researchers to conduct multivariate statistical analyses of
survey data with the item count technique. This survey
methodology is also known as the list experiment or the
unmatched count technique and is an alternative to the commonly
used randomized response method. The package implements the

survey data with list experiments. In addition, the package
implements the statistical test that is designed to detect
certain failures of the item count technique. This survey
methodology is also known as the item count technique or the
methods described in Imai (2010) and Blair and Imai (2010).

Title: Multivariate Statistical Analysis for List Experiments
certain failures of list experiments. This survey methodology
is also known as the item count technique or the unmatched
count technique and is an alternative to the commonly used
randomized response method. The package implements the methods
described in Imai (2010) and Blair and Imai (2010).

Title: Statistical Methods for the Item Count Technique and List

developed by Imai (2010) and Blair and Imai (2010).

developed by Imai (2010) and Blair and Imai (2010), and a
Bayesian MCMC implementation of regression for the standard

Bayesian MCMC implementation of regression for the standard and
multiple sensitive item list experiment designs.

Author: Graeme Blair <gblair@Princeton.edu>, Kosuke Imai <kimai@Princeton.edu>
Description: list is a publicly available R package that allows researchers to conduct multivariate statistical analyses of survey data with list experiments. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) and Blair and Imai (2012), a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs including ceiling and floor effects and a random effects setup, and a Bayesian MCMC hierarchical regression model with up to three hierarchical groups.