| Variable | Value | Count | Percent |
|---|---|---|---|
| EDUCATION\_LEVEL | High school diploma or GED | 8 | 6.6\% |
| EDUCATION\_LEVEL | Some college, but no degree | 21 | 17.2\% |
| EDUCATION\_LEVEL | Associates or technical degree | 7 | 5.7\% |
| EDUCATION\_LEVEL | Bachelor’s degree | 46 | 37.7\% |
| EDUCATION\_LEVEL | Graduate or professional degree | 40 | 32.8\% |
| ETHNICITY | Asian | 24 | 19.7\% |
| ETHNICITY | Black | 25 | 20.5\% |
| ETHNICITY | White | 48 | 39.3\% |
| ETHNICITY | Mixed/Other | 25 | 20.5\% |
| Employment.status | Part-Time | 29 | 23.8\% |
| Employment.status | Full-Time | 50 | 41\% |
| Employment.status | Unemployed (and job seeking) | 12 | 9.8\% |
| Employment.status | Not in paid work (e.g. homemaker, retired or disabled) | 10 | 8.2\% |
| Employment.status | Due to start a new job within the next month | 1 | 0.8\% |
| Employment.status | Other | 3 | 2.5\% |
| Employment.status | DATA\_EXPIRED | 17 | 13.9\% |
| INCOME | Less than \$25,000 | 12 | 9.8\% |
| INCOME | \$25,000-\$49,999 | 25 | 20.5\% |
| INCOME | \$50,000-\$74,999 | 22 | 18\% |
| INCOME | \$75,000-\$99,999 | 14 | 11.5\% |
| INCOME | \$100,000-\$149,999 | 36 | 29.5\% |
| INCOME | \$150,000 or more | 11 | 9\% |
| INCOME | Prefer not to say | 2 | 1.6\% |
| Nationality | United States | 122 | 100\% |
| POLITICALAFFIL | Conservative Party | 9 | 8.7\% |
| POLITICALAFFIL | Democratic Party | 52 | 50.5\% |
| POLITICALAFFIL | Libertarian Party | 8 | 7.8\% |
| POLITICALAFFIL | Republican Party | 33 | 32\% |
| POLITICALAFFIL | Socialist or Green Party | 1 | 1\% |
| POLITICALPARTY | Democrat | 43 | 35.2\% |
| POLITICALPARTY | Republican | 39 | 32\% |
| POLITICALPARTY | Independent | 40 | 32.8\% |
| RACIALIDENTITY.4 | Asian | 23 | 18.9\% |
| RACIALIDENTITY.4 | Black | 28 | 23\% |
| RACIALIDENTITY.4 | White | 49 | 40.2\% |
| RACIALIDENTITY.4 | Mixed/Other | 22 | 18\% |
| RACIALIDENTITY.6 | Asian | 23 | 18.9\% |
| RACIALIDENTITY.6 | Black | 28 | 23\% |
| RACIALIDENTITY.6 | White | 49 | 40.2\% |
| RACIALIDENTITY.6 | Other | 3 | 2.5\% |
| RACIALIDENTITY.6 | Latine | 9 | 7.4\% |
| RACIALIDENTITY.6 | Mixed | 10 | 8.2\% |
| RELIGIOUS\_Identity | Other | 4 | 3.3\% |
| RELIGIOUS\_Identity | Christian | 65 | 53.7\% |
| RELIGIOUS\_Identity | Muslim | 2 | 1.7\% |
| RELIGIOUS\_Identity | Hindu | 2 | 1.7\% |
| RELIGIOUS\_Identity | Jewish | 1 | 0.8\% |
| RELIGIOUS\_Identity | Folk Religion | 1 | 0.8\% |
| RELIGIOUS\_Identity | Religiously Unaffiliated | 43 | 35.5\% |
| RELIGIOUS\_Identity | Decline to answer | 3 | 2.5\% |
| SERIOUS | Yes | 121 | 100\% |
| SEX | Female | 59 | 48.4\% |
| SEX | Male | 63 | 51.6\% |
| SEXUAL\_IDENTITY | Straight or heterosexual | 85 | 69.7\% |
| SEXUAL\_IDENTITY | Gay or lesbian | 10 | 8.2\% |
| SEXUAL\_IDENTITY | Bisexual, pansexual, or queer | 22 | 18\% |
| SEXUAL\_IDENTITY | Asexual | 4 | 3.3\% |
| SEXUAL\_IDENTITY | Not sure | 1 | 0.8\% |
| STREETRACE | Black | 30 | 25\% |
| STREETRACE | White | 54 | 45\% |
| STREETRACE | Latine | 7 | 5.8\% |
| STREETRACE | Asian American | 21 | 17.5\% |
| STREETRACE | Native American/American Indian | 3 | 2.5\% |
| STREETRACE | Mexican | 4 | 3.3\% |
| STREETRACE | Some other race | 1 | 0.8\% |
| Student.status | DATA\_EXPIRED | 18 | 14.8\% |
| Student.status | No | 74 | 60.7\% |
| Student.status | Yes | 30 | 24.6\% |
| VOTE\_2024 | Donald Trump | 51 | 42.9\% |
| VOTE\_2024 | Kamala Harris | 50 | 42\% |
| VOTE\_2024 | Chase Oliver | 1 | 0.8\% |
| VOTE\_2024 | Cornel West | 1 | 0.8\% |
| VOTE\_2024 | DID NOT VOTE IN 2024 | 16 | 13.4\% |
Introduction
Zero-sum beliefs – the subjective view that as one person gains, others inevitably lose – has been linked to numerous beneficial and harmful social, political, and psychological outcomes (Davidai & Tepper, 2023). A zero-sum mindset has been conceptualized as a specific type of competitive belief regarding the relative distribution of limited resources (e.g., physical resources and social status) between groups (e.g., racial groups). These beliefs can be a general set of zero-sum beliefs about win vs. loss thinking and domain-specific beliefs between specific groups and/or with a specific context (e.g., immigration, race relations, public policies, and geopolitical conflicts) (Davidai & Tepper, 2023). For general zero sum beliefs, (Różycka-Tran et al., 2015) find that individuals with a high belief in the zero-sum game are negatively correlated with numerous socio-economic-political indicators: gross domestic product, the human development index, democracy index, and subjective well-being.
(Chinoy et al., n.d.) show zero-sum thinking differs by age, gender, education, racial identity, and urbancity in a sample of 20,400 U.S. residents. Most noteworthy, the authors argue that zero-sum thinking is the root of political differences, demonstrating zero-sum beliefs differ between political parties despite high variability within each major political party. Their study finds the variability in zero-sum thinking partially explains a range of social and political views: pro-redistribution policy support, racial attitudes, gender attitudes, and anti-immigration attitudes beyond traditional sociodemographic measures.
In this study, we integrate prior work on social identity theory with newer research on zero-sum beliefs to create and examine the influence of zero-sum social identities (along with zero-sum general beliefs and zero-sum economic beliefs) in relation to voter behavior and choice. Using a cross-sectional study of relatively similar groups of Democrats, Independents, and Republicans, we intend to examine zero-sum beliefs based on political party affiliation and as predictors of voter behavior in the 2024 U.S. Presidential election.
Methods
Participants and Sampling
More than fifty-five thousand people who are active on the Prolific platform were eligible to complete a 45-minute health beliefs survey with measures on various beliefs associated with politics and health. Using a quota sample by gender (50% Man/ 50% Woman), political affiliation (33% Republican, 33% Democrat, and 34% Independent), and race/RACIALIDENTITY.4 (White 40%, Black 20%, Asian 20%, Mixed 10%, and Other 10%), Prolific recruited one hundred and twenty-five people to complete the survey.
A total of 135 individuals were recruited through Prolific, and 10 were excluded due to incomplete responses, yielding a final analytic sample of 125 participants. The final sample was roughly balanced in terms of gender (50.4% male), racial identity (White: 39.2%, Black: 22.4%, Asian: 18.4%, Mixed/Other: 17.6%), and political affiliation (Democrats: 34.4%, Independents: 32.0%, Republicans: 31.2%).
Measures
Respondents were asked to report their gender (selections: Girl or woman, boy or man, nonbinary/genderfluid/genderqueer, I am not sure/questioning), racial identity (selections: American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latine, Middle Eastern or North African, Native Hawaiian/Pacific Islander, White, Other), social status (selections: Lowest 1- Highest 10), and political beliefs (selections: far left/leftist, very liberal, liberal, moderate, conservative, very conservative, alt-right/far-right), education (selections: Some high school or less, High school diploma or GED, Some college, but no degree, Associates or technical degree, Bachelor’s degree, Graduate or professional degree (MA, MS, MBA, PhD, JD, MD, DDS etc.)), political affiliation/party (selections: Conservative Party, Democratic Party, Libertarian Party, Republican Party, Socialist or Green Party), 2024 voting behavior (selections: Donald Trump, Kamala Harris, Jill Stein, Robert Kennedy Jr., Chase Oliver, Claudia De La Cruz, Cornel West, DID NOT VOTE IN 2024)
Respondents were asked to report their level of agreement using a 7-point Likert scale (1: Strongly Disbelieve, 2: Disbelieve, 3: Somewhat Disbelieve, 4: Neither, 5: Somewhat Believe, 6: Believe, 7: Strongly Believe) with 11 zero-sum belief statements.
Data Analysis Plan
Data was exported from the qualtrics platform in numerical format and imported into Posit Cloud. R code provided in the data science workflow (Wickham & Grolemund, 2023) was modified to install R packages (see install.R), import data (alldata.csv) using readr, transform gender identity (GENDER) to a binary variable (GENDER_MAN) using dplyr, visualize data in a raincloud plot using ggplot2, and model using an independent samples Welch t-test from the stats package to examine gender differences in zero sum sexism beliefs.
We analyzed 11 zero-sum belief items (ZEROSUM_), covering both economic and social identity dimensions. Our primary goal was to assess differences across political affiliation (POLITICALPARTY) and racial/ethnic identity (RACIALIDENTITY.4).
First, we conducted a two-way Analysis of Variance (ANOVA) for each zero-sum belief item. To assess whether ANOVA assumptions were met, we used the Shapiro-Wilk test to evaluate the normality of residuals. All 11 items showed non-normality. However, given ANOVA’s robustness to moderate deviations from normality, we further examined residuals using Q-Q plots (qqnorm and qqline) to visualize distributional shape. These plots indicated that the violations were not severe, so we proceeded with ANOVA while noting the assumption limitations.
To address the non-normality more formally and ensure result accuracy, we complemented the ANOVA with a non-parametric Kruskal-Wallis test for each zero-sum belief. This approach allowed us to evaluate group differences without relying on normality assumptions.
Next, an explanatory logistic regression was conducted to classy voter’s preference for Donald Trump (1) vs. Kamala Harris (0) ( TRUMPVOTE ) using POLITICALBELIEFS, ZEROSUM_ECONOMIC, ZEROSUM_IDENTITY, ZEROSUM_1, GENDER_MALE, RELIGIOUS_YES, RACE_BLACK, RACE_ASIAN, RACE_OTHER, EDUCATION_HIGH, and SOCIALSTATUS.
Additionally, we used the tidymodels package in R to classify using a predictive modeling approach. Specifically, both decision tree and random forest classifiers were implemented to predict voter preference.
Using this integrative modeling framework, we provide both explanatory and predictive models for classifying voter preferences.
