democratic-leaning states consistently reported higher vaccination rates, reflecting trust in government institutions, science, and public health mandates (Funk \u0026amp; Tyson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These states tend to be more urbanized, educated, and diverse\u0026mdash;factors strongly associated with Democratic voting patterns (Pew Research Center, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, Republican-leaning states, which reported lower vaccination rates, often prioritize values such as personal freedom and skepticism of federal mandates, core tenets of conservative ideology (Murphy et al., 2021). In this context, vaccination behavior emerged as a symbolic marker of political identity. Aligning with Democratic health policies, higher vaccination rates signal trust in centralized authority, while vaccine hesitancy or refusal aligns with Republican preferences for limited government intervention and individual autonomy (Callaghan et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e1.4.2. The intersection of health policy and partisan identity\u003c/h2\u003e \u003cp\u003eThe alignment between health behaviors and political identity further underscores the predictive power of vaccination rates. Resistance to vaccination in Republican-leaning states is consistent with long-standing cultural preferences for individualism and skepticism of authority\u0026mdash;hallmarks of conservative ideology (Hornsey et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This trend reflects the broader phenomenon of partisan sorting, in which health behaviors, including vaccination, become symbolic of political allegiance.\u003c/p\u003e \u003cp\u003eKey factors in this alignment include trust in authority, as vaccinated individuals are more likely to trust scientific, medical, and governmental institutions, aligning with Democratic values (Funk \u0026amp; Tyson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, risk perception research suggests that unvaccinated individuals often downplayed the risks of COVID-19 while emphasizing potential vaccine side effects, reflecting Republican skepticism of public health mandates (Murphy et al., 2021).\u003c/p\u003e \u003cp\u003eThese ideological divides, as expressed through vaccination behavior, directly mirror partisan voting patterns, solidifying vaccination rates as a strong proxy for political identity (Callaghan et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.4.3. The role of personality traits\u003c/h2\u003e \u003cp\u003ePersonality differences also provide a psychological foundation for the connection between vaccination behavior and political preferences. Vaccinated individuals often score higher on traits such as agreeableness, and openness to experience\u0026mdash;traits associated with collectivism and progressive ideologies (Sutin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These personality traits align with Democratic values, including support for community health and interdependence.\u003c/p\u003e \u003cp\u003eBy contrast, unvaccinated individuals are more likely to exhibit higher levels of reactance\u0026mdash;a psychological resistance to perceived threats to autonomy\u0026mdash;and lower levels of agreeableness. These traits are consistent with Republican values emphasizing personal freedom and skepticism of external control (Hornsey et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These personality-driven behaviors offer an additional layer of explanation for the strong correlation between vaccination rates and partisan divides.\u003c/p\u003e \u003cp\u003eIn sum, COVID-19 vaccination rates are more than just a public health statistic; they serve as a multifaceted indicator of partisan identity, psychological traits, and ideological alignment. By reflecting deeper cultural and behavioral patterns, vaccination rates emerge as a uniquely powerful predictor of voting behavior in U.S. presidential elections. The analyses presented below delve further into these relationships.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Population\u003c/h2\u003e \u003cp\u003eThe population consisted of the 50 U.S. states. Inferential tests are presented as aids to interpreting the results rather than for generalization. The primary dependent variable was the percentage of state-level votes cast for Republican candidates (\u0026ldquo;percent Red\u0026rdquo;), averaged across the seven U.S. presidential elections since 2000. Additionally, given the recency of the 2024 election, I separately analyzed the percentage of votes cast for Trump (\u0026ldquo;percent Trump\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Election results\u003c/h2\u003e \u003cp\u003eElection outcomes were coded from the United States Federal Election Commission (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Values include the percentage of state residents voting for Trump in 2024 and the average percentage of Red votes across all seven presidential elections this century. Note that I don\u0026rsquo;t report analyses regarding percent Blue, as these correlated \u0026minus;\u0026thinsp;.991 with percent Red.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. State racial composition\u003c/h2\u003e \u003cp\u003ePercent Minority data were retrieved from the U.S. Census (2000, 2010, 2020). Values included the percentage of Black plus Hispanic residents within states, averaged across the three census cycles (minimum correlation\u0026thinsp;=\u0026thinsp;.98).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. State IQ estimates\u003c/h2\u003e \u003cp\u003eState IQ estimates were sourced from Pesta\u0026rsquo;s (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) most recent estimates. Across states, the mean IQ was 99.59 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.00).