RRreg: Randomized Response

RRreg allows for univariate and multivariate analyses of randomized response (RR) designs. RR designs are used in surveys to obtain valid prevalence estimates of sensitive attributes (e.g., cocaine abuse) by ensuring complete anonymity of the respondents. An overview of the RR designs available in RRreg as well as examples can be found in our paper:

  • [PDF] Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1-29. doi:10.18637/jss.v085.i02
    [BibTeX] [Abstract] [GitHub]

    The randomized-response (RR) technique was developed to improve the validity of measures assessing attitudes, behaviors, and attributes threatened by social desirability bias. The RR removes any direct link between individual responses and the sensitive attribute to maximize the anonymity of respondents and, in turn, to elicit more honest responding. Since multivariate analyses are no longer feasible using standard methods, we present the R package RRreg that allows for multivariate analyses of RR data in a user-friendly way. We show how to compute bivariate correlations, how to predict an RR variable in an adapted logistic regression framework (with or without random effects), and how to use RR predictors in a modified linear regression. In addition, the package allows for power-analysis and robustness simulations. To facilitate the application of these methods, we illustrate the benefits of multivariate methods for RR variables using an empirical example.

    @article{heck2018rrreg,
    title = {{{RRreg}}: {{An R}} Package for Correlation and Regression Analyses of Randomized Response Data},
    volume = {85(2)},
    doi = {10.18637/jss.v085.i02},
    abstract = {The randomized-response (RR) technique was developed to improve the validity of measures assessing attitudes, behaviors, and attributes threatened by social desirability bias. The RR removes any direct link between individual responses and the sensitive attribute to maximize the anonymity of respondents and, in turn, to elicit more honest responding. Since multivariate analyses are no longer feasible using standard methods, we present the R package RRreg that allows for multivariate analyses of RR data in a user-friendly way. We show how to compute bivariate correlations, how to predict an RR variable in an adapted logistic regression framework (with or without random effects), and how to use RR predictors in a modified linear regression. In addition, the package allows for power-analysis and robustness simulations. To facilitate the application of these methods, we illustrate the benefits of multivariate methods for RR variables using an empirical example.},
    journaltitle = {Journal of Statistical Software},
    date = {2018},
    pages = {1-29},
    keywords = {heckfirst},
    author = {Heck, Daniel W and Moshagen, Morten},
    github = {https://github.com/danheck/RRreg}
    }

Further examples and model details are illustrated in the package vignette: RRreg Vignette

RRreg is a package for the R statistics environment and can be downloaded as follows:

  • by typing install.packages(‘RRreg’) in an active R session (Link to CRAN)
  • directly from GitHub as explained on the RRreg page (which provides the latest development version)
Please send questions/comments/suggestions to heck@uni-mannheim.de