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A Positive Trait Item Response Model, Joseph F. Lucke
A Positive Trait Item Response Model, Joseph F. Lucke
Joseph Lucke
All current models from item response theory (IRT) assume the latent trait follows a standard normal distribution. While this assumption is appropriate for traits such as ability or attitude, it creates both conceptual and technical problems traits such as addiction (alcohol, drugs, gambling). The distribution of an addiction trait is better assumed to be anchored at zero (no addiction) and positively skewed. A small change to the usual IRT model yields a class of positive-trait item response models (PTIRMs). I discuss PTIRMs and present one model in detail, including item characteristic curves and item information curves. I present an example …
Positive Trait Item Response Models, Joseph F. Lucke
Positive Trait Item Response Models, Joseph F. Lucke
Joseph Lucke
A new item response model is proposed for which the trait is positive. Three such models, the loglogistic, the log-normal, and the Weibull, are presented along with their item information curves. The data of seven addiction items from the DSM-IV from a study on alcohol addiction is analyzed by these three models using Bayesian Markov chain Monte Carlo methods. The item characteristic curves and item information curves are presented for all three models. The person scores for four item response patterns are presented for the log-logistic model.