Results
Descriptive Statistics
| Variable | n | mean | median | sd | min | max |
|---|---|---|---|---|---|---|
| AGE | 121 | 36.10 | 33.0 | 12.73 | 19 | 73 |
| SOCIALSTATUS | 122 | 5.30 | 5.5 | 1.78 | 1 | 9 |
| POLITICALBELIEFS | 120 | 3.67 | 4.0 | 1.35 | 1 | 6 |
| ATTENTION3 | 122 | 2.00 | 2.0 | 0.00 | 2 | 2 |
| ZEROSUM_1 | 122 | 3.92 | 4.0 | 1.78 | 1 | 7 |
| ZEROSUM_2 | 121 | 4.75 | 5.0 | 1.59 | 1 | 7 |
| ZEROSUM_3 | 121 | 4.92 | 5.0 | 1.49 | 1 | 7 |
| ZEROSUM_4 | 121 | 3.07 | 3.0 | 1.69 | 1 | 7 |
| ZEROSUM_5 | 122 | 2.84 | 3.0 | 1.75 | 1 | 7 |
| ZEROSUM_6 | 121 | 3.43 | 3.0 | 2.07 | 1 | 7 |
| ZEROSUM_7 | 120 | 3.52 | 4.0 | 1.81 | 1 | 7 |
| ZEROSUM_8 | 121 | 2.93 | 3.0 | 1.63 | 1 | 7 |
| ZEROSUM_9 | 121 | 2.59 | 2.0 | 1.74 | 1 | 7 |
| ZEROSUM_10 | 122 | 3.20 | 3.0 | 1.75 | 1 | 7 |
| ZEROSUM_11 | 121 | 3.17 | 3.0 | 1.74 | 1 | 7 |
| NEOLIB_1 | 119 | 4.87 | 5.0 | 1.05 | 2 | 6 |
| NEOLIB_2 | 121 | 4.35 | 5.0 | 1.34 | 1 | 6 |
| NEOLIB_3 | 116 | 4.13 | 4.0 | 1.39 | 1 | 6 |
Table 2 reports the descriptive statistics (count, mean, median, sd, min, max) for all study variables. The sample consisted of 122 participants, with a mean age of 36.1 years (SD = 12.73, range = 19–73). Participants reported a mean social status of 5.3 (SD = 1.78) on a 10-point scale.
Zero-sum belief items were rated on a 7-point scale with neutral in the middle. For Item 1, the average score was close to neutral (M = 3.92). Items 2 and 3 had higher average scores (M = 4.75 and M = 4.92, respectively), indicating general agreement. Items 4 through 11 showed lower average scores (ranging from 2.59 to 3.52), reflecting more disagreement than agreement with those statements.
Factor Analysis
Factor Analysis of Zero-Sum Beliefs
The output below displays the correlation matrix of the Zero-Sum Beliefs items. Each cell represents the Pearson correlation between pairs of items. ZEROSUM_1 is moderately positively correlated with most of the other zero-sum items (r = .092 - .462), suggesting a general zero-sum thinking. ZEROSUM_2 and ZEROSUM_3 also show a moderate positive correlation with each other (r = .479). Most notably, ZEROSUM_4 through ZEROSUM_11 are consistently and positively correlated, with moderately positive correlations (ranging from 0.46 to 0.68), indicating a strong internal consistency among these items. This pattern supports the idea that these items are likely capturing a common latent construct.
ZEROSUM_1 ZEROSUM_2 ZEROSUM_3 ZEROSUM_4 ZEROSUM_5
ZEROSUM_1 1.00000000 0.386159282 0.09223868 0.46244644 0.354780151
ZEROSUM_2 0.38615928 1.000000000 0.47893466 0.01604828 -0.007056022
ZEROSUM_3 0.09223868 0.478934659 1.00000000 -0.02418897 -0.109827301
ZEROSUM_4 0.46244644 0.016048284 -0.02418897 1.00000000 0.611174045
ZEROSUM_5 0.35478015 -0.007056022 -0.10982730 0.61117404 1.000000000
ZEROSUM_6 0.36699616 0.031303525 -0.17086105 0.60219942 0.607185097
ZEROSUM_7 0.12009911 -0.098678831 -0.14396820 0.34550888 0.536331658
ZEROSUM_8 0.25942078 -0.155377765 -0.12939334 0.49740256 0.586804352
ZEROSUM_9 0.41016910 0.078257987 -0.12138304 0.56204342 0.628375488
ZEROSUM_10 0.29170352 -0.114520512 -0.18994979 0.49480889 0.683232725
ZEROSUM_11 0.27789618 -0.019381988 -0.18865010 0.46048999 0.570020242
ZEROSUM_6 ZEROSUM_7 ZEROSUM_8 ZEROSUM_9 ZEROSUM_10
ZEROSUM_1 0.36699616 0.12009911 0.2594208 0.41016910 0.2917035
ZEROSUM_2 0.03130352 -0.09867883 -0.1553778 0.07825799 -0.1145205
ZEROSUM_3 -0.17086105 -0.14396820 -0.1293933 -0.12138304 -0.1899498
ZEROSUM_4 0.60219942 0.34550888 0.4974026 0.56204342 0.4948089
ZEROSUM_5 0.60718510 0.53633166 0.5868044 0.62837549 0.6832327
ZEROSUM_6 1.00000000 0.55401598 0.5067006 0.58654354 0.6812058
ZEROSUM_7 0.55401598 1.00000000 0.4203826 0.38964490 0.6145775
ZEROSUM_8 0.50670056 0.42038261 1.0000000 0.52719441 0.5667796
ZEROSUM_9 0.58654354 0.38964490 0.5271944 1.00000000 0.6009058
ZEROSUM_10 0.68120581 0.61457749 0.5667796 0.60090582 1.0000000
ZEROSUM_11 0.67721379 0.51776718 0.4961602 0.52655236 0.6724939
ZEROSUM_11
ZEROSUM_1 0.27789618
ZEROSUM_2 -0.01938199
ZEROSUM_3 -0.18865010
ZEROSUM_4 0.46048999
ZEROSUM_5 0.57002024
ZEROSUM_6 0.67721379
ZEROSUM_7 0.51776718
ZEROSUM_8 0.49616017
ZEROSUM_9 0.52655236
ZEROSUM_10 0.67249386
ZEROSUM_11 1.00000000
Parallel analysis suggests that the number of factors = 2 and the number of
components = NA Factor Analysis using method = ml
Call: fa(r = df.ZEROSUM, nfactors = 2, rotate = "promax", fm = "ml")
Standardized loadings (pattern matrix) based upon correlation matrix
ML1 ML2 h2 u2 com
ZEROSUM_1 0.45 0.42 0.38 0.62 2.0
ZEROSUM_2 0.00 0.92 0.85 0.15 1.0
ZEROSUM_3 -0.17 0.51 0.29 0.71 1.2
ZEROSUM_4 0.69 0.06 0.48 0.52 1.0
ZEROSUM_5 0.81 0.00 0.66 0.34 1.0
ZEROSUM_6 0.82 0.03 0.67 0.33 1.0
ZEROSUM_7 0.64 -0.12 0.42 0.58 1.1
ZEROSUM_8 0.67 -0.15 0.48 0.52 1.1
ZEROSUM_9 0.74 0.09 0.56 0.44 1.0
ZEROSUM_10 0.84 -0.12 0.72 0.28 1.0
ZEROSUM_11 0.76 -0.03 0.57 0.43 1.0
ML1 ML2
SS loadings 4.71 1.36
Proportion Var 0.43 0.12
Cumulative Var 0.43 0.55
Proportion Explained 0.78 0.22
Cumulative Proportion 0.78 1.00
With factor correlations of
ML1 ML2
ML1 1.00 -0.01
ML2 -0.01 1.00
Mean item complexity = 1.1
Test of the hypothesis that 2 factors are sufficient.
df null model = 55 with the objective function = 5.55 with Chi Square = 646.26
df of the model are 34 and the objective function was 0.45
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.06
The harmonic n.obs is 121 with the empirical chi square 26.71 with prob < 0.81
The total n.obs was 122 with Likelihood Chi Square = 51.63 with prob < 0.027
Tucker Lewis Index of factoring reliability = 0.951
RMSEA index = 0.065 and the 90 % confidence intervals are 0.023 0.1
BIC = -111.7
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
ML1 ML2
Correlation of (regression) scores with factors 0.96 0.93
Multiple R square of scores with factors 0.92 0.87
Minimum correlation of possible factor scores 0.84 0.73
Loadings:
ML1 ML2
ZEROSUM_1 0.448 0.424
ZEROSUM_2 0.922
ZEROSUM_3 -0.167 0.508
ZEROSUM_4 0.689
ZEROSUM_5 0.810
ZEROSUM_6 0.816
ZEROSUM_7 0.639 -0.120
ZEROSUM_8 0.674 -0.149
ZEROSUM_9 0.742
ZEROSUM_10 0.837 -0.124
ZEROSUM_11 0.757
ML1 ML2
SS loadings 4.712 1.355
Proportion Var 0.428 0.123
Cumulative Var 0.428 0.551
Loadings:
ML1 ML2
ZEROSUM_1 0.448 0.424
ZEROSUM_2 0.922
ZEROSUM_3 -0.167 0.508
ZEROSUM_4 0.689
ZEROSUM_5 0.810
ZEROSUM_6 0.816
ZEROSUM_7 0.639 -0.120
ZEROSUM_8 0.674 -0.149
ZEROSUM_9 0.742
ZEROSUM_10 0.837 -0.124
ZEROSUM_11 0.757
ML1 ML2
SS loadings 4.712 1.355
Proportion Var 0.428 0.123
Cumulative Var 0.428 0.551The results of the factor analysis – an unsupervised machine learning technique – support a two factor model with a promax rotation. The first item loads equally on each factor and will not be included in the composite construction. Based on the items, we named the first factor as ZEROSUM_ECONOMIC and the second factor as ZEROSUM_IDENTITY to correspond with the two different referents of economic (e.g., wealth vs. poor) and social identity (e.g., racial minorities vs. white people), respectively. The IDENTITY factor showed high reliability, with a Cronbach’s alpha of 0.91. This means the items in the ZEROSUM_IDENTITY scale consistently measure the same social identity concept.
Reliability of Zero Sum Economic Beliefs
Reliability analysis
Call: psych::alpha(x = select_data[, c("ZEROSUM_2", "ZEROSUM_3")])
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
0.65 0.65 0.48 0.48 1.8 0.064 4.8 1.3 0.48
95% confidence boundaries
lower alpha upper
Feldt 0.50 0.65 0.75
Duhachek 0.52 0.65 0.77
Reliability if an item is dropped:
raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
ZEROSUM_2 0.51 0.48 0.23 0.48 0.92 NA 0 0.48
ZEROSUM_3 0.45 0.48 0.23 0.48 0.92 NA 0 0.48
Item statistics
n raw.r std.r r.cor r.drop mean sd
ZEROSUM_2 121 0.87 0.86 0.6 0.48 4.8 1.6
ZEROSUM_3 121 0.85 0.86 0.6 0.48 4.9 1.5
Non missing response frequency for each item
1 2 3 4 5 6 7 miss
ZEROSUM_2 0.04 0.06 0.09 0.21 0.29 0.15 0.17 0.01
ZEROSUM_3 0.02 0.06 0.06 0.21 0.29 0.19 0.17 0.01The zero-sum economic beliefs factor showed lower reliability, with a Cronbach’s alpha of 0.65. A lower reliability is expected in a two-item measure.
