---
title: "Example: Gender and Zero-Sum Beliefs"
subtitle: "Testing social identity theory with zero sum gender beliefs"
date: "August 20, 2025"
author: 
  - name: "Zihan Hei"
    email: zhei@binghamton.edu
    affiliation:
      - name: Binghamton University 
        url: "https://fripublichealth.quarto.pub/zerosum"
    attributes:
      corresponding: true
abstract: |
  
  Background: Zero-sum beliefs—the perception that one group's gains necessarily result in another group's losses—are important predictors of political attitudes. However, less is known about whether one's gender-related zero sum beliefs differ based on gender identity. 
  
  Method: A sample of 122 participants were recruited on the platform Prolific to participate in a Qualtrics survey. Respondents completed a measure of zero-sum social identity beliefs (Khan et al., 2025), such as: 'As women face less sexism, men end up facing more sexism.' An independent samples Welch t-test for non-normal continuous data was used to test for differences based on gender identity. 
  
  Results: Inconsistent with the alternative hypothesis, ratings of zero-sum gender beliefs did not differ by gender identity between men and women. 
  
  Discussion: Inconsistent with social identity theory, a person's gender identity does not explain zero-sum gender beliefs in this study. Future research should examine whether other social identities may be key drivers of variation in zero sum social identity beliefs. Ultimately, this research aims to identify causes of zero-sum social identity thinking to reduce prejudice undermining support for gender equity health policies.
  
keywords:
  - zero sum beliefs
  - gender identity
  - sexism
  - social identity theory
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bibliography: references.bib
---

# Introduction

Zero-sum gender beliefs refer to the perception that progress for one gender necessarily comes at the expense of the other (Davidai & Tepper, 2023). Such beliefs are linked to more negative attitudes toward gender equality and poorer interpersonal outcomes (Wong et al., 2022). Prior work suggests that these beliefs may differ across demographic groups, particularly between men and women. The objective of the present study is to answer the research question: *Do zero-sum beliefs about gender discrimination differ by sex?*

# Methods

## **Participants and Data**

Participants were recruited using the Prolific platform, which houses over 55,000 active users. All active users were eligible to participate in the survey if they met the inclusion criteria: 18 years of age or older. Using a quota sampling, participants were recruited by Prolific to achieve specific sub-group sample sizes for sex (50% male, 50% female), political affiliation (33% Republican, 33% Democrat, and 34% Independent), and racial identity (White 40%, Black 20%, Asian 20%, Mixed 10%, Other 10%). The survey reached 135 individuals, with 10 responses being excluded due to being incomplete. This left a sample of 125. The final sample was similar demographically to the quotas for sex (50.4% male, 49.6% female), 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%). Research was conducted ethically to protect the rights, welfare, confidentiality, and privacy of participants. Also, participants were informed of the project and provided their consent before beginning the survey. Respondents completed a survey on Qualtrics with open-ended and closed-ended responses, resulting in a mix of qualitative and quantitative data. This study is a subset of a larger study on health beliefs.

## **Measures**

A single item of a 11-item zero sum beliefs measure (Davidai & Ongis, 2019) was measured using a 7-point Likert scale (1 = *Strongly Disbelieve*, 7 = *Strongly Believe*): *“As women face less sexism, men end up facing more sexism.”* (`ZEROSUM_4`). The analytic sample included participants who reported their sex as male or female.

A measure of gender identity (`GENDER`) included the response options of man, woman, non-binary, prefer not to say. Due to the small sample of non-binary partcipants (n=4), a binary variable of gender identity (`GENDER_MAN`) was created based on participants who identified as a man (0) or woman (1).

## **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](https://r4ds.hadley.nz/whole-game.html)) 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.

## Load

```{r}
#| label: LOAD
#| output: false
#| message: false
#| warning: false

library(ggplot2)
library(dplyr)
library(tidyr)
library(ggrain)  
library(rmarkdown)
library(readr)
library(patchwork)
library(codebookr)
library(dplyr)
library(haven)
library(codebook)
library(rempsyc)
library(car)
library(knitr)
library(broom)
library(ggdist)
library(patchwork)
library(car)
library(dataMaid)
library(devtools)
library(MBESS)
library(apaTables)
library(ggpubr)
library(psych)
library(forcats)
library(corrplot)
library(kableExtra)
```

## Import Data

```{r}
#| label: IMPORT 

select_data <- read_csv("data/select_data.csv")

#explanation: import raw data into posit cloud
```

```{r}
#| label: TRANSFORM-GENDER (man (0) or woman (1))

select_data <- select_data %>%
  mutate(GENDER_MAN = case_when(
    GENDER == 1 ~ 1,         # women 1
    GENDER == 2 ~ 0,         # man 0
    TRUE  ~ NA_real_  
)) %>%
# categorical version for plot
  mutate(GENDER_MAN_cat = case_when(
      GENDER_MAN == 1 ~ "Women",
      GENDER_MAN == 0 ~ "Men",
      TRUE    ~ NA_character_
))
```

## Transform

```{r}
#| label: TRANSFORM
library(dplyr)
avg_zerosum <- select_data %>%
  group_by(GENDER_MAN) %>%
  summarise(mean_zerosum = mean(ZEROSUM_4, na.rm = TRUE))

avg_zerosum

#Explanation: use summarise to create the average score on ZEROSUM_4 variable by GENDER_MAN
```

# Results

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 gender.

