About This Study

The present study aimed to explore

  • the willingness of individuals within the general public to participate in genetic research,
  • whether willingness to participate in genetic research differed by age, gender, race, ethnicity, and education level,
  • whether an individual’s trust in research establishment, knowledge of genetics, level of worry, health anxiety, altruism, health status (for themselves or a loved one) predicted their willingness to participate in genetics research.,
For individuals unwilling to provide a saliva sample/blood sample for genetic research, we explored
  • the primary reason for their unwillingness and
  • factors that impact their decision to participate in genetic research (e.g., compensation, confidentiality, analytic approach, topic studied, feedback options).

About This App

This interactive Shiny application allows users to explore predictors of willingness to participate in behavior genetic research. Users can visualize variable distributions across participant samples (Prolific and SONA), view outcome definitions, and run logistic regression models to examine how predictors relate to willingness to participate in different research types.

Important: To view model results (tables and heatmaps), you must first select outcomes and predictors, then press the Run Logistic Models button in the sidebar. Model results will not appear or update until this is done.

Authors

  • Shannon M. O’Connor, University of Toledo
  • S. Mason Garrison, Wake Forest University

Contact

For questions or feedback, please contact: garrissm@wfu.edu or Shannon.OConnor@utoledo.edu

Source Code

The full source code is available on GitHub .

This table provides descriptions of the outcome variables used in the logistic regression models.

Outcome Prompt

Please take your time and read through the following list. Assume that you would be compensated for your time. This is hypothetical, so also assume that you have free time that would allow you to participate.

This heatmap displays the odds ratios for each predictor variable across different outcome variables.
This table displays precomputed summary statistics for numeric and categorical variables.

Numeric Summary Statistics

Categorical Summary Statistics

This table displays the results of the logistic regression models, including estimates, p-values, and confidence intervals.
This table displays the results of the logistic regression models in a format similar to regression tables, with coefficients, standard errors, and significance stars. Each column represents a different model.