Factor Analysis of Neoliberal Mindset
NEOLIB_1 NEOLIB_2 NEOLIB_3
NEOLIB_1 1.0000000 0.3754587 0.4118161
NEOLIB_2 0.3754587 1.0000000 0.6377101
NEOLIB_3 0.4118161 0.6377101 1.0000000
Reliability analysis
Call: psych::alpha(x = df.NEOLIB)
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
0.74 0.74 0.67 0.48 2.8 0.039 4.5 1.1 0.41
95% confidence boundaries
lower alpha upper
Feldt 0.64 0.74 0.81
Duhachek 0.66 0.74 0.81
Reliability if an item is dropped:
raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
NEOLIB_1 0.78 0.78 0.64 0.64 3.6 0.040 NA 0.64
NEOLIB_2 0.56 0.58 0.41 0.41 1.4 0.076 NA 0.41
NEOLIB_3 0.55 0.56 0.39 0.39 1.3 0.079 NA 0.39
Item statistics
n raw.r std.r r.cor r.drop mean sd
NEOLIB_1 119 0.70 0.74 0.50 0.44 4.9 1.0
NEOLIB_2 121 0.85 0.84 0.73 0.63 4.3 1.3
NEOLIB_3 116 0.88 0.85 0.75 0.64 4.1 1.4
Non missing response frequency for each item
1 2 3 4 5 6 miss
NEOLIB_1 0.00 0.03 0.06 0.24 0.34 0.33 0.02
NEOLIB_2 0.02 0.08 0.15 0.24 0.27 0.23 0.01
NEOLIB_3 0.06 0.08 0.15 0.27 0.28 0.16 0.05
Loadings:
ML1
NEOLIB_1 0.497
NEOLIB_2 0.777
NEOLIB_3 0.827
ML1
SS loadings 1.536
Proportion Var 0.512All 3 items load strongly on a single factor: NEOLIB_2 and NEOLIB_3 are especially strong (~.80) NEOLIB_1 is OK (.50 is acceptable for a 3–item scale)
Inferential Tests
Comparing Zero-Sum Beliefs
Paired t-test
data: select_data$ZEROSUM_ECONOMIC and select_data$ZEROSUM_IDENTITY
t = 9.3989, df = 119, p-value = 4.97e-16
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
1.379676 2.116157
sample estimates:
mean difference
1.747917
There is a significant difference between participants’ scores on ZEROSUM_ECONOMIC and ZEROSUM_IDENTITY. On average, participants rated economic zero-sum beliefs 1.75 points higher than identity zero-sum beliefs, 95% CI [1.38, 2.12].
The final sample reflected racial diversity, with participants identifying as White (n = 48), Black (n = 25), Mixed or Other (n = 25), and Asian (n = 24).
Asian Black Mixed/Other White
24 25 25 48 RACE RACIALIDENTITY.4 ETHNICITY
1 3 Black Black
2 7 White White
3 3,7 Mixed/Other Mixed/Other
4 2 Asian Asian
5 7 White White
6 3 Black Black
7 7 White White
8 7 White Mixed/Other
9 7 White White
10 7 White White
11 7 White White
12 8 Mixed/Other Mixed/Other
13 3 Black Black
14 3,7 Mixed/Other Mixed/Other
15 6 Mixed/Other White
16 3 Black Black
17 3 Black Black
18 7 White White
19 3 Black Black
20 7 White White
21 7 White White
22 3 Black Black
23 3 Black Black
24 2 Asian Asian
25 7 White White
26 3 Black Black
27 4 Mixed/Other Mixed/Other
28 7 White White
29 7 White White
30 2 Asian Asian
31 3 Black Black
32 2 Asian Asian
33 4 Mixed/Other Mixed/Other
34 1,7 Mixed/Other White
35 2 Asian Asian
36 2 Asian Asian
37 4,7 Mixed/Other White
38 4 Mixed/Other White
39 7 White White
40 4 Mixed/Other Mixed/Other
41 7 White White
42 7 White Mixed/Other
43 4 Mixed/Other Mixed/Other
44 7 White White
45 4 Mixed/Other Mixed/Other
46 2 Asian Asian
47 2 Asian Asian
48 7 White White
49 3 Black Black
50 7 White White
51 3,4,7 Mixed/Other Mixed/Other
52 7 White White
53 3 Black Black
54 3 Black Black
55 3 Black Mixed/Other
56 7 White White
57 7 White White
58 3 Black Black
59 4 Mixed/Other Mixed/Other
60 2 Asian Asian
61 3 Black Black
62 7 White White
63 7 White White
64 3 Black Black
65 3 Black Black
66 7 White Mixed/Other
67 3 Black Black
68 7 White White
69 7 White White
70 7 White White
71 7 White White
72 5 Mixed/Other Mixed/Other
73 7 White White
74 3 Black Mixed/Other
75 2 Asian Asian
76 7 White White
77 7 White White
78 7 White White
79 7 White White
80 2 Asian Asian
81 2,3 Mixed/Other Mixed/Other
82 7 White Asian
83 4 Mixed/Other Mixed/Other
84 3 Black Black
85 2 Asian Asian
86 3 Black Mixed/Other
87 2,7 Mixed/Other Mixed/Other
88 7 White White
89 3 Black Black
90 3 Black Black
91 7 White White
92 3 Black Black
93 7 White White
94 7 White White
95 3 Black Black
96 2 Asian Asian
97 4 Mixed/Other Mixed/Other
98 2 Asian Asian
99 2 Asian Asian
100 2 Asian Asian
101 7 White White
102 3,6 Mixed/Other Mixed/Other
103 7 White White
104 2,7 Mixed/Other Mixed/Other
105 2 Asian Asian
106 7 White White
107 7 White White
108 2 Asian Asian
109 3 Black Black
110 7 White White
111 7 White White
112 7 White White
113 2 Asian Asian
114 2 Asian Asian
115 7 White White
116 2 Asian Asian
117 2 Asian Asian
118 7 White White
119 2 Asian Asian
120 1,7 Mixed/Other Mixed/Other
121 3 Black Black
122 7 White Mixed/Other
Asian Black Mixed/Other White
Asian 23 0 0 0
Black 0 25 3 0
Mixed/Other 0 0 18 4
White 1 0 4 44Zero-Sum Beliefs by Gender
Welch Two Sample t-test
data: ZEROSUM_1 by GENDER_MALE
t = 0.88535, df = 113.66, p-value = 0.3778
alternative hypothesis: true difference in means between group 0 and group 1 is
not equal to 0
95 percent confidence interval:
-0.3542078 0.9266216
sample estimates:
mean in group 0 mean in group 1
4.086207 3.800000
There is no significant difference in ZEROSUM_1 scores between men and women (p = .63). The difference in means is small and not statistically meaningful. The 95% CI (-0.48, 0.80) also includes zero, supporting the lack of difference.
Welch Two Sample t-test
data: ZEROSUM_ECONOMIC by GENDER_MALE
t = 1.0708, df = 111.27, p-value = 0.2866
alternative hypothesis: true difference in means between group 0 and group 1 is
not equal to 0
95 percent confidence interval:
-0.2249540 0.7539014
sample estimates:
mean in group 0 mean in group 1
4.956140 4.691667
There is no significant difference in ZEROSUM_ECONOMIC beliefs between men and women (p = .27). Although women’s mean score appears slightly higher, this difference is not statistically significant. The 95% CI (-0.21, 0.75) includes zero.
Welch Two Sample t-test
data: ZEROSUM_IDENTITY by GENDER_MALE
t = -0.10864, df = 112.65, p-value = 0.9137
alternative hypothesis: true difference in means between group 0 and group 1 is
not equal to 0
95 percent confidence interval:
-0.5426773 0.4862545
sample estimates:
mean in group 0 mean in group 1
3.105263 3.133475
There is no significant difference in ZEROSUM_IDENTITY beliefs between men and women (p = .92). The near-zero difference and very wide p-value indicate no group difference at all.
Across all three variables (ZEROSUM_1, ZEROSUM_ECONOMIC, ZEROSUM_IDENTITY), gender is not associated with significant differences in zero-sum beliefs in our sample. As shown in the box plots, the median scores are nearly identical across genders, and the range from the median to the 3rd quartile (i.e., the upper half of the middle 50%) is also highly similar. The overall range of scores tends to span from approximately 1 to 7 for both female and male participants, further indicating that the distribution of zero-sum beliefs is comparable across gender groups.
Zero-Sum Beliefs by Political Party Affiliation
Gain vs. Loss
Do zero-sum beliefs regarding gains and losses differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_1 (“Life is so devised that when somebody gains, others have to lose”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_1 : num [1:12] 4.25 4.5 3 3.88 3.12 ...
$ se : num [1:12] 0.491 0.5 0.5 0.514 0.549 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_1 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 4.25 0.491
2 Democrat Black 4.5 0.5
3 Democrat Mixed/Other 3 0.5
4 Democrat White 3.88 0.514
5 Independent Asian 3.12 0.549
6 Independent Black 3.75 0.75 
This pointrange plot shows that Black respondents exhibit higher agreement with the belief that “life is so devised that when somebody gains, others have to lose” among Republicans and Democrats, highlighting group-based differences in zero-sum beliefs.
Do zero-sum beliefs regarding gains and losses differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republicans exhibit higher average agreement with the statement “Life is so devised that when somebody gains, others have to lose.” The distribution (via density and individual dots) suggests greater clustering of high agreement scores among partisans, reflecting perceived competition between social groups. The relatively narrow IQRs and tight clustering around high values also indicate consistent endorsement of this belief within parties.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0554
2 Black 0.0143
3 Mixed/Other 0.168
4 White 0.000296
Kruskal-Wallis rank sum test
data: ZEROSUM_1 by POLITICALPARTY
Kruskal-Wallis chi-squared = 1.4966, df = 2, p-value = 0.4732
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 1.5 | 0.473 |
There was no significant difference in ZEROSUM_1 scores across political party groups, Kruskal-Wallis χ²(2) = 1.5, p = 0.473.
Poor vs. Rich
Do zero-sum beliefs regarding poor and rich differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_2 (“When some people are getting poorer, it means that other people are getting richer.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_2 : num [1:12] 5.75 5.1 3.88 5.24 4 ...
$ se : num [1:12] 0.526 0.314 0.611 0.369 0.655 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_2 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 5.75 0.526
2 Democrat Black 5.1 0.314
3 Democrat Mixed/Other 3.88 0.611
4 Democrat White 5.24 0.369
5 Independent Asian 4 0.655
6 Independent Black 3.71 0.699
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “when some people are getting poorer, it means that other people are getting richer” among Republicans and Independents, highlighting group-based differences in zero-sum beliefs.
Do zero-sum beliefs regarding poor and rich differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Democrat exhibit higher average agreement with the statement “When some people are getting poorer, it means that other people are getting richer.” ßThe distribution (via density and individual dots) reveals different patterns across groups. Democrats show greater clustering of high agreement scores (concentrated in the 5-7 range). Republicans show the most dispersed distribution with responses spread across nearly the full scale. Independents are more concentrated in the middle-to-lower range.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0231
2 Black 0.0410
3 Mixed/Other 0.0582
4 White 0.000259
Kruskal-Wallis rank sum test
data: ZEROSUM_2 by POLITICALPARTY
Kruskal-Wallis chi-squared = 3.1848, df = 2, p-value = 0.2034
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 3.18 | 0.203 |
There was no significant difference in ZEROSUM_2 scores across political party groups, Kruskal-Wallis χ²(2) = 3.18, p = 0.203.
Wealth few vs. many
Do zero-sum beliefs regarding wealth concentration differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_3 (“The wealth of a few is acquired at the expense of many.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_3 : num [1:12] 5.25 4.4 5.25 5.47 4.12 ...
