{ "_id": "6a229b7fcd65a98ecbd57975", "Package": "list", "Version": "9.2.6", "Title": "Statistical Methods for the Item Count Technique and List\nExperiment", "Authors@R": "c(person(\"Graeme\", \"Blair\", email = \"graeme.blair@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Winston\", \"Chou\", email = \"wchou@princeton.edu\", role = c(\"aut\")),\nperson(\"Kosuke\", \"Imai\", email = \"imai@harvard.edu\", role = c(\"aut\")),\nperson(\"Bethany\", \"Park\", email = \"bapark@princeton.edu\", role = c(\"ctb\")),\nperson(\"Alexander\", \"Coppock\", email = \"alex.coppock@yale.edu\", role = c(\"ctb\")))", "VignetteBuilder": "knitr", "Description": "Allows researchers to conduct multivariate statistical\nanalyses of survey data with list experiments. This survey\nmethodology is also known as the item count technique or the\nunmatched count technique and is an alternative to the commonly\nused randomized response method. The package implements the\nmethods developed by Imai (2011)\n, Blair and Imai (2012)\n, Blair, Imai, and Lyall (2013)\n, Imai, Park, and Greene (2014)\n, Aronow, Coppock, Crawford, and Green\n(2015) , Chou, Imai, and Rosenfeld\n(2017) , and Blair, Chou, and\nImai (2018)\n.\nThis includes a Bayesian MCMC implementation of regression for\nthe standard and multiple sensitive item list experiment\ndesigns and a random effects setup, a Bayesian MCMC\nhierarchical regression model with up to three hierarchical\ngroups, the combined list experiment and endorsement experiment\nregression model, a joint model of the list experiment that\nenables the analysis of the list experiment as a predictor in\noutcome regression models, a method for combining list\nexperiments with direct questions, and methods for diagnosing\nand adjusting for response error. 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