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

In [1]:

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

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.

In [2]:

Show the code

#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 appropriatedefined]

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

Read Me

Read Me

GENERAL INFORMATION

Principal Investigator Information

Data set

SHARING/ACCESS INFORMATION

FILES

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

Methods for processing the data:

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

Sociodemographics

Zero Sum Beliefs

Outcomes

DATA-SPECIFIC INFORMATION FOR: `select_data.csv`

DATA-SPECIFIC INFORMATION FOR: [FILENAME]

Source

Read Me

GENERAL INFORMATION

Principal Investigator Information

Data set

SHARING/ACCESS INFORMATION

FILES

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

Methods for processing the data:

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

Sociodemographics

Zero Sum Beliefs

Outcomes

DATA-SPECIFIC INFORMATION FOR: select_data.csv

DATA-SPECIFIC INFORMATION FOR: [FILENAME]

Source

DATA-SPECIFIC INFORMATION FOR: `select_data.csv`