Example: Gender and Zero-Sum Beliefs

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) 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

In [1]:

library(ggplot2)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(ggrain)  

Registered S3 methods overwritten by 'ggpp':
  method                  from   
  heightDetails.titleGrob ggplot2
  widthDetails.titleGrob  ggplot2

library(rmarkdown)
library(readr)
library(patchwork)
library(codebookr)
library(dplyr)
library(haven)
library(codebook)

Attaching package: 'codebook'

The following object is masked from 'package:codebookr':

    codebook

library(rempsyc)

Suggested APA citation: Thériault, R. (2023). rempsyc: Convenience functions for psychology. 
Journal of Open Source Software, 8(87), 5466. https://doi.org/10.21105/joss.05466

library(car)

Loading required package: carData

Attaching package: 'car'

The following object is masked from 'package:dplyr':

    recode

library(knitr)
library(broom)
library(ggdist)
library(patchwork)
library(car)
library(dataMaid)

Attaching package: 'dataMaid'

The following object is masked from 'package:rmarkdown':

    render

The following object is masked from 'package:dplyr':

    summarize

library(devtools)

Loading required package: usethis

Attaching package: 'devtools'

The following object is masked from 'package:dataMaid':

    check

library(MBESS)
library(apaTables)
library(ggpubr)
library(psych)

Attaching package: 'psych'

The following object is masked from 'package:MBESS':

    cor2cov

The following object is masked from 'package:car':

    logit

The following object is masked from 'package:codebook':

    bfi

The following objects are masked from 'package:ggplot2':

    %+%, alpha

library(forcats)
library(corrplot)

corrplot 0.95 loaded

library(kableExtra)

Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

Import Data

In [2]:

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

Rows: 122 Columns: 74
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (18): INCOME, STREETRACE, SEXUAL_IDENTITY, POLITICALAFFIL, SERIOUS, POLI...
dbl (54): AGE, EDUCATION, SOCIALSTATUS, GENDER, POLITICALBELIEFS, VOTE2024, ...
num  (2): RELIGIOUS_IDENTITY, RACE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#explanation: import raw data into posit cloud

In [3]:

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

In [4]:

library(dplyr)
avg_zerosum <- select_data %>%
  group_by(GENDER_MAN) %>%
  summarise(mean_zerosum = mean(ZEROSUM_4, na.rm = TRUE))

avg_zerosum

# A tibble: 3 × 2
  GENDER_MAN mean_zerosum
       <dbl>        <dbl>
1          0         3.2 
2          1         2.95
3         NA         3   

#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

In [5]:

t_test <- t.test(ZEROSUM_4 ~ GENDER_MAN, data = select_data)
t_test

    Welch Two Sample t-test

data:  ZEROSUM_4 by GENDER_MAN
t = 0.82137, df = 114.93, p-value = 0.4131
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -0.3566147  0.8618779
sample estimates:
mean in group 0 mean in group 1 
       3.200000        2.947368 

#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 (114.93) = 0.82, p = 0.413, 95% CI [-0.36, 0.86].

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

In [6]:

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)
  )

Warning: Removed 17 rows containing missing values or values outside the scale range
(`geom_point()`).

Figure 1. Distribution of agreement with ZEROSUM_4 by GENDER_MAN

#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