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

Gerhard Tutz1, Moritz Berger2

  • 1Department of Statistics, Ludwig-Maximilians-Universitat Munchen.

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|June 11, 2026
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Summary

Hierarchical models offer a parsimonious approach for analyzing ordinal responses, like Likert scales. These models provide better fit and reveal response tendencies beyond substantive content.

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Area of Science:

  • Statistics
  • Psychometrics
  • Ordinal Data Analysis

Background:

  • Traditional ordinal models may not fully capture the nuances of response categories.
  • Likert-type items often present ordered categories (e.g., disagreement to agreement).
  • Existing methods may lack flexibility for complex hierarchical structures.

Purpose of the Study:

  • To propose hierarchical models for ordinal responses.
  • To provide a flexible framework for analyzing Likert-type data.
  • To investigate dispersion effects in ordinal responses.

Main Methods:

  • Developing hierarchical models by successively partitioning response categories.
  • Utilizing specialized fitting tools for ordinal models.
  • Implementing a general procedure for fitting any hierarchically structured model.

Main Results:

  • Hierarchical models offer a parsimonious representation of predictor effects.
  • These models often provide a better fit compared to traditional ordinal models.
  • The framework allows for the investigation of dispersion effects, independent of content.

Conclusions:

  • Hierarchical models are well-suited for ordinal data, especially Likert items.
  • The proposed methods offer improved model fit and deeper insights into response patterns.
  • The general fitting procedure enhances the applicability of hierarchical modeling.