IRT Model Selection Methods for Dichotomous Items

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A Latent Class Approach to Estimating Test-Score Reliability (2011, background): “Recently, Kang and Cohen (2007); Kang, Cohen, and Sung (2009); and Li, Cohen, Kim, and Cho (2009) evaluated several relative fit measures including AIC and BIC for choosing the correct item response theory model.”
On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, methodology): “Based on these results, one could argue for the RHO-RDINA model without Skill 3 as being the most parsimonious model, as indicated by the BIC (cf. Kang & Cohen, 2007; Li, Cohen, Kim, & Cho, 2009); however, the view here is that decisions about the Q-matrix should not be based on fit statistics…”
Model Selection Methods for Mixture Dichotomous IRT Models (2009, methodology): “In the absence of statistical tests of significance, one approach used in previous research (Kang & Cohen, 2007; Kang, Cohen, & Sung, 2005) for making comparisons among these kinds of statistics was to do so on common sets of data.”
Performances of LOO and WAIC as IRT Model Selection Methods (2017, methodology): “Kang and Cohen (2007) conducted a simulation study to compare the performances of the likelihood ratio test (LRT), AIC, BIC, DIC, and the cross-validation log-likelihood (CVLL; O’Hagan, 1995) in selecting the correct model among the one-parameter logistic model (1PLM), the two-parameter logistic…”