Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (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>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724–732, <doi:10.3758/s13428-016-0729-x>).
|Author||Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>), Morten Moshagen [aut]|
|Maintainer||Daniel W. Heck <firstname.lastname@example.org>|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.