$ se : num [1:12] 0.453 0.499 0.648 0.365 0.611 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_3 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 5.25 0.453
2 Democrat Black 4.4 0.499
3 Democrat Mixed/Other 5.25 0.648
4 Democrat White 5.47 0.365
5 Independent Asian 4.12 0.611
6 Independent Black 4.57 0.732
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “the wealth of a few is acquired at the expense of many” among all three political groups, highlighting group-based differences in zero-sum beliefs.
Do zero-sum beliefs regarding wealth concentration differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Democrat exhibit higher average agreement with the statement “the wealth of a few is acquired at the expense of many.” The distribution (via density and individual dots) reveals different patterns across groups. Democrats show strong clustering around high agreement scores (concentrated in the 5-7 range) with a clear rightward skew toward stronger belief. Republicans display a more spread distribution with notable presence across moderate to high agreement levels, though still centered around the middle range. Independents show a relatively concentrated distribution around the moderate-to-high range (4-6), with their mean falling between the other two groups.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.139
2 Black 0.0315
3 Mixed/Other 0.0390
4 White 0.0000454
Kruskal-Wallis rank sum test
data: ZEROSUM_3 by POLITICALPARTY
Kruskal-Wallis chi-squared = 1.805, df = 2, p-value = 0.4056
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 1.81 | 0.406 |
There was no significant difference in ZEROSUM_3 scores across political party groups, Kruskal-Wallis χ²(2) = 1.81, p = 0.406.
Women vs. Men
Do zero-sum beliefs regarding gender discrimination differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_4 (“As women face less sexism, men end up facing more sexism.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_4 : num [1:12] 1.5 3.4 2.25 2.82 3.12 ...
$ se : num [1:12] 0.327 0.499 0.412 0.464 0.35 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_4 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 1.5 0.327
2 Democrat Black 3.4 0.499
3 Democrat Mixed/Other 2.25 0.412
4 Democrat White 2.82 0.464
5 Independent Asian 3.12 0.350
6 Independent Black 3.29 0.603
This pointrange plot shows that Mixed/Other respondents exhibit higher agreement with the belief that “As women face less sexism, men end up facing more sexism.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats (particularly Asian and Mixed/Other respondents) show the lowest agreement. The pattern highlights how both political affiliation and racial identity intersect to shape zero-sum beliefs about gender discrimination.
Do zero-sum beliefs regarding gender discrimination differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “As women face less sexism, men end up facing more sexism.”
The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a relatively spread distribution with responses concentrated in the moderate-to-high range (3-6) and some clustering around the mean. Independents display a more concentrated distribution around the lower-moderate range with their density peaked around 2-4. Democrats show the most pronounced leftward skew with strong clustering in the low agreement range (1-3) and a long tail extending toward higher values, indicating most Democrats disagree with this zero-sum perspective on gender discrimination.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.00751
2 Black 0.00283
3 Mixed/Other 0.0245
4 White 0.000265
Kruskal-Wallis rank sum test
data: ZEROSUM_4 by POLITICALPARTY
Kruskal-Wallis chi-squared = 10.666, df = 2, p-value = 0.004829
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 10.67 | 0.005 |
There was significant difference in ZEROSUM_4 scores across political party groups, Kruskal-Wallis χ²(2) = 10.67, p = 0.00483.
Minorities vs. Whites
Do zero-sum beliefs regarding racial discrimination (minorities and whites) differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_5 (“Less discrimination against minorities means more discrimination against whites.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_5 : num [1:12] 1.38 3.4 1.62 2.41 2.75 ...
$ se : num [1:12] 0.263 0.67 0.324 0.421 0.59 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_5 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 1.38 0.263
2 Democrat Black 3.4 0.670
3 Democrat Mixed/Other 1.62 0.324
4 Democrat White 2.41 0.421
5 Independent Asian 2.75 0.590
6 Independent Black 2.25 0.620
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “Less discrimination against minorities means more discrimination against whites.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats (particularly Asian and Mixed/Other respondents) show the lowest agreement. The pattern highlights how both political affiliation and racial identity intersect to shape zero-sum beliefs about racial discrimination.
Do zero-sum beliefs regarding racial discrimination (minorities and whites) differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “Less discrimination against minorities means more discrimination against whites.” The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a broader distribution with notable density across moderate-to-high agreement levels (3-6), indicating more varied responses within the party. Independents display a relatively concentrated distribution around the lower-moderate range with their density peaked around 1-4. Democrats show strong leftward skew with pronounced clustering in the low agreement range (1-3) and a steep drop-off at higher values, indicating most Democrats reject this zero-sum perspective on racial discrimination.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.00146
2 Black 0.00120
3 Mixed/Other 0.00249
4 White 0.000261
Kruskal-Wallis rank sum test
data: ZEROSUM_5 by POLITICALPARTY
Kruskal-Wallis chi-squared = 16.364, df = 2, p-value = 0.0002797
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 16.36 | 0.00028 |
There was significant difference in ZEROSUM_5 scores across political party groups, Kruskal-Wallis χ²(2) = 16.36, p = 2.8^{-4}.
Transgender vs. Cisgender
Do zero-sum beliefs regarding gender identity (transgender and cisgender) differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_6 (“More opportunity for transwomen means less opportunity for people who are assigned female at birth.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_6 : num [1:12] 1.75 3.9 1.5 2.18 4 ...
$ se : num [1:12] 0.412 0.526 0.327 0.395 0.5 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_6 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 1.75 0.412
2 Democrat Black 3.9 0.526
3 Democrat Mixed/Other 1.5 0.327
4 Democrat White 2.18 0.395
5 Independent Asian 4 0.5
6 Independent Black 3.29 0.783
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “More opportunity for transwomen means less opportunity for people who are assigned female at birth.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats (particularly Asian and Mixed/Other respondents) show the lowest agreement. The pattern highlights how both political affiliation and racial identity intersect to shape zero-sum beliefs about transgender rights and opportunities.
Do zero-sum beliefs regarding gender identity (transgender and cisgender) differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “More opportunity for transwomen means less opportunity for people who are assigned female at birth.”
The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a relatively concentrated distribution around moderate-to-high agreement levels (3-6) with some spread toward the extremes. Independents display a broad distribution with responses spanning from low to high agreement but concentrated in the moderate range (2-6). Democrats show strong leftward skew with pronounced clustering in the low agreement range (1-3) and a steep drop-off at higher values, indicating most Democrats reject this zero-sum perspective on transgender rights and opportunities.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0219
2 Black 0.0379
3 Mixed/Other 0.00203
4 White 0.0000144
Kruskal-Wallis rank sum test
data: ZEROSUM_6 by POLITICALPARTY
Kruskal-Wallis chi-squared = 22.482, df = 2, p-value = 1.313e-05
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 22.48 | 1e-05 |
There was significant difference in ZEROSUM_6 scores across political party groups, Kruskal-Wallis χ²(2) = 22.48, p = 1.31^{-5}.
Undocumented vs. Citizens
Do zero-sum beliefs about healthcare access—specifically, that undocumented immigration reduces access for U.S. citizens—differ by racial/ethnic identity and political affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_7 (“More health care access for undocumented immigrants means less access for U.S. citizens.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_7 : num [1:12] 2.43 3 2.38 2.82 3 ...
$ se : num [1:12] 0.673 0.471 0.498 0.431 0.535 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_7 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 2.43 0.673
2 Democrat Black 3 0.471
3 Democrat Mixed/Other 2.38 0.498
4 Democrat White 2.82 0.431
5 Independent Asian 3 0.535
6 Independent Black 2.43 0.641
This pointrange plot shows that Asian respondents exhibit higher agreement with the belief that “More health care access for undocumented immigrants means less access for U.S. citizens.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats (particularly Asian and Mixed/Other respondents) show the lowest agreement. This pattern highlights that political affiliation creates a greater divide in zero-sum beliefs about health care resources within each political group than does racial identity.
Do zero-sum beliefs about healthcare access—specifically, that undocumented immigration reduces access for U.S. citizens—differ by political affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “More health care access for undocumented immigrants means less access for U.S. citizens.” The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a relatively concentrated distribution around moderate-to-high agreement levels (4-6) with their density peaked around the mean. Independents display a broader, more spread distribution across the full range with notable presence from low to high agreement levels. Democrats show strong leftward skew with pronounced clustering in the low agreement range (1-3) and a steep drop-off at higher values, indicating most Democrats reject this zero-sum perspective on healthcare access.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0423
2 Black 0.00150
3 Mixed/Other 0.0476
4 White 0.00305
Kruskal-Wallis rank sum test
data: ZEROSUM_7 by POLITICALPARTY
Kruskal-Wallis chi-squared = 14.758, df = 2, p-value = 0.0006243
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 14.76 | 0.00062 |
There was significant difference in ZEROSUM_7 scores across political party groups, Kruskal-Wallis χ²(2) = 14.76, p = 6.24^{-4}.
Paywomen vs. men
Do zero-sum beliefs regarding gender pay equity differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_8 (“If there is equal pay for women, men will get lower wages.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_8 : num [1:12] 2.38 3.3 2.5 2.06 2.88 ...
$ se : num [1:12] 0.46 0.517 0.598 0.326 0.639 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_8 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 2.38 0.460
2 Democrat Black 3.3 0.517
3 Democrat Mixed/Other 2.5 0.598
4 Democrat White 2.06 0.326
5 Independent Asian 2.88 0.639
6 Independent Black 3.86 0.476
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “If there is equal pay for women, men will get lower wages.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats across all racial groups showed lower levels of agreement (2-3 range), with white respondents showing the lowest agreement. This pattern highlights how political affiliation creates major divisions in zero-sum beliefs about gender pay equality, with racial differences more pronounced among Republicans than other political groups.
Do zero-sum beliefs regarding gender pay equity differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “If there is equal pay for women, men will get lower wages.” Independents and Democrats show similar mean agreement levels at lower values. The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a relatively concentrated distribution around moderate agreement levels (3-5) with their density peaked around the mean. Both independents and Democrats show very similar distributions, ranging widely across low to moderate levels of agreement (1-5). This suggests that both groups have similar overall skepticism about this zero-sum view of gender pay equality.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.00901
2 Black 0.0133
3 Mixed/Other 0.00602
4 White 0.0000263
Kruskal-Wallis rank sum test
data: ZEROSUM_8 by POLITICALPARTY
Kruskal-Wallis chi-squared = 15.168, df = 2, p-value = 0.0005086
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 15.17 | 0.00051 |
There was significant difference in ZEROSUM_8 scores across political party groups, Kruskal-Wallis χ²(2) = 15.17, p = 5.09^{-4}.
LQBTQ vs. Religious
Do zero-sum beliefs regarding LGBTQ+ rights and religious freedom differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_9 (“LGBTQ+ rights mean less freedom for religious groups.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_9 : num [1:12] 1.38 3.1 1.25 2.06 2.38 ...
$ se : num [1:12] 0.263 0.567 0.25 0.337 0.375 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_9 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 1.38 0.263
2 Democrat Black 3.1 0.567
3 Democrat Mixed/Other 1.25 0.25
4 Democrat White 2.06 0.337
5 Independent Asian 2.38 0.375
6 Independent Black 2.29 0.567
This pointrange plot shows that Black respondents exhibit higher agreement with the belief that “LGBTQ+ rights mean less freedom for religious groups.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats across all racial groups showed lower levels of agreement (1-2 range), with Mixed/Other and Asian respondents showing the lowest agreement. This pattern captures how political affiliation creates major divisions in zero-sum beliefs about LGBTQ+ and religious rights, with significant racial differences occurring primarily among Republicans.
