---
title: "Read Me"
subtitle: "Zero-sum social identity, not zero-sum economic beliefs, predict voting behavior in 2024 U.S. presidential election" 
author:
  - name: Aashia Khan
  - name: Zihan Hei
  - name: Jeff John
  - name: Shane McCarty
abstract: |
  *Background:* This readme file was generated on 2025-07-03 by Shane McCarty.
editor: 
  markdown: 
    wrap: 72
---

*Note: \[text within square brackets should be changed to specific
information about your dataset.\]* help text within asterisks should be
deleted before finalizing your document\*\*

# GENERAL INFORMATION

## Principal Investigator Information

Name: Shane McCarty

ORCID: 0000-0001-8930-7049

Institution: Binghamton University

Email: smccarty1\@binghamton.edu

## Data set

-   Title of data set: 2025 Prolific Health Beliefs Survey

<!-- -->

-   Date of data collection: 2025-04-15

-   Geographic location of data collection: online via Prolific

-   Information about funding sources that supported the collection of
    the data: First-year Research Immersion (FRI) program, Binghamton
    University

# SHARING/ACCESS INFORMATION

-   Licenses/restrictions placed on the data: no restrictions
-   Links to publications that cite or use the data: TBD
-   Links to other publicly accessible locations of the data: TBD
-   Links/relationships to ancillary data sets: TBD
-   Was data derived from another source? No
    -   If yes, list source(s):
-   Recommended citation for this dataset: McCarty, S. (2025). Health
    beliefs on violence, mental health, and food \[data set\]. \[*OSF
    URL TBD\]*

# FILES

| File Type | Source | Details | Pr ovided |
|------------------|------------------|------------------|------------------|
| Data | `data/originaldata.csv` | Full data set beyond project | No |
| Data | `data/alldata.csv` | alldata needed for project | Yes |
| Data | `data/select_data.csv` | cleaned data | Yes |
| **R Script** | `scripts/install.R` | install packages | Yes |
| **R Script** | `scripts/clean.R` | cleaning procedures to produce `select_data` | Yes |
| Quarto Markdown Document | `quarto/anova.qmd` | original report with ANOVAs | Yes |
| Quarto Markdown Document | `quarto/report.qmd` | final report with Kruskall Wallis tests | Yes |
| Quarto Markdown Document | `quarto/manuscript.qmd` | final manuscript | TBD |
| Quarto Markdown Document | `quarto/supplementary.qmd` | additional assumption tests, plots, and more | TBD |
| Quarto Markdown Document | `quarto/poster.qmd` | poster of the results | TBD |
| Quarto Markdown Document | `quarto/presentation.qmd` | presentation of the results | TBD |

```{mermaid}
flowchart TD
    A[alldata.csv] --> B[clean.R]
    B --> C[select_data.csv]
    C --> D[anova.qmd]
    C --> E[report.qmd]
    E --> F[manuscript.qmd]
    F --> G[supplementary.qmd]
    F --> H[presentation.qmd]
    F --> I[poster.qmd]
    
    %% Styling
    classDef dataFile fill:#e1f5fe,stroke:#0277bd,stroke-width:2px
    classDef rScript fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef qmdFile fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px
    classDef finalOutput fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    
    class A,C dataFile
    class B rScript
    class D,E qmdFile
    class F,G,H,I finalOutput
```

**Documentation**

`readme.qmd`

:   *documentation for the project*

**Data Files and Dataframes**

`🔢 alldata.csv` and `alldata`

:   *raw data and original dataframe*

`🔢 select_data`

:   *dataframe used for all analyses*

**Core R Scripts**

📦 `install.R`

:   *installing R packages*

🧹 `clean.R`

:   *cleaning of raw data*

    mutate `race` variable into `racialidentity_[]`

**Analysis Reports**

📊 `anova.qmd`

:   *normality tests and ANOVAs*

:   -   Eleven normality tests and analysis of variance (ANOVA) tests
        were conducted for each of the zero-sum belief items.

    -   Based on the non-normal results, we ran Kruskal Wallis tests
        instead.

`logisticregression.qmd`

:   -   Logistic regression models examining predictors of 2024 U.S.
        presidential vote choice.

