Estimating Explanatory Extensions of Dichotomous and Polytomous Rasch Models: The eirm Package in R

Abstract

Explanatory item response modeling (EIRM) enables researchers and practitioners to incorporate item and person properties into item response theory (IRT) models. Unlike traditional IRT models, explanatory IRT models can explain common variability stemming from the shared variance among item clusters and person groups. In this tutorial, we present the R package eirm, which provides a simple and easy-to-use set of tools for preparing data, estimating explanatory IRT models based on the Rasch family, extracting model output, and visualizing model results. We describe how functions in the eirm package can be used for estimating traditional IRT models (e.g., Rasch model, Partial Credit Model, and Rating Scale Model), item-explanatory models (i.e., Linear Logistic Test Model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses. In addition to demonstrating the general functionality of the eirm package, we also provide real-data examples with annotated R codes based on the Rosenberg Self-Esteem Scale.

Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data

“…We compared model fit indices and model variable coefficients to identify the most desired problem-solving style in TRE for participants. All EIRM analyses were implemented using the package eirm ( Bulut 2021 ; Bulut et al 2021 ) within the R computing environment ( R Core Team 2022 ). Tasks with varying numbers of response categories were handled by the polyreformat function of the eirm package.…”

“…Tasks with varying numbers of response categories were handled by the polyreformat function of the eirm package. Specifically, the polyreformat function transforms dichotomous and polytomous responses into a series of dummy-coded responses ( Bulut et al 2021 ). Figure 2 demonstrates how polytomous (i.e., task 1) and dichotomous response categories (i.e., task 2) are dichotomized in the new data set.…”

Validation of response processes in medical assessment using an explanatory item response model

“…The statistical analysis was performed with the use of SPSS software (SPSS for Windows, version 28.0) and R software, version 4.2.1 (R Foundation for Statistical Computing), using the eirm package for EIRMs [ 17 ]. (Additional file 2 ).…”

Editorial of the Psych Special Issue “Computational Aspects, Statistical Algorithms and Software in Psychometrics”

“…Bulut et al [15] is a tutorial paper of the eirm package that implements exploratory item response models. The functionality of the eirm package includes traditional item response models (e.g., Rasch model, partial credit model, and rating scale model), itemexplanatory models (i.e., a linear logistic test model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses.…”

“…The functionality of the eirm package includes traditional item response models (e.g., Rasch model, partial credit model, and rating scale model), itemexplanatory models (i.e., a linear logistic test model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses. Bulut et al [15] illustrate the general functionality of the eirm package with annotated R codes based on the Rosenberg self-esteem scale as a running empirical example.…”