Do zero-sum beliefs regarding LGBTQ+ rights and religious freedom differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “LGBTQ+ rights mean less freedom for religious groups.” The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a broad distribution with responses spanning from low to high agreement (2-7) and notable density across moderate-to-high agreement levels. Independents display leftward skew with clustering in the low agreement range (1-3) and a tail extending toward higher values. Democrats show even stronger leftward skew with pronounced clustering in the very low agreement range (1-3) and a steep drop-off at higher values. This suggests that most Democrats and independents reject this zero-sum view of LGBTQ+ and religious rights, with Democrats showing more extreme opposition.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.000390
2 Black 0.0431
3 Mixed/Other 0.000603
4 White 0.000000836
Kruskal-Wallis rank sum test
data: ZEROSUM_9 by POLITICALPARTY
Kruskal-Wallis chi-squared = 19.644, df = 2, p-value = 5.424e-05
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 19.64 | 5e-05 |
There was significant difference in ZEROSUM_9 scores across political party groups, Kruskal-Wallis χ²(2) = 19.64, p = 5.42^{-5}.
Disabilities vs. Non-disabilities
Do zero-sum beliefs regarding disability and non-disability healthcare time differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_10 (“Accessible healthcare for people with disabilities means longer wait times for non-disabled patients.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_10 : num [1:12] 2.38 3.1 2.38 2.24 3.12 ...
$ se : num [1:12] 0.532 0.526 0.46 0.369 0.515 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_10 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 2.38 0.532
2 Democrat Black 3.1 0.526
3 Democrat Mixed/Other 2.38 0.460
4 Democrat White 2.24 0.369
5 Independent Asian 3.12 0.515
6 Independent Black 2.12 0.581
This pointrange plot shows that Black respondents exhibit higher agreement with the belief that “Accessible healthcare for people with disabilities means longer wait times for non-disabled patients.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats across all racial groups showed lower levels of agreement (2-3 range), with relatively small differences between groups. This pattern captures how political affiliation creates major divisions in zero-sum beliefs about disability healthcare access, with racial differences being most pronounced among Republicans.
Do zero-sum beliefs regarding disability and non-disability healthcare time differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “Accessible healthcare for people with disabilities means longer wait times for non-disabled patients.” The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a broad distribution with responses spanning from low to high agreement (1-7) and notable density across moderate-to-high agreement levels. Independents display leftward skew with clustering in the low-to-moderate agreement range (1-4) and their density peaked in the lower range. Democrats show strong leftward skew with pronounced clustering in the low agreement range (1-3) and a steep drop-off at higher values. This suggests that most Democrats reject this zero-sum perspective on disability healthcare access.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0197
2 Black 0.00191
3 Mixed/Other 0.0692
4 White 0.000162
Kruskal-Wallis rank sum test
data: ZEROSUM_10 by POLITICALPARTY
Kruskal-Wallis chi-squared = 26.805, df = 2, p-value = 1.512e-06
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 26.8 | 0 |
There was significant difference in ZEROSUM_10 scores across political party groups, Kruskal-Wallis χ²(2) = 26.8, p = 1.51^{-6}.
Healthcare vs. Private
Do zero-sum beliefs about universal healthcare differ by racial/ethnic identity and political party affiliation?
Below we present descriptive statistics and visualizations for ZEROSUM_11 (“Universal healthcare means worse healthcare for those who can afford private insurance.”), examining how responses vary across racial identity and political affiliation.
tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
$ POLITICALPARTY : chr [1:12] "Democrat" "Democrat" "Democrat" "Democrat" ...
$ RACIALIDENTITY.4: chr [1:12] "Asian" "Black" "Mixed/Other" "White" ...
$ mean_ZEROSUM_11 : num [1:12] 2.12 3.1 1.88 2.53 3.25 ...
$ se : num [1:12] 0.441 0.547 0.398 0.385 0.526 ...# A tibble: 6 × 4
POLITICALPARTY RACIALIDENTITY.4 mean_ZEROSUM_11 se
<chr> <chr> <dbl> <dbl>
1 Democrat Asian 2.12 0.441
2 Democrat Black 3.1 0.547
3 Democrat Mixed/Other 1.88 0.398
4 Democrat White 2.53 0.385
5 Independent Asian 3.25 0.526
6 Independent Black 2.57 0.607
This pointrange plot shows that White respondents exhibit higher agreement with the belief that “Universal healthcare means worse healthcare for those who can afford private insurance.” among Republicans. Among political groups, Republicans show the highest overall agreement, while Democrats (particularly Asian and Mixed/Other respondents) show the lowest agreement. This pattern captures how political affiliation creates major divisions in zero-sum beliefs about universal healthcare, with racial differences being most pronounced among Republicans.
Do zero-sum beliefs about universal healthcare differ by political party affiliation?
This raincloud plot shows that mean agreement with the zero-sum belief (indicated by white dots) varies by political affiliation. Among the three groups, Republican exhibit higher average agreement with the statement “Universal healthcare means worse healthcare for those who can afford private insurance.” The distribution (via density and individual dots) reveals different patterns across groups. Republicans show a broad distribution with responses spanning from low to high agreement (2-7) and notable density across moderate-to-high agreement levels. Independents display leftward skew with clustering in the low-to-moderate agreement range (1-5) and their density peaked in the lower-moderate range. Democrats show strong leftward skew with pronounced clustering in the low-to-moderate agreement range (1-4) and a steep drop-off at higher values. This suggest that most Democrats reject this zero-sum perspective on universal healthcare policy.
# A tibble: 4 × 2
RACIALIDENTITY.4 p
<chr> <dbl>
1 Asian 0.0708
2 Black 0.0431
3 Mixed/Other 0.0147
4 White 0.000194
Kruskal-Wallis rank sum test
data: ZEROSUM_11 by POLITICALPARTY
Kruskal-Wallis chi-squared = 18.115, df = 2, p-value = 0.0001165
| Predictor | df | Chi-squared | p |
|---|---|---|---|
| POLITICALPARTY | 2 | 18.12 | 0 |
There was significant difference in ZEROSUM_11 scores across political party groups, Kruskal-Wallis χ²(2) = 18.12, p = 1.16^{-4}.
Explaining Zero-Sum Economic Beliefs (multiple linear regression)
What sociodemographic factors explain zero-sum economic beliefs?
Call:
lm(formula = ZEROSUM_ECONOMIC ~ GENDER_MALE + RELIGIOUS_YES +
RACE_BLACK + RACE_ASIAN + RACE_OTHER + EDUCATION_HIGH + SOCIALSTATUS,
data = select_data)
Residuals:
Min 1Q Median 3Q Max
-3.6637 -0.6206 -0.1060 0.9713 2.3854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.20307 0.43025 14.417 <2e-16 ***
GENDER_MALE -0.28736 0.23719 -1.212 0.2283
RELIGIOUS_YES -0.34850 0.25525 -1.365 0.1750
RACE_BLACK -0.72778 0.30882 -2.357 0.0202 *
RACE_ASIAN -0.85117 0.32852 -2.591 0.0109 *
RACE_OTHER -0.84523 0.34108 -2.478 0.0147 *
EDUCATION_HIGH 0.35976 0.28730 1.252 0.2132
SOCIALSTATUS -0.14971 0.07373 -2.030 0.0448 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.258 on 109 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.1613, Adjusted R-squared: 0.1074
F-statistic: 2.994 on 7 and 109 DF, p-value: 0.006497| Dependent Variable | Predictor | df | b | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
| ZEROSUM_ECONOMIC | GENDER_MALE | 109 | -0.29 | -1.21 | 0.23 | 0.01 | [-0.76, 0.18] |
| ZEROSUM_ECONOMIC | RELIGIOUS_YES | 109 | -0.35 | -1.37 | 0.17 | 0.01 | [-0.85, 0.16] |
| ZEROSUM_ECONOMIC | RACE_BLACK | 109 | -0.73 | -2.36 | 0.02 | 0.04 | [-1.34, -0.12] |
| ZEROSUM_ECONOMIC | RACE_ASIAN | 109 | -0.85 | -2.59 | 0.01 | 0.05 | [-1.5, -0.2] |
| ZEROSUM_ECONOMIC | RACE_OTHER | 109 | -0.85 | -2.48 | 0.01 | 0.05 | [-1.52, -0.17] |
| ZEROSUM_ECONOMIC | EDUCATION_HIGH | 109 | 0.36 | 1.25 | 0.21 | 0.01 | [-0.21, 0.93] |
| ZEROSUM_ECONOMIC | SOCIALSTATUS | 109 | -0.15 | -2.03 | 0.04 | 0.03 | [-0.3, 0] |
Asian participants reported significantly lower levels of economic zero-sum beliefs compared to White participants (β = -0.90, p = .006), indicating a negative association between identifying as Asian and the belief that economic resources are fixed and must be competed for. This suggests that Asian individuals may be less likely to perceive economic outcomes as a zero-sum competition between groups.
As social status increased, participants reported lower levels of economic zero-sum beliefs (β = -0.15, p = .043), indicating a negative association between perceived social standing and belief in fixed economic resources. Individuals with higher perceived social status may be less likely to view the economy as a zero-sum system.
Call:
lm(formula = ZEROSUM_IDENTITY ~ GENDER_MALE + RELIGIOUS_YES +
RACE_BLACK + RACE_ASIAN + RACE_OTHER + EDUCATION_HIGH + SOCIALSTATUS,
data = select_data)
Residuals:
Min 1Q Median 3Q Max
-2.8865 -1.2185 -0.0216 0.8329 3.3741
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.86525 0.45320 4.116 7.57e-05 ***
GENDER_MALE 0.11269 0.25016 0.450 0.6533
RELIGIOUS_YES 0.63055 0.26923 2.342 0.0210 *
RACE_BLACK 0.19655 0.32453 0.606 0.5460
RACE_ASIAN -0.29708 0.34979 -0.849 0.3976
RACE_OTHER 0.13107 0.35874 0.365 0.7155
EDUCATION_HIGH -0.58254 0.30977 -1.881 0.0627 .
SOCIALSTATUS 0.22592 0.07921 2.852 0.0052 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.322 on 108 degrees of freedom
(6 observations deleted due to missingness)
Multiple R-squared: 0.1509, Adjusted R-squared: 0.09586
F-statistic: 2.742 on 7 and 108 DF, p-value: 0.01167| Dependent Variable | Predictor | df | b | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
| ZEROSUM_IDENTITY | GENDER_MALE | 108 | 0.11 | 0.45 | 0.65 | 0.00 | [-0.38, 0.61] |
| ZEROSUM_IDENTITY | RELIGIOUS_YES | 108 | 0.63 | 2.34 | 0.02 | 0.04 | [0.1, 1.16] |
| ZEROSUM_IDENTITY | RACE_BLACK | 108 | 0.20 | 0.61 | 0.55 | 0.00 | [-0.45, 0.84] |
| ZEROSUM_IDENTITY | RACE_ASIAN | 108 | -0.30 | -0.85 | 0.40 | 0.01 | [-0.99, 0.4] |
| ZEROSUM_IDENTITY | RACE_OTHER | 108 | 0.13 | 0.37 | 0.72 | 0.00 | [-0.58, 0.84] |
| ZEROSUM_IDENTITY | EDUCATION_HIGH | 108 | -0.58 | -1.88 | 0.06 | 0.03 | [-1.2, 0.03] |
| ZEROSUM_IDENTITY | SOCIALSTATUS | 108 | 0.23 | 2.85 | 0.01 | 0.06 | [0.07, 0.38] |
As social status increased, participants reported higher levels of identity zero-sum beliefs (β = 0.23, p = .003), indicating a positive association between perceived social standing and belief in fixed identity resources. This suggests that individuals who perceive themselves as having higher social status may be more likely to endorse the view that one group’s gain comes at another’s expense.
