plot.powerplot

Plot estimated power from Monte Carlo simulation as a function of the sample size, separately for different effect sizes and multivariate RR methods

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>).

Daniel W. Heck

RRreg

Correlation and Regression Analyses for Randomized Response Data

Morten Moshagen

plot.powerplot function

<dl><dt>x</dt>
<dd>a <code>powerplot</code> object</dd>
<dt>...</dt>
<dd>ignored</dd></dl>

Arguments

Plot power of multivariate RR methods — plot.powerplot

<dl>

<dt>x</dt>
<dd>a <code>powerplot</code> object</dd>


<dt>...</dt>
<dd>ignored</dd>

</dl>

plot.powerplot: Plot power of multivariate RR methods

Description

Usage

Arguments