4 papers
Modeling Ordinal Survey Data with Unfolding Models
Rayleigh Lei, Abel Rodriguez
Surveys that rely on ordinal polychotomous (Likert-like) items are widely employed to capture individual preferences because they allow respondents to express both the direction an…
Calibrating hierarchical Bayesian domain inference for a proportion
Rayleigh Lei, Yajuan Si
Small area estimation (SAE) improves estimates for local communities or groups, such as counties, neighborhoods, or demographic subgroups, when data are insufficient for each area.…
pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data in R
Skylar Shi, Abel Rodriguez, Rayleigh Lei
Probit unfolding models (PUMs) are a novel class of scaling models that allow for items with both monotonic and non-monotonic response functions and have shown great promise in the…
Logit unfolding choice models for binary data
Rayleigh Lei, Abel Rodriguez
Discrete choice models with non-monotonic response functions are important in many areas of application, especially political sciences and marketing. This paper describes a novel u…