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. State well-being estimates\u003c/h2\u003e \u003cp\u003eWell-being data, also sourced from Pesta (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), included four sub-domains: Crime, Education, Health, and Income. These variables were standardized as \u003cem\u003eZ\u003c/em\u003e scores and derived via Principal Component Analyses (PCA) of relevant state-level indicators. For instance, Income combined employment rates, poverty rates, household income, and median home values. Additionally, a global well-being measure was created via higher-order PCA of the four sub-domains (see Pesta, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, for details), which I also include in analyses reported below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. State personality estimates\u003c/h2\u003e \u003cp\u003eBig 5 data were sourced from Rentfrow et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who based their estimates on large-scale Internet surveys conducted between 1999 and 2005. Values for each trait are reported as \u003cem\u003eZ\u003c/em\u003e scores. My reported analyses here focused on Openness, Extroversion, and Conscientiousness, as neither Neuroticism nor Agreeableness displayed significant associations with voting preferences in preliminary tests, nor were these two traits included in my hypotheses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6. State Covid-19 vaccination rates\u003c/h2\u003e \u003cp\u003eCOVID-19 vaccination rates were coded from Pesta (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and represent the percentage of state residents fully vaccinated against the virus as of October 1, 2021.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analyses\u003c/h2\u003e \u003cp\u003eAnalyses began with bivariate correlations between all study variables. To test for suppression effects, two-variable regression models were run, predicting either percent Trump or percent Red. Independent variables included percent Minority alongside either IQ, a well-being sub-domain, or the global well-being measure. Next, a series of multiple regressions were conducted to identify best-fitting models for predicting percent Trump and percent Red. Finally, I report results from Principal Component Analysis (PCA) for all variables as an aid to interpreting the complex relationships reported below.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Hypothesis tests regarding state IQ estimates\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH1\u003c/b\u003e: State-level IQ and percent Minority will show relatively weak bivariate correlations with election outcomes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH2\u003c/b\u003e: When included together in regression models, IQ and percent Minority will exhibit mutual suppression effects, significantly improving each\u0026rsquo;s predictive power.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH3\u003c/b\u003e: Suppression effects will reveal relatively strong inverse correlations between both IQ and percent Minority as predictors of percent Republican votes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a correlation matrix of all variables used in this study. Regarding \u003cb\u003eH1\u003c/b\u003e, the table shows non-significant correlations between percent Minority and either percent Trump (-.19) or percent Red (-.17). The IQ correlations were likewise non-significant (-.25 and \u0026minus;\u0026thinsp;.19, respectively). Whether these correlations are relatively weak depends on the analyses presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e where both percent Minority and IQ appear in the same regression equations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelation matrix for the key study variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"27\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c27\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1. Trump 2024 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2. Red Votes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3. Minority (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e4. IQ (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5. Crime (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e6. Education (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e7. Health (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e8. Income (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e9. Well-being (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e10. Openness (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e11. Conscientiousness (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c27\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. Agreeableness (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. Covid Vaccine (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe mutual suppression of state IQ and percent minority predicting Republican votes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΒ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrump %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ (-.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRed %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e. Values in parentheses represent the standardized Beta weight for the variable when it alone is entered to predict percent Trump or percent Red. These values mirror the correlations reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, relationships between percent Trump and percent Minority increase from the simple correlation of \u0026minus;\u0026thinsp;.19 to a Beta weight of \u0026minus;\u0026thinsp;.652. The IQ estimates also increase from the simple correlation of \u0026minus;\u0026thinsp;.25 to a Beta weight of \u0026minus;\u0026thinsp;.685. Likewise in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Beta weight between percent Red and percent Minority increases from the simple correlation of \u0026minus;\u0026thinsp;.17 to a Beta weight of \u0026minus;\u0026thinsp;.541. Moreover, the IQ estimates also increase from the simple correlation of \u0026minus;\u0026thinsp;.19 to a Beta weight of \u0026minus;\u0026thinsp;.557. Hence, the results provide strong support for \u003cb\u003eH1\u003c/b\u003e and \u003cb\u003eH2\u003c/b\u003e, together with \u003cb\u003eH3\u003c/b\u003e (i.e., regarding the direction of the effects revealed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e3.2. Hypotheses tests regarding state well-being estimates\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH4\u003c/b\u003e: Bivariate correlations between voting outcomes and the well-being indicators (e.g., crime, education, health, income, and the global well-being index) will be relatively weak.