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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models.

Biao Zeng¹, Hongbo Wen¹, Minjeong Jeon²

¹Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.

Multivariate Behavioral Research

|May 27, 2025

Summary

This summary is machine-generated.

This study presents new Unfolding Tree (UTree) models for Likert scale data analysis. These models accurately capture ideal point response processes and latent traits, outperforming existing methods when data aligns with the ideal point assumption.

Keywords:

Item response tree model dominance process extreme response style ideal point process latent traits likert scale unfolding tree model

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Area of Science:

Psychometrics
Statistical Modeling
Behavioral Science

Background:

Likert scale data analysis often relies on assumptions about response processes.
Existing Item Response Tree (IRTree) models may not fully capture ideal point response behaviors.
Developing robust analytical frameworks is crucial for understanding latent traits.

Purpose of the Study:

Introduce and evaluate three novel Unfolding Tree (UTree) models for Likert scale data.
Compare the performance of UTree models against established Item Response Tree (IRTree) models.
Investigate respondents' decision-making processes and latent trait structures.

Main Methods:

Developed three new Unfolding Tree (UTree) models based on the ideal point assumption.
Conducted simulation studies to assess model performance under various conditions.
Applied UTree and IRTree models to empirical data for comparative analysis.

Main Results:

Fit indices effectively distinguished between correct and incorrect models.
Both UTree and IRTree models accurately recovered parameters when correctly specified, with improved precision for larger sample sizes and more items.
Mis-specified models yielded biased individual parameter estimates, particularly for ideal point response processes.
Empirical data supported the ideal point response process over the dominance process.
Respondents' extreme response choices were primarily driven by target traits, not extreme response style.
Identified two distinct, moderately correlated target traits influencing decisions across stages.

Conclusions:

The proposed Unfolding Tree (UTree) models provide a valid framework for analyzing Likert scale data, especially when ideal point processes are present.
The ideal point assumption better explains respondents' decision-making and response patterns than the dominance process.
Findings underscore the importance of selecting appropriate models to accurately estimate latent traits and understand response behaviors.