    -   Conducted factor analysis on 11 zero-sum belief items, resulting
        in two reliable factors:

        \- `ZEROSUM_ECONOMIC` (e.g., beliefs about rich vs. poor)

        \- `ZEROSUM_IDENTITY` (e.g., beliefs about racial or social
        groups)

`machinelearning.qmd`

:   -   Classification models predicting 2024 U.S. presidential vote
        choice using decision trees and random forests.

📈 `report.qmd`

:   *contains assumption checks, all tests, and plots.*

**Publication Materials**

📝 `manuscript.qmd`

:   *contains all tests and select plots for manuscript*

    Statistical tests:

    -   11 Kruskall Wallis tests

    -   1 binomial logistic regression

:   Figures:

    -   Fig 1: `POLITICALPARTY`, `ZEROSUM_3` (Few vs. Many)

    -   Fig 2: `POLITICALPARTY`, `ZEROSUM_5` (Minorities vs. White)

    -   Fig 3: `POLITICALPARTY`, `ZEROSUM_11` (Undocumented vs. U.S.
        Citizens)

    -   Fig 4: `POLITICALPARTY`, `ZEROSUM_11` (Universal vs. Private
        Insurance)

    -   Fig 5: `TRUMPVOTE` \~ `ZEROSUM_IDENTITY`

📝 `supplementary.qmd`

:   report.qmd code chunks excluded from the manuscript

**Communication Materials**

🖼️ `poster.qmd`

:   research poster using `posterdown` package

💻 `presentation.qmd`

:   slides of the research

***Data workflow using files***

-   `install.R` —\> `clean.R` —\> `anova.qmd` —\>
    `logisticregression.qmd` —\> `machinelearning.qmd` —\> `report.qmd`
    —\> `manuscript.qmd` —\> `supplementary.qmd`

    -   This workflow was used to produce the final manuscript and
        supplementary materials.

-   `manuscript.qmd` —\> `presentation.qmd` —\> `poster.qmd`

    -   The manuscript was converted into a presentation format and
        poster format

-   Additional related data collected that was not included in the
    current data package: The 2025 health beliefs data set consists of
    additional variables beyond the sociodemographics and zero-sum
    beliefs.

-   Are there multiple versions of the dataset? No.

    -   If yes, name of file(s) that was updated:
    -   Why was the file updated?
    -   When was the file updated?

# METHODOLOGICAL INFORMATION

## Description of methods used for collection/generation of data:

*include links or references to publications or other documentation
containing experimental design or protocols used in data collection*

## Methods for processing the data:

The Qualtrics survey was posted to the Prolific platform, resulting in
XXX of participants who completed the survey.

*describe how the submitted data were generated from the raw or
collected data*

![](participant_flowchart.png){fig-align="center"}

## Instrument- or software-specific information needed to interpret the data:

*include full name and version of software, and any necessary packages
or libraries needed to run scripts.*

```{r}
#sessionInfo()
```

*include any additional methodological information needed to interpret
and/or use the data, as appropriate*:

### Sociodemographics

`AGE`

:   What is your age? (enter a whole number, such as 45) 

`GENDER`

:   Gender identity is how someone feels about their own gender. There
    are many ways a person can describe their gender identity and many
    labels a person can use. Which of the following terms best describes
    your current gender identity?

    o Girl or woman  (1) 

    o Boy or man  (2) 

    o Nonbinary, genderfluid, or genderqueer  (3) 

    o I am not sure or questioning  (4) 

    o I don’t know what this question means  (-50) 

    o Decline to answer  (-99)

`RACIALIDENTITY`

:   What is your racial/ethnic identity?

:   ▢        American Indian or Alaska Native  (1) 

    ▢        Asian  (2)

    ▢        Black or African American  (3) 

    ▢        Hispanic or Latine  (4) 

    ▢        Middle Eastern or North African  (5) 

    ▢        Native Hawaiian or Pacific Islander  (6) 

    ▢        White  (7)

    ▢        Other  (8)
    \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

    ▢        ⊗Prefer not to say  (-99)

`SOCIALCLASS`

:   Think of this ladder as representing where people stand in the
    United States. At the top of the ladder are the people who are the
    best off – those who have the most money, the most education, and
    the most respected jobs. At the bottom are the people who are the
    worst off – those who have the least money, least education, the
    least respected jobs, or no job. The higher up you are on this
    ladder, the closer you are to the people at the very top; the lower
    you are, the closer you are to the people at the very bottom.   

