Structured hierarchical regression for Likert scales including dispersion effects: Models and fitting tools

Hierarchical models for ordinal responses, in which responses are modeled successively by partitioning groups of categories into finer subgroups are proposed. These partitions reflect conceptually meaningful distinctions among categories. Such models are particularly well suited for Likert items, which typically differentiate between disagreement, agreement, and, in some cases, a neutral category. The hierarchical framework offers a parsimonious representation of predictor effects and often provides a better fit than traditional ordinal models. It also enables the investigation of dispersion effects, that is, systematic tendencies of respondents to prefer either extreme categories or middle categories, independently of the substantive content. In addition to specialized fitting tools for ordinal models, we provide a more general procedure that can be used to fit any hierarchically structured model. The practical use of these methods is demonstrated through illustrative examples. (PsycInfo Database Record (c) 2026 APA, all rights reserved).