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A bifactor approach to subscore assessment.

David M Dueber¹, Michael D Toland²

¹Department of Educational, School, and Counseling Psychology, University of Kentucky.

Psychological Methods

|December 23, 2021

Summary

This study introduces new bifactor indices to assess the reliability and interpretability of subscores in multidimensional data. These statistical measures help determine if subscores offer meaningful insights beyond the total score.

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Area of Science:

Psychometrics
Statistical Modeling
Factor Analysis

Background:

Bifactor confirmatory factor analysis (CFA) models are used to justify unidimensional interpretations of multidimensional data.
The utility of bifactor indices for assessing subscore strength has not been previously explored.

Purpose of the Study:

To investigate the relationship between bifactor indices and the strength of subscores.
To develop guidelines for interpreting subscores based on bifactor model statistics.

Main Methods:

A simulation study was conducted to examine the predictive power of bifactor indices (OmegaHS, ECVSS) on subscore strength (OmegaS).
The influence of the number of factors on this relationship was also assessed.
Statistical cutoffs were derived for evaluating subscore interpretability.

Main Results:

Bifactor indices OmegaHS and ECVSS strongly predict subscore strength, conditional on OmegaS.
The number of factors plays a minor role in this prediction.
Specific cutoffs for OmegaHS and ECVSS were established for low and moderate subscore reliability to indicate added value.

Conclusions:

The study provides a framework for using bifactor CFA models to assess dimensionality and guide subscore interpretation.
High OmegaHS or ECVSS, conditional on OmegaS, can support the statistical appropriateness of interpreting subscores.
These findings extend the application of bifactor models in psychometric research.