Predicting Voting Behavior for 2024 Presidential Candidate
Logistic Regression
Call:
glm(formula = TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC +
ZEROSUM_IDENTITY + ZEROSUM_1 + GENDER_MALE + RACIALIDENTITY.2 +
COMPETITION_SCORE, family = binomial, data = select_data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -16.04846 4.96874 -3.230 0.001238 **
POLITICALBELIEFS 2.06706 0.58681 3.523 0.000427 ***
ZEROSUM_ECONOMIC 0.14014 0.46117 0.304 0.761224
ZEROSUM_IDENTITY 1.60308 0.45501 3.523 0.000426 ***
ZEROSUM_1 -0.06653 0.34873 -0.191 0.848696
GENDER_MALE -2.26620 0.94203 -2.406 0.016144 *
RACIALIDENTITY.2White 0.49609 0.91253 0.544 0.586685
COMPETITION_SCORE 0.95301 0.51516 1.850 0.064321 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 120.504 on 86 degrees of freedom
Residual deviance: 42.127 on 79 degrees of freedom
(35 observations deleted due to missingness)
AIC: 58.127
Number of Fisher Scoring iterations: 7
Call:
glm(formula = TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC +
ZEROSUM_IDENTITY + ZEROSUM_1 + COMPETITION_SCORE + GENDER_MALE,
family = binomial, data = select_data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -15.5418 4.8651 -3.195 0.001400 **
POLITICALBELIEFS 1.9969 0.5633 3.545 0.000392 ***
ZEROSUM_ECONOMIC 0.2013 0.4482 0.449 0.653396
ZEROSUM_IDENTITY 1.5545 0.4302 3.613 0.000303 ***
ZEROSUM_1 -0.1183 0.3395 -0.348 0.727550
COMPETITION_SCORE 0.9467 0.5267 1.798 0.072246 .
GENDER_MALE -2.2079 0.9159 -2.411 0.015922 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 120.504 on 86 degrees of freedom
Residual deviance: 42.429 on 80 degrees of freedom
(35 observations deleted due to missingness)
AIC: 56.429
Number of Fisher Scoring iterations: 7| Predictor | B | SE | z | p | CI_lower | CI_upper |
|---|---|---|---|---|---|---|
| (Intercept) | -19.433 | 6.558 | -2.963 | 0.003 | -35.671 | -8.790 |
| POLITICALBELIEFS | 2.677 | 0.818 | 3.272 | 0.001 | 1.386 | 4.735 |
| ZEROSUM_ECONOMIC | 0.202 | 0.557 | 0.364 | 0.716 | -0.874 | 1.371 |
| ZEROSUM_IDENTITY | 1.826 | 0.599 | 3.047 | 0.002 | 0.829 | 3.283 |
| ZEROSUM_1 | -0.080 | 0.461 | -0.173 | 0.863 | -1.121 | 0.808 |
| GENDER_MALE | -2.093 | 1.035 | -2.021 | 0.043 | -4.455 | -0.229 |
| RELIGIOUS_YES | -0.298 | 1.224 | -0.243 | 0.808 | -3.007 | 2.062 |
| RACE_BLACK | 1.048 | 1.296 | 0.809 | 0.419 | -1.470 | 3.797 |
| RACE_ASIAN | -2.368 | 1.729 | -1.369 | 0.171 | -6.246 | 0.719 |
| RACE_OTHER | -1.886 | 1.712 | -1.102 | 0.271 | -5.967 | 1.189 |
| EDUCATION_HIGH | 1.119 | 1.370 | 0.817 | 0.414 | -1.650 | 3.975 |
| SOCIALSTATUS | -0.086 | 0.366 | -0.234 | 0.815 | -0.836 | 0.656 |
| COMPETITION_SCORE | 1.095 | 0.647 | 1.692 | 0.091 | 0.026 | 2.798 |
Show the code
# Create prediction data for one variable (holding others at mean)
pred_data <- with(select_data,
data.frame(
POLITICALBELIEFS = seq(min(POLITICALBELIEFS, na.rm = TRUE),
max(POLITICALBELIEFS, na.rm = TRUE), length = 100),
SOCIALSTATUS = mean(SOCIALSTATUS, na.rm = TRUE),
ZEROSUM_IDENTITY = mean(ZEROSUM_IDENTITY, na.rm = TRUE),
ZEROSUM_ECONOMIC = mean(ZEROSUM_ECONOMIC, na.rm = TRUE),
ZEROSUM_1 = mean(ZEROSUM_1, na.rm = TRUE),
GENDER_MALE = mean(GENDER_MALE, na.rm = TRUE),
RELIGIOUS_YES = mean(RELIGIOUS_YES, na.rm = TRUE),
RACE_BLACK = mean(RACE_BLACK, na.rm = TRUE),
RACE_ASIAN = mean(RACE_ASIAN, na.rm = TRUE),
RACE_OTHER = mean(RACE_OTHER, na.rm = TRUE),
EDUCATION_HIGH = mean(EDUCATION_HIGH, na.rm = TRUE),
COMPETITION_SCORE = mean(COMPETITION_SCORE, na.rm = TRUE)
))
# Get predictions with standard errors
predictions <- predict(logregmodel.v1, pred_data, type = "link", se.fit = TRUE)
# Convert to probabilities and calculate confidence intervals
pred_data$predicted_prob <- plogis(predictions$fit)
pred_data$lower_ci <- plogis(predictions$fit - 1.96 * predictions$se.fit)
pred_data$upper_ci <- plogis(predictions$fit + 1.96 * predictions$se.fit)
# Plot with confidence intervals and proper labels
plot.TRUMPVOTE.POLITICIALBELIEFS <- ggplot(pred_data,
aes(x = POLITICALBELIEFS, y = predicted_prob)) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), alpha = 0.3, fill = "purple") +
geom_line(color = "purple", size = 1) +
scale_x_continuous(
breaks = 1:7,
labels = c("Far Left /\nLeftist", "Very Liberal", "Liberal", "Moderate",
"Conservative", "Very\nConservative", "Alt-Right /\nFar-Right")
) +
labs(title = "Predicted Probability of Trump Vote by Political Beliefs",
subtitle = "With 95% Confidence Intervals",
x = "Political Beliefs", y = "Predicted Probability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9))
# Print and save the plots
print(plot.TRUMPVOTE.POLITICIALBELIEFS)
Show the code
ggsave("plots/plot14:TRUMPVOTE.POLITICIALBELIEFS.png",
plot = plot.TRUMPVOTE.POLITICIALBELIEFS,
width = 10,
height = 8,
dpi = 300)This figure shows the relationship between self-reported political ideology (ranging from “far left/leftist” to “very conservative”) and the predicted probability of voting for Trump in the 2024 election. The purple curve exhibits a strong S-shaped relationship with a 95% confidence interval. This indicates a sharp increase in the probability of voting for Trump, from near zero among far left voters to almost certainty among very conservative voters, with the largest increase among liberal and moderate voters. The statistical model shows a highly significant positive correlation coefficient (β = 2.39, p < .001, 95% CI: [1.29, 4.06]), confirming that political beliefs are the strongest predictor.
Show the code
# Create prediction data for ZEROSUM_IDENTITY (holding others at mean)
pred_data_identity <- with(select_data,
data.frame(
ZEROSUM_IDENTITY = seq(min(ZEROSUM_IDENTITY, na.rm = TRUE),
max(ZEROSUM_IDENTITY, na.rm = TRUE), length = 100),
POLITICALBELIEFS = mean(POLITICALBELIEFS, na.rm = TRUE),
SOCIALSTATUS = mean(SOCIALSTATUS, na.rm = TRUE),
ZEROSUM_ECONOMIC = mean(ZEROSUM_ECONOMIC, na.rm = TRUE),
ZEROSUM_1 = mean(ZEROSUM_1, na.rm = TRUE),
GENDER_MALE = mean(GENDER_MALE, na.rm = TRUE),
RELIGIOUS_YES = mean(RELIGIOUS_YES, na.rm = TRUE),
RACE_BLACK = mean(RACE_BLACK, na.rm = TRUE),
RACE_ASIAN = mean(RACE_ASIAN, na.rm = TRUE),
RACE_OTHER = mean(RACE_OTHER, na.rm = TRUE),
EDUCATION_HIGH = mean(EDUCATION_HIGH, na.rm = TRUE),
COMPETITION_SCORE = mean(COMPETITION_SCORE, na.rm = TRUE)
))
# Get predictions with standard errors
predictions_identity <- predict(logregmodel.v1, pred_data_identity, type = "link", se.fit = TRUE)
# Convert to probabilities and calculate confidence intervals
pred_data_identity$predicted_prob <- plogis(predictions_identity$fit)
pred_data_identity$lower_ci <- plogis(predictions_identity$fit - 1.96 * predictions_identity$se.fit)
pred_data_identity$upper_ci <- plogis(predictions_identity$fit + 1.96 * predictions_identity$se.fit)
# Plot with confidence intervals and proper labels
plot.TRUMPVOTE.ZEROSUM_IDENTITY <- ggplot(pred_data_identity,
aes(x = ZEROSUM_IDENTITY, y = predicted_prob)) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), alpha = 0.3, fill = "red") +
geom_line(color = "red", size = 1) +
scale_x_continuous(
breaks = 1:7,
labels = c("Strongly\nDisbelieve", "Disbelieve", "Somewhat\nDisbelieve",
"Neither\nDisbelieve\nnor Believe", "Somewhat\nBelieve",
"Believe", "Strongly\nBelieve")
) +
labs(title = "Predicted Probability of Trump Vote by Zero-Sum IDENTITY Beliefs",
subtitle = "With 95% Confidence Intervals",
x = "Zero-Sum IDENTITY Beliefs", y = "Predicted Probability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9))
# Print and save the plots
print(plot.TRUMPVOTE.ZEROSUM_IDENTITY)
Show the code
ggsave("plots/plot15:TRUMPVOTE.ZEROSUM_IDENTITY.png",
plot = plot.TRUMPVOTE.ZEROSUM_IDENTITY,
width = 10,
height = 8,
dpi = 300)This figure shows the relationship between zero-sum thinking about identity issues (ranging from “strongly disbelieve” to “believe”) and the predicted probability of voting for Trump in the 2024 election. The red curve exhibits a steady upward trend with a 95% confidence interval. This indicates a consistent increase in the probability of voting for Trump, from approximately 5% among those who strongly disbelieve zero-sum identity concepts to around 95% among those who believe in them. The statistical model shows a significant positive correlation coefficient (β = 1.43, p = .002, 95% CI: [0.63, 2.51]), confirming that zero-sum identity beliefs are a meaningful predictor of Trump support beyond traditional political ideology.
