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Estimating the Nominal Response Model Under Nonnormal Conditions


The nominal response model (NRM), a much understudied polytomous item response theory (IRT) model, provides researchers the unique opportunity to evaluate within-item category distinctions. Polytomous IRT models, such as the NRM, are frequently applied to psychological assessments representing constructs that are unlikely to be normally distributed in the population. Unfortunately, models estimated using estimation software with the MML/EM algorithm frequently employs a set of normal quadrature points, effectively ignoring the true shape of the latent trait distribution. To address this problem, the current research implements an alternative estimation approach, Ramsay Curve Item Response Theory (RC-IRT), to provide more accurate item parameter estimates modeled under the NRM under normal, skewed, and bimodal latent trait distributions for ordered polytomous items. Based on the results of improved item parameter recovery under RC-IRT, it is recommended that RC-IRT estimation be implemented whenever a researcher considers the construct being measured has the potential of being nonnormally distributed. © The Author(s) 2013.

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