Parsimonious asymmetric item response theory modeling with the complementary log-log link

Traditional item response theory (IRT) models assume a symmetric error distribution and rely on symmetric (logit or probit) link functions to model the response probabilities. As an alternative, we investigated the one-parameter complementary log-log model (CLLM), which is founded on an asymmetric error distribution and results in an asymmetric item response function with important psychometric properties. In a series of simulation studies, we demonstrate that the CLLM (a) is estimable in small sample sizes, (b) facilitates item-weighted scoring, and (c) accounts for the effect of guessing, despite the presence of a single parameter. We then provide further evidence for these claims by applying the CLLM to empirical data. Finally, we discuss how this work contributes to the growing psychometric literature on model complexity.