Correlation and Regression Analyses for Randomized Response Data
Analysis of Deviance for Logistic RR Regression Models
Get Misclassification Matrices for RR Models
Plot power of multivariate RR methods
Plot Logistic RR Regression
Power plots for multivariate RR methods
Predict Individual Prevalences of the RR Attribute
Bivariate correlations including randomized response variables
Generate randomized response data
Linear randomized response regression
Logistic randomized response regression
Mixed Effects Logistic Regression for RR Data
Correlation and Regression Analyses for Randomized Response Designs
Monte Carlo simulation for one or two RR variables
Univariate analysis of randomized response data
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>).