Comparison of Relative Fit Indices for Diagnostic Model Selection

Sedat Şen · Laine P. Bradshaw

41 citations0/0 files storedApplied Psychological Measurement

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Psychometric Methodologies and Testing Educational Assessment and Pedagogy Statistical Methods and Bayesian Inference

Abstract

The purpose of this study was to thoroughly examine the performance of three information-based fit indices-Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample-size-adjusted BIC (SABIC)-using the log-linear cognitive diagnosis model and a set of well-known item response theory (IRT) models. Two simulation studies were conducted to examine the extent to which relative fit indices can identify the generating model under a variety of data conditions and model misspecifications. Generally, indices performed better when item quality was stronger. When the IRT was the generating model, all three indices correctly selected the IRT model for all replications. When the true model was a diagnostic classification model, for all three fit indices, the multidimensional IRT model was incorrectly selected as frequently as 70% of the replications. The results of this study identify situations for researchers where commonly used-and typically well-performing-fit indices may not be appropriate to compare models for selection.

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2020The Science and Practice of Item Response Theory in Organizations 2021Inferring the Number of Attributes for the Exploratory DINA Model 2021Sample Size Requirements for Applying Diagnostic Classification Models

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