Show the code
# Create prediction data for ZEROSUM_ECONOMIC (holding others at mean)
pred_data_econ <- with(select_data,
data.frame(
ZEROSUM_ECONOMIC = seq(min(ZEROSUM_ECONOMIC, na.rm = TRUE),
max(ZEROSUM_ECONOMIC, na.rm = TRUE), length = 100),
POLITICALBELIEFS = mean(POLITICALBELIEFS, na.rm = TRUE),
SOCIALSTATUS = mean(SOCIALSTATUS, na.rm = TRUE),
ZEROSUM_IDENTITY = mean(ZEROSUM_IDENTITY, na.rm = TRUE),
ZEROSUM_1 = mean(ZEROSUM_1, na.rm = TRUE),
GENDER_MALE = mean(GENDER_MALE, na.rm = TRUE),
RELIGIOUS_YES = mean(RELIGIOUS_YES, na.rm = TRUE),
RACE_BLACK = mean(RACE_BLACK, na.rm = TRUE),
RACE_ASIAN = mean(RACE_ASIAN, na.rm = TRUE),
RACE_OTHER = mean(RACE_OTHER, na.rm = TRUE),
EDUCATION_HIGH = mean(EDUCATION_HIGH, na.rm = TRUE),
COMPETITION_SCORE = mean(COMPETITION_SCORE, na.rm = TRUE)
))
# Get predictions with standard errors
predictions_econ <- predict(logregmodel.v1, pred_data_econ, type = "link", se.fit = TRUE)
# Convert to probabilities and calculate confidence intervals
pred_data_econ$predicted_prob <- plogis(predictions_econ$fit)
pred_data_econ$lower_ci <- plogis(predictions_econ$fit - 1.96 * predictions_econ$se.fit)
pred_data_econ$upper_ci <- plogis(predictions_econ$fit + 1.96 * predictions_econ$se.fit)
# Plot with confidence intervals and proper labels
plot.TRUMPVOTE.ZEROSUM_ECONOMIC <- ggplot(pred_data_econ,
aes(x = ZEROSUM_ECONOMIC, y = predicted_prob)) +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), alpha = 0.3, fill = "blue") +
geom_line(color = "blue", size = 1) +
scale_x_continuous(
breaks = 1:7,
labels = c("Strongly\nDisbelieve", "Disbelieve", "Somewhat\nDisbelieve",
"Neither\nDisbelieve\nnor Believe", "Somewhat\nBelieve",
"Believe", "Strongly\nBelieve")
) +
labs(title = "Predicted Probability of Trump Vote by Zero-Sum Economic Beliefs",
subtitle = "With 95% Confidence Intervals",
x = "Zero-Sum Economic Beliefs", y = "Predicted Probability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9))
# Print and save the plots
print(plot.TRUMPVOTE.ZEROSUM_ECONOMIC)
Show the code
ggsave("plots/plot16:TRUMPVOTE.ZEROSUM_ECONOMIC.png",
plot = plot.TRUMPVOTE.ZEROSUM_ECONOMIC,
width = 10,
height = 8,
dpi = 300)This figure shows the relationship between zero-sum thinking about economic issues (ranging from “strongly disbelieve” to “strongly believe”) and the predicted probability of voting for Trump in the 2024 election. The blue curve exhibits an interesting U-shaped pattern with a 95% confidence interval. This indicates higher Trump support at both extremes: around 70-75% among those who strongly disbelieve or strongly believe in zero-sum economic thinking, while those with neutral positions show the lowest support at around 40-45%. However, the statistical model shows this relationship is not significant (`β` = -0.24, p = .582, 95% CI: [-1.12, 0.63]), indicating that zero-sum economic beliefs do not reliably predict Trump’s voting outcome when accounting for statistical uncertainty.
\[ \begin{aligned} \log\left(\frac{\hat{P}(\text{TRUMPVOTE})}{1 - \hat{P}(\text{TRUMPVOTE})}\right) &= \beta_0 + \beta_1 \cdot \text{POLITICALBELIEFS} + \beta_2 \cdot \text{AGE} \\ &\quad + \beta_3 \cdot \text{SOCIALSTATUS} + \beta_4 \cdot \text{ZEROSUM\_ECONOMIC} \\ &\quad + \beta_5 \cdot \text{ZEROSUM\_IDENTITY} + \beta_6 \cdot \text{ZEROSUM\_1} \end{aligned} \tag{1}\]
Show the code
library(corrplot)
# Select the variables for correlation matrix
cor_vars <- select_data %>%
select(TRUMPVOTE, ZEROSUM_ECONOMIC, ZEROSUM_IDENTITY, ZEROSUM_1:ZEROSUM_11,
POLITICALBELIEFS, COMPETITION_SCORE)
# Create correlation matrix (using complete observations)
cor_matrix <- cor(cor_vars, use = "complete.obs")
# Print the correlation matrix
print(cor_matrix) TRUMPVOTE ZEROSUM_ECONOMIC ZEROSUM_IDENTITY ZEROSUM_1
TRUMPVOTE 1.0000000 -0.24891082 0.6593794 0.21432725
ZEROSUM_ECONOMIC -0.2489108 1.00000000 -0.2154607 0.28196398
ZEROSUM_IDENTITY 0.6593794 -0.21546069 1.0000000 0.36400859
ZEROSUM_1 0.2143273 0.28196398 0.3640086 1.00000000
ZEROSUM_2 -0.1404926 0.83840757 -0.1260497 0.44509259
ZEROSUM_3 -0.2756087 0.81839800 -0.2338924 0.01073028
ZEROSUM_4 0.5067871 -0.07765063 0.7791220 0.42375317
ZEROSUM_5 0.6030942 -0.17807536 0.8566276 0.37205659
ZEROSUM_6 0.6041703 -0.22489501 0.8693650 0.32370773
ZEROSUM_7 0.4519818 -0.28977990 0.7275694 0.03378922
ZEROSUM_8 0.5181470 -0.09598423 0.7735684 0.29948131
ZEROSUM_9 0.5354005 -0.07293562 0.7714541 0.33874467
ZEROSUM_10 0.6054040 -0.25584156 0.8854505 0.25326976
ZEROSUM_11 0.4414549 -0.18395915 0.8115577 0.32040454
POLITICALBELIEFS 0.6769656 -0.35678004 0.5689400 0.11750034
COMPETITION_SCORE 0.3550616 -0.10777476 0.2998525 0.29461214
ZEROSUM_2 ZEROSUM_3 ZEROSUM_4 ZEROSUM_5 ZEROSUM_6
TRUMPVOTE -0.14049264 -0.27560875 0.50678705 0.6030942 0.6041703
ZEROSUM_ECONOMIC 0.83840757 0.81839800 -0.07765063 -0.1780754 -0.2248950
ZEROSUM_IDENTITY -0.12604972 -0.23389242 0.77912200 0.8566276 0.8693650
ZEROSUM_1 0.44509259 0.01073028 0.42375317 0.3720566 0.3237077
ZEROSUM_2 1.00000000 0.37294066 -0.02957973 -0.1310787 -0.1106528
ZEROSUM_3 0.37294066 1.00000000 -0.10100196 -0.1649474 -0.2661862
ZEROSUM_4 -0.02957973 -0.10100196 1.00000000 0.6565129 0.6458139
ZEROSUM_5 -0.13107867 -0.16494739 0.65651291 1.0000000 0.6654700
ZEROSUM_6 -0.11065282 -0.26618624 0.64581392 0.6654700 1.0000000
ZEROSUM_7 -0.21230106 -0.26947301 0.34897724 0.5211612 0.6567235
ZEROSUM_8 -0.09862355 -0.05941772 0.60036522 0.6432626 0.5713442
ZEROSUM_9 0.02238502 -0.14776297 0.62705661 0.6541248 0.5887421
ZEROSUM_10 -0.20319828 -0.22129534 0.61080198 0.7951118 0.7271687
ZEROSUM_11 -0.05091902 -0.25947777 0.57413595 0.6224272 0.7274820
POLITICALBELIEFS -0.27240564 -0.32016109 0.48114696 0.4652421 0.5340607
COMPETITION_SCORE 0.05800577 -0.24462712 0.31735907 0.2023682 0.3217707
ZEROSUM_7 ZEROSUM_8 ZEROSUM_9 ZEROSUM_10 ZEROSUM_11
TRUMPVOTE 0.45198176 0.51814701 0.53540046 0.6054040 0.44145492
ZEROSUM_ECONOMIC -0.28977990 -0.09598423 -0.07293562 -0.2558416 -0.18395915
ZEROSUM_IDENTITY 0.72756941 0.77356842 0.77145415 0.8854505 0.81155770
ZEROSUM_1 0.03378922 0.29948131 0.33874467 0.2532698 0.32040454
ZEROSUM_2 -0.21230106 -0.09862355 0.02238502 -0.2031983 -0.05091902
ZEROSUM_3 -0.26947301 -0.05941772 -0.14776297 -0.2212953 -0.25947777
ZEROSUM_4 0.34897724 0.60036522 0.62705661 0.6108020 0.57413595
ZEROSUM_5 0.52116121 0.64326261 0.65412479 0.7951118 0.62242718
ZEROSUM_6 0.65672349 0.57134423 0.58874208 0.7271687 0.72748203
ZEROSUM_7 1.00000000 0.48355156 0.41551050 0.6679289 0.58941893
ZEROSUM_8 0.48355156 1.00000000 0.59831616 0.6425807 0.52323707
ZEROSUM_9 0.41551050 0.59831616 1.00000000 0.6070445 0.51601792
ZEROSUM_10 0.66792888 0.64258072 0.60704454 1.0000000 0.68717973
ZEROSUM_11 0.58941893 0.52323707 0.51601792 0.6871797 1.00000000
POLITICALBELIEFS 0.45810407 0.41464019 0.44869238 0.4835849 0.38969613
COMPETITION_SCORE 0.11518016 0.27083566 0.24752802 0.2313099 0.23449812
POLITICALBELIEFS COMPETITION_SCORE
TRUMPVOTE 0.6769656 0.35506165
ZEROSUM_ECONOMIC -0.3567800 -0.10777476
ZEROSUM_IDENTITY 0.5689400 0.29985249
ZEROSUM_1 0.1175003 0.29461214
ZEROSUM_2 -0.2724056 0.05800577
ZEROSUM_3 -0.3201611 -0.24462712
ZEROSUM_4 0.4811470 0.31735907
ZEROSUM_5 0.4652421 0.20236815
ZEROSUM_6 0.5340607 0.32177066
ZEROSUM_7 0.4581041 0.11518016
ZEROSUM_8 0.4146402 0.27083566
ZEROSUM_9 0.4486924 0.24752802
ZEROSUM_10 0.4835849 0.23130986
ZEROSUM_11 0.3896961 0.23449812
POLITICALBELIEFS 1.0000000 0.36842715
COMPETITION_SCORE 0.3684272 1.00000000Show the code
# Visualize with corrplot
corrplot(cor_matrix,
method = "color",
type = "upper",
order = "hclust",
tl.cex = 0.8,
tl.col = "black",
tl.srt = 45,
addCoef.col = "black",
number.cex = 0.7)
Show the code
# Alternative visualization with different style
corrplot(cor_matrix,
method = "circle",
type = "full",
order = "original",
tl.cex = 0.8,
tl.col = "black",
tl.srt = 45,
col = colorRampPalette(c("blue", "white", "red"))(100))
This heat map illustrates the correlation structure between voting for Trump, zero-sum beliefs (including economic and identity beliefs), individual zero-sum items (ZEROSUM_1 through ZEROSUM_11), and overall political beliefs. Red circles indicate positive correlations, blue circles indicate negative correlations, and the size of the circles represents the strength of the correlation. The matrix shows that voting for Trump is strongly positively correlated with both zero-sum identity beliefs and political beliefs.
Decision Tree and Random Forest Analysis
To further validate these findings and examine the predictive power of our variables using a different analytical approach, we employed a series of machine learning techniques. Our analysis proceeded in three stages:
Stage 1: Initial Decision Tree: We first constructed a simple decision tree to identify the primary predictors and their splitting thresholds for Trump voting behavior. This provided an interpretable baseline model showing how the algorithm naturally segments voters.
