list - Statistical Methods for the Item Count Technique and List
Experiment
Allows researchers to conduct multivariate statistical
analyses of survey data with 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)
<doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012)
<doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013)
<doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014)
<doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green
(2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld
(2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and
Imai (2018)
<https://imai.fas.harvard.edu/research/files/listerror.pdf>.
This includes a Bayesian MCMC implementation of regression for
the standard and multiple sensitive item list experiment
designs and a random effects setup, a Bayesian MCMC
hierarchical regression model with up to three hierarchical
groups, the combined list experiment and endorsement experiment
regression model, a joint model of the list experiment that
enables the analysis of the list experiment as a predictor in
outcome regression models, a method for combining list
experiments with direct questions, and methods for diagnosing
and adjusting for response error. In addition, the package
implements the statistical test that is designed to detect
certain failures of list experiments, and a placebo test for
the list experiment using data from direct questions.