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH5\u003c/b\u003e: When percent Minority is included in the regression models, mutual suppression effects will significantly enhance predictive betas for both variables.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH6\u003c/b\u003e: Suppression effects will reveal relatively strong inverse relationships between both the well-being indicators and percent Minority as predictors of percent Republican votes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTurning back to the correlation matrix in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, moderate to very strong correlations exist between percent Trump and the well-being variables. Values ranged from .34 (Crime) to \u0026minus;\u0026thinsp;.70 (Education). The identical pattern occurred with Red Votes, as the lowest well-being correlation occurred with Crime (.37) and the highest occurred with Education (-.67). Again, however, whether these correlations are relatively weak (\u003cb\u003eH4\u003c/b\u003e) depends on the analyses presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, where both percent Minority and each of the well-being variables (separately) appear in the same equation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe mutual suppression of percent minority and well-being variables predicting percent Trump\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΒ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime (.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (-.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth (-.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (-.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being (-.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e. Values in parentheses represent the standardized beta weight for the variable when it alone is entered to predict percent Trump. These values mirror the correlations reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe mutual suppression of race and well-being variables predicting percent Red\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΒ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime (.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (-.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth (-.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (-.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority (-.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being (-.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e. Values in parentheses represent the standardized beta weight for the variable when it alone is entered to predict percent Red Votes. These values mirror the correlations reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first regression in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e features Crime and percent Minority as predictors of votes cast for percent Trump. Consistent with prior research, strong mutual suppression effects exist. Values between percent Trump and percent Minority increase from a simple correlation of \u0026minus;\u0026thinsp;.19 to a beta weight of \u0026minus;\u0026thinsp;.469. Likewise, the values for Crime increase from a simple correlation of .34 to a Beta weight of .568.\u003c/p\u003e \u003cp\u003eSuppression effects also existed regarding Education and percent Minority as predictors of percent Trump. Here, the values between percent Trump and percent Minority increase from a simple correlation of \u0026minus;\u0026thinsp;.19 to a beta weight of \u0026minus;\u0026thinsp;.384. Likewise, the values for Education increase from a simple correlation of \u0026minus;\u0026thinsp;.70 to a Beta weight of \u0026minus;\u0026thinsp;.789. Conversely, although suppression effects technically exist regarding Health and percent Trump, the effects were nominal: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.19, Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.224 (percent Minority); and \u003cem\u003er\u003c/em\u003e = -50; Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.510 (Health).\u003c/p\u003e \u003cp\u003eNext, Income and percent Minority also produced mutual suppression effects as the before / after correlations (Beta weights) for these variables when predicting percent Trump were: \u003cem\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;.19, Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.338 (percent Minority); and \u0026minus;\u0026thinsp;.60 and \u0026minus;\u0026thinsp;.678 (Income). Finally, similar-sized suppression effects also occurred when percent Minority and the Global Well-being variable appeared in the same equation: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.19; Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.40 (percent Minority), and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.62; Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.737 (Well-being).\u003c/p\u003e \u003cp\u003eWhat\u0026rsquo;s striking about the values displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is that ten different before (bivariate correlation) and after (Beta weight after controlling for another variable) comparisons are presented. Yet in all these, the bivariate correlation is always (at least nominally, but sometimes substantially) weaker than the Beta values resulting from the five pairwise analyses between percent Minority and one of the well-being variables.