    If you are a college student, please tell us where you think your
    family would be on this ladder. For everyone else, please select the
    rung where you think you stand at this time in your life relative to
    other people in the United States.

    o 10  (10) 

    o 9  (9) 

    o 8  (8) 

    o 7  (7) 

    o 6  (6) 

    o 5  (5) 

    o 4  (4) 

    o 3  (3) 

    o 2  (2) 

    o 1  (1)

`POLITICALAFFIL`

:   What is your political party affiliation?

    o Conservative Party  (1) 

    o Democratic Party  (2) 

    o Libertarian Party  (3) 

    o Republican Party  (4) 

    o Socialist or Green Party  (5) 

    o None of the above  (6)

`POLITICALBELIEFS`

:   What are your political beliefs?

    o Far Left / Leftist  (1) 

    o Very Liberal  (2) 

    o Liberal  (3) 

    o Moderate  (4) 

    o Conservative  (5) 

    o Very Conservative  (6) 

    o Alt-Right / Far-Right  (7) 

    o Prefer not to say  (-99) 

    o None of the above or don't know  (-50)

### Zero Sum Beliefs

`ZEROSUM_1`

:   Life is so devised that when somebody gains, others have to lose.

`ZEROSUM_2`

:   When some people are getting poorer, it means that other people are
    getting richer.

`ZEROSUM_3`

:   The wealth of a few is acquired at the expense of many.

`ZEROSUM_4`

:   As women face less sexism, men end up facing more sexism.

`ZEROSUM_5`

:   Less discrimination against minorities means more discrimination
    against whites.

`ZEROSUM_6`

:   More opportunity for transwomen means less opportunity for people
    who are assigned female at birth.

`ZEROSUM_7`

:   More health care access for undocumented immigrants means less
    access for U.S. citizens.

`ZEROSUM_8`

:   If there is equal pay for women, men will get lower wages.

`ZEROSUM_9`

:   LGBTQ+ rights mean less freedom for religious groups.

`ZEROSUM_10`

:   Accessible healthcare for people with disabilities means longer wait
    times for non-disabled patients.

`ZEROSUM_11`

:   Universal healthcare means worse healthcare for those who can afford
    private insurance.

### Outcomes

`VOTE2024`

:   Who did you vote for in the 2024 election? 

    o Donald Trump  (1) 

    o Kamala Harris  (2) 

    o Jill Stein  (3) 

    o Robert Kennedy Jr.  (4) 

    o Chase Oliver  (5) 

    o Claudia De La Cruz  (6) 

    o Cornel West  (7) 

    o DID NOT VOTE IN 2024  (8) 

    o Prefer not to say  (-99)

`TRUMPVOTE`

:   mutated variable from `VOTE2024`

    o Kamala Harris  (0) 

    o Donald Trump  (1) 

Standards and calibration information, if appropriate: \*

Environmental/experimental conditions: \*

Describe any quality-assurance procedures performed on the data: \*

People involved with sample collection, processing, analysis, and/or
submission:

# DATA-SPECIFIC INFORMATION FOR: `select_data.csv`

*repeat this section for each dataset, folder or file, as
appropriate*defined\]

-   Number of variables:
-   Number of cases/rows:
-   Variable List: *list variable name(s), description(s), unit(s) and
    value labels as appropriate for each*
-   Missing data codes: *list code/symbol and definition*
-   Specialized formats or other abbreviations used:

# DATA-SPECIFIC INFORMATION FOR: \[FILENAME\]

*repeat this section for each dataset, folder or file, as appropriate*

-   Number of variables:
-   Number of cases/rows:
-   Variable List: *list variable name(s), description(s), unit(s) and
    value labels as appropriate for each*
-   Missing data codes: *list code/symbol and definition*
-   Specialized formats or other abbreviations used:

## Source

This readme.qmd is adapted from a *Readme Template* *for* *Data*:
Kozlowski, Wendy. (2025) Readme Template for Data. Cornell University
eCommons Repository. https://doi.org/10.7298/mhns-zm71