## Model

### Descriptives

| Variable    | n   | mean | sd   | range |
|-------------|-----|------|------|-------|
| `ZEROSUM_4` | 121 | 3.07 | 1.69 | 1-7   |

```{r}
#| label: MODEL
t_test <- t.test(ZEROSUM_4 ~ GENDER_MAN, data = select_data)
t_test

#explanation: use independent t-test to compare men vs. women on ZEROSUM_4
```

However, the independent samples Welch *t*-test found that this difference was not statistically significant, *t* (`r round(t_test$parameter, 2)`) = `r round(t_test$statistic, 2)`, *p* = `r round(t_test$p.value, 3)`, 95% CI \[`r round(t_test$conf.int[1], 2)`, `r round(t_test$conf.int[2], 2)`\].

## Visualize

This raincloud plot (Figure 1) shows the distribution of agreement scores for the statement “*As women face less sexism, men end up facing more sexism*” by participants' gender identity. The half-violins represent smoothed distributions, boxplots display the interquartile range and median, white points mark group means, and colored dots show individual participant ratings. While both men and women tended to fall near the middle of the scale, men’s responses were slightly higher on average, indicating a small shift toward greater agreement with the statement. Descriptive statistics indicated that men (M = 3.2) scored slightly higher than women (M = 2.95) on `ZEROSUM_4`

```{r}
#| label: VISUALIZE
#| fig-cap: "Figure 1. Distribution of agreement with ZEROSUM_4 by GENDER_MAN"

library(ggplot2)
library(ggdist)
library(dplyr)

select_data %>%
  filter(!is.na(ZEROSUM_4), !is.na(GENDER_MAN_cat)) %>%
  ggplot(aes(x = GENDER_MAN_cat, y = ZEROSUM_4, fill = GENDER_MAN_cat, color = GENDER_MAN_cat)) +
  stat_halfeye(
    adjust = 0.5,
    width = 0.6,
    .width = 0,
    justification = -0.2,
    point_colour = NA,
    alpha = 0.7
  ) +
  geom_boxplot(
    width = 0.15,
    outlier.shape = NA,
    alpha = 0.5
  ) +
  geom_point(
    size = 1.3,
    alpha = 0.3,
    position = position_jitter(seed = 1, width = 0.1)
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    size = 3,
    color = "white",
    stroke = 1.5,
    shape = 21
  ) +
  theme_bw() +
  labs(
    title = "As women face less sexism, men end up facing more sexism.",
    x = "Political Affiliation",
    y = "Level of Agreement",
    caption = "White dots = means; colored dots = individual responses"
  ) +
  theme(
    plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
    plot.caption = element_text(size = 9, color = "gray40", hjust = 0.5),
    axis.title = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    legend.position = "none",
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3)
  ) +
  scale_y_continuous(
    breaks = 1:7,
    labels = c(
      "1: Strongly Disbelieve", 
      "2: Disbelieve", 
      "3: Somewhat Disbelieve", 
      "4: Neither", 
      "5: Somewhat Believe", 
      "6: Believe", 
      "7: Strongly Believe"
    ),
    limits = c(1, 7)
  )

#explanation: use ggplot2 to create a raincloud plot of ZEROSUM_4 variable by GENDER_MAN_cat
```

# Discussion

The results of this study are inconsistent with social identity theory (SIT), which posits that individuals' attitudes and beliefs vary based on their social identities (Tajfel & Turner, 1979). In our sample, participants' gender identity did not significantly influence their zero-sum gender beliefs. Although men reported slightly higher zero-sum beliefs than women, the difference was not statistically significant, which contrasts with previous research demonstrating stronger zero-sum beliefs among men (Wong et al., 2022). This discrepancy may be attributable to the small sample size (N=122), which represents a key limitation of this study. The lack of statistical power may have obscured meaningful differences that exist in the broader population. Future research employing larger and more diverse samples is needed to clarify whether gender-based patterns in zero-sum beliefs persist and to explore how these beliefs relate to broader political and social attitudes regarding gender equity. Understanding the mechanisms underlying zero-sum thinking remains critical for addressing prejudice that may undermine support for gender equity policies.

# References

Davidai, S., & Tepper, S. J. (2023). The psychology of zero-sum beliefs. *Nature Reviews Psychology*, *2*(8), 472-482. https://doi.org/10.1038/s44159-023-00194-9

Wong, Y. J., Klann, E. M., Bijelić, N., & Aguayo, F. (2017). The link between men’s zero-sum gender beliefs and mental health: Findings from Chile and Croatia. *Psychology of Men & Masculinity*, *18*(1), 12-19. http://dx.doi.org/10.1037/men0000035