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e can be seen as a replication of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in that the latter features percent Red, which captures all seven presidential elections occurring in this century (note, the conclusions reported here did not change when re-coding percent Red to exclude the 2024 election, results of which were just presented above). In all ten of the before (bivariate correlation) and after (Beta weight after controlling for another variable) comparisons, effects were at least nominally and sometimes substantially larger in the \u0026ldquo;after\u0026rdquo; analyses. The mutual suppression effects here ranged in magnitude from the analysis focusing on Health (i.e., percent Minority increases from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.17 to Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.192; Health increases from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.40 to Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.409), to the analysis focusing on Crime (i.e., percent Minority increases from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.17 to Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.457; Crime increases from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.37 to Beta\u0026thinsp;=\u0026thinsp;.595).\u003c/p\u003e \u003cp\u003eIn sum, \u003cb\u003eH4\u003c/b\u003e and \u003cb\u003eH5\u003c/b\u003e seem strongly supported by the evidence. In every comparison, bivariate correlations were (at least nominally) smaller than the Beta weights resulting by including a second predictor in the regression equation. Moreover, every result featured in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows inverse relationships between both percent Minority and Well-being as predictors of votes cast for Republicans, thus supporting \u003cb\u003eH6\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e3.3. Hypotheses tests regarding state personality estimates\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH7\u003c/b\u003e: Openness will predict votes cast for Democratic candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH8\u003c/b\u003e: Conscientiousness will predict votes cast for Republican candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH9\u003c/b\u003e: Agreeableness will predict votes cast for Democratic candidates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH10\u003c/b\u003e: These traits will predict election outcomes even after controlling for sociodemographic variables.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAlthough I made no predictions on the issue, I first tested for suppression effects between either O, C, or A (entered separately) and percent Minority as predictors of election outcomes. The analyses revealed null effects for both O and A (results not reported here), but an unexpected and reasonably large suppression effect with C. Specifically, the before and after comparisons for Conscientiousness and percent Minority predicting percent Trump were: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.19; Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.318 (percent Minority), and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.24; Beta\u0026thinsp;=\u0026thinsp;.353 (Conscientiousness). Likewise when predicting percent Red, these values were: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.17; Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.315 (percent Minority), and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.30; Beta\u0026thinsp;=\u0026thinsp;.414 (Conscientiousness).\u003c/p\u003e \u003cp\u003eTurning next to the hypotheses, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that Openness was indeed negatively (and strongly) correlated both with both percent Trump (-.53) and percent Red (-.50), thus supporting \u003cb\u003eH7\u003c/b\u003e. However, correlations between Conscientiousness and election outcomes were marginal (.24 with percent Trump, and .30 with percent Red), although the direction of the effects were as predicted, showing partial support for \u003cb\u003eH8\u003c/b\u003e\u003csup\u003e[1]\u003c/sup\u003e. Next, \u003cb\u003eH9\u003c/b\u003e was not supported, as only weak correlations existed between Agreeableness and election outcomes (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13 for both percent Trump and percent Red); moreover, the direction of the effects was opposite from that predicted (i.e., the predicted signs should be negative here; but see the hierarchical regressions presented below).\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (predicting percent Trump) and 6 (predicting percent Red) display results relevant to \u003cb\u003eH10\u003c/b\u003e. In each table, Step 1 includes percent Minority, IQ, and the four well-being variables as \u0026ldquo;controls,\u0026rdquo; whereas Step 2 also includes O, C, and A. As an aside, several things are noteworthy about the Step 1 values in these tables. First, these variables alone explained almost 70% of the variance when predicting votes cast for Republicans. Second, Step 1 produced very large beta-weights for both Education (-.931 and \u0026minus;\u0026thinsp;1.01) and IQ (.580 and .825) in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHierarchical regressions predicting percent Trump\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΒ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 3:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Vaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHierarchical regressions predicting percent Red\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΒ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 3:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Vaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThird, and rather curiously, the IQ effects in each Table reflected massive suppression effects for this variable (smaller suppression effects also existed with Education across these two tables). Specifically, bivariate correlations between IQ and percent Trump (-.25) and percent Red (-.19) were small and negative in sign (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Yet at Step 1 in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the IQ Betas increased dramatically but they also flipped in direction from negative to positive (.580 and .825, respectively). Pesta (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported similar results when both IQ and the well-being variables were entered as predictors of election results. Follow-up analyses by Pesta (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that these effects were likely due to large collinearity between IQ and well-being. In fact, Pesta et al.