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.
Highlights
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 decompose the common variability across item and person clusters using item properties, person properties, or both [2,3]
Explanatory IRT models show how much of the total variance in item-level accuracy is due to between-item differences and betweenperson differences (English language learners vs. native speakers taking a reading test; female vs. male respondents completing a personality inventory)
Summary
Traditional item response theory (IRT) models enable researchers and practitioners to analyze response data from a measurement instrument such as a test, an attitude scale, or a psychological inventory and obtain both item information (e.g., difficulty, discrimination, and guessing) and persons’ levels of a latent trait being measured by the instrument. That is, these models produce unique parameters for each item and each person. Both the Rasch model and PCM are fully descriptive models that describe the variation within items through unique difficulty parameters and the variation among persons through unique person parameters representing trait (or ability) levels [1]
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