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The Bayesian Multilevel Trifactor Item Response Theory Model.

Ken A Fujimoto¹

¹Loyola University Chicago, IL, USA.

Educational and Psychological Measurement

|May 21, 2019

Summary

This summary is machine-generated.

This study introduces a new Bayesian item response theory (IRT) model to handle complex nested method effects in psychometric analysis. The model accurately identifies multilevel trifactor structures, even with smaller sample sizes.

Keywords:

Bayesian IRT multidimensional IRT multilevel IRT nested method–source interaction trifactor structure

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Area of Science:

Psychometrics
Statistical Modeling

Background:

Item response theory (IRT) models typically control for cluster and method effects.
Existing IRT models lack the capacity to address nested method-source interactions where one source's effects vary across another's aspects.

Purpose of the Study:

To propose a novel Bayesian IRT model capable of accounting for nested method-source interactions.
To control for individual clustering within samples in psychometric analyses with complex method effects.

Main Methods:

Development of a Bayesian IRT model incorporating a multilevel trifactor structure for the latent trait space.
Specification of the model to manage nested method-source interactions and individual clustering.

Main Results:

Simulations confirm the model's ability to identify multilevel trifactor structures in data, even with small samples (N=250, clusters=50).
Failure to account for the trifactor structure can lead to inaccurate item discrimination estimates.

Conclusions:

The proposed Bayesian IRT model effectively addresses complex nested method-source interactions in psychometric data.
The model demonstrates utility in analyzing empirical data and provides accurate parameter recovery under specified conditions.