\u0026rsquo;s (2010) original global well-being index included the IQ estimates as one of its sub-domains to avoid the issue of collinearity. Likewise, Pesta and McDaniel opted for two-variable regressions when testing for suppression effects for this same reason.\u003c/p\u003e \u003cp\u003eIn the present analyses, for example, Variance Inflation Factors for IQ ranged from 6.92\u0026ndash;13.54 in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. My goal with these tables, however, was to test whether O, C, or A uniquely predicted election results when important controls were also included in the models. To address the collinearity issue, however, I also conducted additional regressions (not reported here), either by including IQ but not the well-being variables or vice versa. These regressions justified two important conclusions. First, the IQ Betas remained negative when predicting election results, once the well-being variables were removed. Second, neither of these additional regressions altered the conclusions I report next regarding O, C, and A.\u003c/p\u003e \u003cp\u003eTurning back to \u003cb\u003eH10\u003c/b\u003e, Step 2 in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that Openness clearly predicted votes for Republicans even when various controls were also included in the equations. The effects, however, were attenuated relative to the bivariate correlations. Specifically in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Openness correlated \u0026minus;\u0026thinsp;.53 and \u0026minus;\u0026thinsp;.50 with percent Trump and percent Red, respectively; whereas the Step 2 Betas (from Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) were \u0026minus;\u0026thinsp;.258 and \u0026minus;\u0026thinsp;.248, respectively. Next, Conscientiousness failed to emerge as an important predictor of either percent Trump (Beta\u0026thinsp;=\u0026thinsp;.050) or percent Red (Beta\u0026thinsp;=\u0026thinsp;.130) in these analyses.\u003c/p\u003e \u003cp\u003eResults regarding Agreeableness are interesting in that, relative to the bivariate correlations (.13 for both percent Trump and percent Red), the signs flipped when control variables were also included in the models (Betas\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.150 and \u0026minus;\u0026thinsp;.218, respectfully). Though the resulting Betas are relatively weak here, they are consistent with \u003cb\u003eH9\u003c/b\u003e (i.e., that Agreeableness will predict votes cast for Democratic candidates), but only marginally consistent with \u003cb\u003eH10\u003c/b\u003e (as these Betas were greater than zero, but not significant). In sum, the analyses reported here reveal mixed support regarding tests of hypotheses related to personality traits as predictors of election outcomes.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Covid-19 vaccination rates and the best-fitting model\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (at Step 3) also show the robustness of Covid-19 vaccination rates as predictors of votes cast for Republicans. Regarding percent Trump (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), vaccination rates displayed the biggest Beta weight by far (-.609, with a next highest Beta of -257 for Education). However, the only other variable emerging as a significant predictor of percent Trump was Openness (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.166).\u003c/p\u003e \u003cp\u003eLikewise, when predicting percent Red (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), vaccination rates again emerged as the best predictor, with a Beta weight of \u0026minus;\u0026thinsp;.617 (wherein again Education produced the next highest Beta at \u0026minus;\u0026thinsp;.317). Here, however, all three personality traits hovered around \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05 when predicting percent Red: Openness (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.155, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.064), Conscientiousness (Beta\u0026thinsp;=\u0026thinsp;.255, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.058), and Agreeableness (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.241, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.025). Note also that that at least in these analyses, the directions (i.e., positive or negative) of the effects for O, C, and A are consistent with \u003cb\u003eH7\u003c/b\u003e, \u003cb\u003eH8\u003c/b\u003e, and \u003cb\u003eH9\u003c/b\u003e, respectively.\u003c/p\u003e \u003cp\u003eAlthough theoretical explanations for all effects reported above are of paramount importance scientifically, significant practical interest also exists in terms of establishing mere predictive power (e.g., over 80% of the variance is predicted at Step 3 in both Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e therefore provides a \u0026ldquo;dustbowl empiricism\u0026rdquo; test of all measures used in this study. Specifically, and disregarding theory, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents a stepwise regression aimed at finding the best-fitting models for predicting either percent Trump or percent Red.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBest fitting predictive models via stepwise regression\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent Trump:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Vaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent Red:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Vaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Covid-19 vaccination rates very strongly predicted percent Trump (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.720). Significant but much weaker effects also appeared for both Openness (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.179) and the Health sub-domain of well-being (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.161). Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e thus shows that these three variables explain fully 80% of the variance when predicting percent Trump. Interestingly, however, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reveals that only vaccination rates uniquely predicted percent Red (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.878, which is the value of the bivariate correlation presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The next strongest Beta weight was only \u0026minus;\u0026thinsp;.229, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.788 for Openness. In sum, vaccination rates alone explained 77% of the variance when predicting percent Red. Moreover, the prediction here was \u0026ldquo;robust\u0026rdquo; given that the effect emerged even after numerous controls were included in the regression equation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Principal Component Analyses\u003c/h2\u003e \u003cp\u003eGiven the complex pattern of results reported above, I sought to simplify explanations by conducting two separate PCA\u0026rsquo;s, one including percent Trump and all other variables except percent Red, and vice versa for the other analysis. Results appear in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePrincipal Components Analysis of all variables with either percent Trump or percent Red\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComponent 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComponent 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent Trump\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrump %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccinated %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Variance Explained %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19.8 / 65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(13.8 / 79.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent Red\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccinated %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Variance Explained %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19.6 / 64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(13.8 / 78.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. Loadings less than |.60| are not reported here.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding percent Trump, three significant components emerged, and each seem readily interpretable. Component 1 clearly features relationships between IQ and the well-being variables (together with vaccination rates) as covariates of votes cast for Trump. Moreover, the directions for all these loadings are consistent with the hypotheses tested above. Component 2 seems to capture a strong positive relationship between percent Minority and Openness, while Component 3 reflects the strong positive relationship between Conscientiousness and Agreeableness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.67 from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with Rentfrow et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, providing the original data).\u003c/p\u003e \u003cp\u003eIn terms of variable loadings, the PCA featuring percent Red exactly mirrored the PCA featuring percent Trump. In sum, findings from the PCA analyses here are consistent with the main results reported above: (1) once untangled, percent Minority, IQ, well-being, and vaccination rates all covary strongly with Republican votes; whereas (2) no overly strong relationships appear with the personality variables and election outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides novel insights into the predictors of U.S. presidential voting patterns, emphasizing the unexpected strength of COVID-19 vaccination rates as a correlate of Republican voting outcomes. Below, I contextualize these findings, discuss their implications, and identify avenues for future research.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Key findings and interpretations\u003c/h2\u003e \u003cp\u003eThe most striking result of this study is the robust negative correlation between state-level COVID-19 vaccination rates and votes cast for Republican candidates. This relationship persisted across multiple elections and statistical models, highlighting the extent to which health behaviors have become a marker of partisan identity in the U.S. Democratic-leaning states, characterized by higher vaccination rates, likely reflect greater trust in science, government, and public health policies. Conversely, Republican-leaning states, with lower vaccination rates, embody cultural values prioritizing individualism and skepticism of authority, consistent with conservative ideology (e.g., Callaghan et al., 2021; Murphy et al., 2021).\u003c/p\u003e \u003cp\u003eMutual suppression effects further reveal the complexity of voting behavior. Variables such as state IQ and well-being indices gained predictive power when analyzed alongside racial composition. These results underscore the importance of multivariate approaches to understanding the interplay of demographic, psychological, and sociocultural factors. For example, while state-level IQ and minority population percentages exhibited weak bivariate correlations with voting outcomes, their combined analysis illuminated strong and significant relationships. This finding aligns with prior research on suppression effects in political science and psychology (Pesta \u0026amp; McDaniel, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the Big Five personality trait of Openness to Experience emerged as a consistent predictor of Democratic voting. This finding supports existing literature linking Openness to liberal social and political values, such as a preference for diversity and progressive change (Rentfrow et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, other traits, such as Conscientiousness and Agreeableness, showed weaker and less consistent associations, warranting further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Implications for electoral modeling\u003c/h2\u003e \u003cp\u003eThe results of this study highlight the need to incorporate non-traditional variables, such as vaccination rates, into electoral prediction models. These findings suggest that health behaviors may serve as powerful proxies for underlying ideological and cultural divides, offering new avenues for understanding voter preferences. Traditional predictors, including income and education, remain important but may need to be contextualized within emerging societal trends.