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Structured hierarchical regression for Likert scales including dispersion effects: Models and fitting tools
1Department of Statistics, Ludwig-Maximilians-Universitat Munchen.
Psychological Methods
|June 11, 2026
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.
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.
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