Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. (December 2022)

• Record Type: Journal Article
• Authors: Molenaar, Dylan; Cúri, Mariana; Bazán, Jorge L.
• Is Part Of: Journal of educational and behavioral statistics. Volume 47:Number 6(2022)
• Page Start: 693
• Page End: 735
• Publication Date: 2022-12
• Subjects: item response theory -- bounded data -- nonnormal data -- Bayesian statistics; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/10769986221108455
• Languages: English
• ISSNs: 1076-9986

Abstract

Bounded continuous data are encountered in many applications of item response theory, including the measurement of mood, personality, and response times and in the analyses of summed item scores. Although different item response theory models exist to analyze such bounded continuous data, most models assume the data to be in an open interval and cannot accommodate data in a closed interval. As a result, ad hoc transformations are needed to prevent scores on the bounds of the observed variables. To motivate the present study, we demonstrate in real and simulated data that this practice of fitting open interval models to closed interval data can majorly affect parameter estimates even in cases with only 5% of the responses on one of the bounds of the observed variables. To address this problem, we propose a zero and one inflated item response theory modeling framework for bounded continuous responses in the closed interval. We illustrate how four existing models for bounded responses from the literature can be accommodated in the framework. The resulting zero and one inflated item response theory models are studied in a simulation study and a real data application to investigate parameter recovery, model fit, and the consequences of fitting the incorrect distribution to the data. We find that neglecting the bounded nature of the data biases parameters and that misspecification of the exact distribution may affect the results depending on the data generating model.