\u003c/p\u003e \u003cp\u003eThe significant role of suppression effects also underscores the limitations of simple bivariate analyses in explaining complex phenomena. Future research and electoral models should prioritize multivariate approaches to better capture the nuanced relationships between variables like racial composition, intelligence, and well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and future directions\u003c/h2\u003e \u003cp\u003eWhile the study provides robust statistical evidence, several limitations must be acknowledged. The use of aggregate state-level data risks ecological fallacy, where relationships observed at the group level may not apply to individuals. Future research should validate these findings with individual-level data to strengthen their generalizability.\u003c/p\u003e \u003cp\u003eAdditionally, the theoretical basis for the link between vaccination rates and voting patterns requires further exploration. While the study identifies a strong empirical relationship, understanding the psychological and sociocultural mechanisms driving this connection is crucial. Expanding the scope to include related factors, such as media consumption and misinformation, could provide deeper insights.\u003c/p\u003e \u003cp\u003eFinally, while this study focuses on the U.S. context, its broader applicability remains uncertain. Cross-national studies examining similar variables in other democracies could illuminate whether these patterns are unique to the U.S. or reflect broader global trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Conclusion\u003c/h2\u003e \u003cp\u003eThis study contributes to the growing body of literature on the psychological and sociocultural determinants of voting behavior, offering a novel perspective on the role of health behaviors in shaping political identity. The findings highlight the importance of adopting multidimensional and dynamic models in electoral studies. By integrating traditional and non-traditional predictors, researchers can develop a more comprehensive understanding of voter behavior in an increasingly polarized society.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eNotes. This research was not funded. Clinical trial number: not applicable. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll of it.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data appear in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCallaghan, Timothy, Ali Moghtaderi, Jennifer A. 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Penguin Books.\u003c/li\u003e\n\u003cli\u003eSutin, Angelina R., Yannick Stephan, and Antonio Terracciano. 2021. \u0026quot;Personality Traits and Susceptibility to COVID-19 Infection and Vaccine Hesitancy.\u0026quot; \u003cem\u003ePersonality and Individual Differences \u003c/em\u003e177: 110817. https://doi.org/10.1016/j.paid.2021.110817.\u003c/li\u003e\n\u003cli\u003eU.S. Census Bureau. 2000, 2010, 2020. \u0026quot;State Racial Composition Data.\u0026quot; https://www.census.gov/topics/population/race.html.\u003c/li\u003e\n\u003cli\u003eUnited States Federal Election Commission. 2024. \u0026quot;Election Results and Voting Information.\u0026quot; https://www.fec.gov/data/candidates/president/presidential-map/.\u003c/li\u003e\n\u003cli\u003eVecchione, Michele, and Gian Vittorio Caprara. 2009. \u0026quot;Personality Determinants of\u003c/li\u003e\n\u003cli\u003ePolitical Participation: The Contribution of Traits and Self-Efficacy Beliefs.\u0026quot; \u003cem\u003ePersonality\u003c/em\u003e \u003cem\u003eand Individual Differences\u003c/em\u003e 46(4): 487\u0026ndash;92. https://doi.org/10.1016/j.paid.2008.11.021.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e However, one could argue that H8 was fully supported here even though the correlation between Conscientiousness and percent Trump (.24) was not \u0026ldquo;significant.\u0026rdquo; This is because statistical significance is irrelevant when dealing with populations (i.e., the 50 US states) versus samples, and because even a correlation of .24 has predictive value.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6228815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6228815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the predictors of U.S. presidential voting patterns, focusing on the interplay between psychological, sociocultural, and health-related factors. Using state-level data from seven presidential elections (2000–2024), the analysis evaluates the predictive power of intelligence (IQ), well-being indicators (e.g., education, income), Big Five personality traits, and COVID-19 vaccination rates. Among these, vaccination rates emerged as the strongest and most consistent predictor of state-level election outcomes, underscoring the polarization of health behaviors as a reflection of partisan identity. Additionally, suppression effects highlighted the complex interactions between demographic variables, such as racial composition and IQ, in enhancing predictive accuracy. While traditional predictors like well-being and personality traits remain relevant, the findings reveal that health-related behaviors encapsulate deeper ideological and cultural divides. By integrating established and novel predictors, this study advances the understanding of voting behavior in an increasingly polarized society and emphasizes the value of multidimensional approaches in electoral modeling.\u003c/p\u003e","manuscriptTitle":"States, Stats, and Shots: The Omnibus Theoretical Value of Vaccination Rates As Predictors of US Presidential Voting Patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 14:00:00","doi":"10.21203/rs.3.rs-6228815/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2205e86b-cd1a-4295-a594-9899e6679333","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-13T18:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 14:00:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6228815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6228815","identity":"rs-6228815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}