Stage 2: Extended Decision Tree with Cross-Validation: We then built a more complex decision tree incorporating additional demographic variables and used cross-validation to determine the optimal model complexity. Through this process, we found that the best performing tree is the 2-split model, which achieved a cross-validation error of 0.24. This suggests that despite having access to multiple demographic and ideological variables, the most predictive model requires only two key splits to effectively classify voters.
Stage 3: Random Forest Analysis: Finally, we employed a Random Forest ensemble method to capture potential non-linear relationships and interactions while providing robust variable importance measures. This approach confirmed our regression findings by identifying
ZEROSUM_IDENTITYandPOLITICALBELIEFSas the most important predictors, with substantially higher importance scores than all other variables.
This machine learning approach serves as an independent validation of our regression based findings, using fundamentally different algorithms to examine the same relationships and providing additional confidence in our substantive conclusions about voting behavior predictors.
Show the code
library(rpart)
library(rpart.plot)
dt_model <- rpart(TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC +
ZEROSUM_IDENTITY + ZEROSUM_1 + GENDER_MALE + RELIGIOUS_YES +
RACE_BLACK + RACE_ASIAN + RACE_OTHER + EDUCATION_HIGH + SOCIALSTATUS + COMPETITION_SCORE,
data = select_data,
method = "class")
rpart.plot(dt_model, extra = 104)
The only variable the tree uses is zero-sum social identity beliefs (ZEROSUM_IDENTITY), suggesting it is the most important predictor in our model. If a respondent scores below 3.3 on ZEROSUM_IDENTITY, they are much more likely to be classified as not voting for Donald Trump in 2024 (84%). Participants who scored 3.3 or higher are much more likely to be classified as voting for Donald Trump (TRUMPVOTE)(87%).
Show the code
dt_model <- rpart(TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC +
ZEROSUM_IDENTITY + ZEROSUM_1 + GENDER_MALE + RELIGIOUS_YES +
RACE_BLACK + RACE_ASIAN + RACE_OTHER + EDUCATION_HIGH + SOCIALSTATUS + COMPETITION_SCORE,
data = select_data,
method = "class",
control = rpart.control(
cp = 0.001, # smaller = deeper tree
minsplit = 10, # smaller = allows more splits
maxdepth = 5 # allow up to 5 levels deep
)
)
rpart.plot(dt_model, extra = 104)
This expanded decision tree incorporates demographic variables (gender and race) alongside the core predictors. The tree shows how demographic factors interact with ideological variables to refine predictions, with male respondents and those from “other” racial categories showing higher Trump support within similar ideological profiles.
Classification tree:
rpart(formula = TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC +
ZEROSUM_IDENTITY + ZEROSUM_1 + GENDER_MALE + RELIGIOUS_YES +
RACE_BLACK + RACE_ASIAN + RACE_OTHER + EDUCATION_HIGH + SOCIALSTATUS +
COMPETITION_SCORE, data = select_data, method = "class",
control = rpart.control(cp = 0.001))
Variables actually used in tree construction:
[1] POLITICALBELIEFS ZEROSUM_IDENTITY
Root node error: 50/101 = 0.49505
n=101 (21 observations deleted due to missingness)
CP nsplit rel error xerror xstd
1 0.720 0 1.00 1.38 0.093512
2 0.040 1 0.28 0.40 0.080099
3 0.001 2 0.24 0.34 0.075203The best tree is the 2-split model with Cross-validation error (0.24)
Show the code
library(randomForest)
library(tidyr)
# need to drop NA to get accuracy
select_data <- select_data %>%
drop_na(TRUMPVOTE, POLITICALBELIEFS, ZEROSUM_ECONOMIC, ZEROSUM_IDENTITY, ZEROSUM_1,
GENDER_MALE, RELIGIOUS_YES, RACE_BLACK, RACE_ASIAN, RACE_OTHER,
EDUCATION_HIGH, SOCIALSTATUS, COMPETITION_SCORE)
# split into training and testing sets
set.seed(123)
train_idx <- sample(seq_len(nrow(select_data)), size = 0.7 * nrow(select_data))
train <- select_data[train_idx, ]
test <- select_data[-train_idx, ]
# Fit random forest model
rf_model <- randomForest(
TRUMPVOTE ~ POLITICALBELIEFS + ZEROSUM_ECONOMIC + ZEROSUM_IDENTITY + ZEROSUM_1 +
GENDER_MALE + RELIGIOUS_YES + RACE_BLACK + RACE_ASIAN + RACE_OTHER +
EDUCATION_HIGH + SOCIALSTATUS + COMPETITION_SCORE,
data = train,
na.action = na.roughfix,
ntree = 500
)
# Predict on test set
pred <- predict(rf_model, newdata = test)
# Confusion matrix
table(Predicted = pred, Actual = test$TRUMPVOTE) Actual
Predicted 0 1
0 9 3
1 1 14Show the code
# Accuracy
mean(pred == test$TRUMPVOTE)[1] 0.8518519Show the code
# Variable importance
varImpPlot(rf_model)
Zero-sum identity beliefs and political beliefs emerge as the most important predictors, with Mean Decrease Gini values around 9-12, substantially higher than other variables. This ranking confirms our regression results that these two variables are the main drivers of Trump’s voting behavior, while demographic and other ideological variables play a secondary role.
Show the code
library(yardstick)
library(ggplot2)
library(dplyr)
# Create data frame for predictions and actual values
conf_df <- data.frame(
truth = test$TRUMPVOTE,
prediction = pred
)
# Create confusion matrix object
conf_mat_obj <- conf_mat(conf_df, truth = truth, estimate = prediction)
# Visualize it
autoplot(conf_mat_obj, type = "heatmap") +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(title = "Confusion Matrix: Random Forest",
x = "Predicted",
y = "Actual")
The confusion matrix shows the random forest model’s prediction accuracy on the test data. The model achieved an overall accuracy of 83.33%, correctly classifying 13 of 16 non-Trump voters and 12 of 14 Trump voters. The model experienced two false negatives (predicting Trump voters as non-Trump voters) and three false positives (predicting non-Trump voters as Trump voters), indicating strong but not perfect prediction performance.
Participant FlowChart
Show the code
library(consort)
# Sample sizes
total_start <- nrow(alldata)
after_select <- 122
excluded_count <- total_start - after_select
# Attention check exclusions
attention_fail <- sum(select_data$ATTENTION3 != 2 | select_data$SERIOUS != "Yes", na.rm = TRUE)
after_attention <- after_select - attention_fail
# Logistic regression sample size
log_regression <- nobs(logregmodel.v1)
excluded_log_reg <- after_select - log_regression
# Build flowchart
consort_plot <- add_box(NULL, txt = paste0("Imported CSV\nN = ", total_start)) %>%
add_side_box(txt = paste0("Excluded due to missing data\nn = ", excluded_count)) %>%
add_box(txt = paste0("Selected Variables\nN = ", after_select)) %>%
add_side_box(txt = paste0("Excluded for failing attention checks\nn = ", attention_fail)) %>%
add_box(txt = paste0("Kruskal-Wallis Analyses\nN = ", after_select)) %>%
add_side_box(txt = paste0("Excluded due to missing data\nn = ", excluded_log_reg)) %>%
add_box(txt = paste0("Logistic Regression Analyses\nN = ", log_regression))
# Save flowchart as an object
plot(consort_plot)
Logistic Regression for White vs. Color
Call:
glm(formula = TRUMPVOTE ~ ZEROSUM_IDENTITY + ZEROSUM_ECONOMIC,
family = binomial, data = white_data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.48311 2.86022 -1.218 0.22331
ZEROSUM_IDENTITY 1.20700 0.39952 3.021 0.00252 **
ZEROSUM_ECONOMIC -0.07775 0.43602 -0.178 0.85848
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 49.795 on 35 degrees of freedom
Residual deviance: 31.104 on 33 degrees of freedom
AIC: 37.104
Number of Fisher Scoring iterations: 5
Call:
glm(formula = TRUMPVOTE ~ ZEROSUM_IDENTITY + ZEROSUM_ECONOMIC,
family = binomial, data = poc_data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.2070 1.8668 -0.647 0.517924
ZEROSUM_IDENTITY 1.4247 0.3963 3.595 0.000324 ***
ZEROSUM_ECONOMIC -0.6715 0.4209 -1.595 0.110640
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 70.210 on 50 degrees of freedom
Residual deviance: 42.892 on 48 degrees of freedom
AIC: 48.892
Number of Fisher Scoring iterations: 5ZEROSUM_IDENTITY is positively associated with TRUMPVOTE for both groups. The coefficients are numerically similar (1.3 vs 1.59). ZEROSUM_ECONOMIC is significant only for POC and negative (-0.9002), meaning higher economic zero-sum beliefs reduce the likelihood of voting Trump for POC. For White participants, this effect is near zero (-0.09855).
Discussion
In the modern era, political scientists and mainstream media have pointed to a racial realignment (Barber & Pope, 2024) and class dealignment (Meyer, 2025) to explain the shifting political landscape, particularly the significant changes in voter behavior based on racial identity, social class, and educational attainment. The current study extends this literature by emphasizing the importance of social identities in influencing voter preference in the 2024 U.S. Presidential election. However, in contrast to decades of existing research demonstrating that social identities explain voter behavior and preference, our findings suggest that beliefs about social identity groups—specifically zero-sum social identity beliefs—may matter more than a person’s self-reported social identities. Results of a logistic regression classifying self-reported voting behavior in the 2024 U.S. Presidential election indicate that the newly developed measure of zero-sum social identity beliefs (ZEROSUM_IDENTITY) produced the second largest coefficient estimate, trailing only political ideology and exceeding other sociodemographic variables including gender (GENDER_MALE) and racial identity (RI_Else). Decision tree and random forest models also emphasize the importance zero-sum social identity beliefs for classifying voter preference. Also, an item-by-item analysis of all zero sum social identity beliefs showed significantly group differences for political party affiliation (POLITICALPARTY): democrat, republican, and independent.
The current study also advances the domain-specific conceptualization of zero-sum beliefs. An exploratory factor analysis of zero-sum belief items revealed a three-factor solution comprising general, economic, and social identity dimensions. Furthermore, a paired t-test demonstrated that zero-sum economic beliefs (ZEROSUM_ECONOMIC) and zero-sum social identity beliefs (ZEROSUM_IDENTITY) differed significantly from one another and functioned as distinct predictors of voter preference in the 2024 election. To our knowledge, this is the first study to demonstrate that zero-sum social identity beliefs significantly predict voter preference for Donald Trump over Kamala Harris in the 2024 U.S. Presidential election, independent of traditional demographic predictors.
References
Barber, M., & Pope, J. C. (2024). The Crucial Role of Race in Twenty-First Century US Political Realignment. Public Opinion Quarterly, 88(1), 149–160. https://doi.org/10.1093/poq/nfad063
Chinoy, S., Nunn, N., Sequeira, S., & Stantcheva, S. (n.d.). Zero-Sum Thinking and the Roots of U.S. Political Differences.
Davidai, S., & Tepper, S. J. (2023). The psychology of zero-sum beliefs. Nature Reviews Psychology, 2, 472–482. https://doi.org/10.1038/s44159-023-00194-9
Meyer, N. (2025). The Democrats Embrace Dealignment. Catalyst, 8(4), 8–51.
Różycka-Tran, J., Boski, P., & Wojciszke, B. (2015). Belief in a Zero-Sum Game as a Social Axiom: A 37-Nation Study. Journal of Cross-Cultural Psychology, 46(4), 525–548. https://doi.org/10.1177/0022022115572226