Two-part sequential measurement models for distinguishing between symptom presence and symptom severity.

Two common aspects of symptom measurement are 1) the occurrence or presence of symptoms, and 2) the intensity or severity of symptoms when they occur. We adopt a latent trait perspective based on item response theory (IRT), using both unidimensional and multidimensional IRT models. We demonstrate how to (a) prepare data for analysis, (b) specify, estimate, and compare models, (c) interpret model parameters, (d) compare scores from models, and (e) visualize analysis results. We develop the relevant sequential IRT model, noting its flexibility, congruence with the theorized data generating process for symptom measures, and its promise for facilitating additional research and practical applications. The sequential model is less frequently used than other IRT models for polytomous data such as the generalized partial credit or graded response models. However, estimation of the sequential model can be readily accomplished with standard latent variable modeling and IRT software for binary indicators that allows constraints on the discrimination parameters. We compare the sequential model to other modeling options. We provide discussion of future research directions.