Graeme Blair [aut, cre], Kosuke Imai [aut, cre], Bethany Park [ctb],
Alexander Coppock [ctb], Winston Chou [ctb]

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>,
and Chou, Imai, and Rosenfeld (2016)
<http://imai.princeton.edu/research/files/auxiliary.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, and a method for combining
list experiments with direct questions. 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.

c(person("Graeme", "Blair", email = "graeme.blair@ucla.edu", role = c("aut", "cre")),
person("Winston", "Chou", email = "wchou@princeton.edu", role = c("aut")),
person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")),
person("Bethany", "Park", email = "bapark@princeton.edu", role = c("ctb")),
person("Alexander", "Coppock", email = "alex.coppock@yale.edu", role = c("ctb")))
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.
Graeme Blair [aut, cre],
Winston Chou [aut],
Kosuke Imai [aut],
Bethany Park [ctb],
Alexander Coppock [ctb]