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), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.
|Author||Daniel W. Heck [aut, cre], Morten Moshagen [aut]|
|Date of publication||2017-03-08 08:22:11|
|Maintainer||Daniel W. Heck <firstname.lastname@example.org>|
getPW: Get Misclassification Matrices for RR Models
minarets: Minaret Data
plot.powerplot: Plot power of multivariate RR methods
plot.RRlog: Plot Logistic RR Regression
powerplot: Power plots for multivariate RR methods
predict.RRlog: Predict Individual Prevalences of the RR Attribute
RRcor: Bivariate correlations including randomized response...
RRgen: Generate randomized response data
RRlin: Linear randomized response regression
RRlog: Logistic randomized response regression
RRmixed: Mixed Effects Logistic Regression for RR Data
RRreg-package: Correlation and Regression Analyses for Randomized Response...
RRsimu: Monte Carlo simulation for one or two RR variables
RRuni: Univariate analysis of randomized response data