Non-normal Latent Traits and Parameter Estimation in the Bifactor Graded Response Model

Résumé fourni par la source

Item response theory (IRT) models usually assume that latent traits follow normal distributions. Nonetheless, violations of the normality assumption are not uncommon in psychology and psychiatry. While several studies have investigated the impact of non-normality on parameter estimation for some IRT models, bifactor models for polytomous data have been largely excluded despite their increasing popularity in psychology and education. Existing findings on other IRT models may not apply to bifactor models because of their unique characteristics. In this study, we examined the impact of latent trait non-normality on parameter estimation in the bifactor graded response model using a simulation study. The results indicate that (1) general-factor non-normality primarily affected item parameter estimates associated with the general factor but not specific factors, (2) specific-factor non-normality showed smaller and more localized effects, primarily on bias in specific-factor discrimination and intercept estimates; and (3) despite the misspecification of latent trait priors, maximum a posteriori estimates of latent traits tend to be more stable than the maximum likelihood estimates. Implications of the findings are discussed.

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