UCLA

It is generally assumed that the latent trait is normally distributed in the population when estimating logistic item response theory (IRT) model parameters. This assumption requires that the latent trait be fully continuous and the population homogenous (i.e., not a mixture). When this normality assumption is violated, models are misspecified, and item and person parameter estimates are inaccurate. When normality cannot be assumed, it might be appropriate to consider alternative modeling approaches: (a) a zero-inflated mixture, (b) a log-logistic, (c) a Ramsay curve, or (d) a heteroskedastic-skew model. The first 2 models were developed to address modeling problems associated with so-called quasi-continuous or unipolar constructs, which apply only to a subset of the population, or are meaningful at one end of the continuum only. The second 2 models were developed to address non-normal latent trait distributions and violations of homogeneity of error variance, respectively. To introduce these alternative IRT models and illustrate their strengths and weaknesses, we performed real data application comparing results to those from a graded response model. We review both statistical and theoretical challenges in applying these models and choosing among them. Future applications of these and other alternative models (e.g., unfolding, diffusion) are needed to advance understanding about model choice in particular situations.