A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties

TL;DR

This paper introduces a new diagnostic classification model with properties similar to Rasch and 1PL models, offering reliable, invariant measurement for educational assessment.

Contribution

It proposes a one-parameter DCM with familiar measurement properties, bridging diagnostic classification and IRT models.

Findings

Model exhibits test score sufficiency

Measurement is item-free and person-free

Maintains invariant item and person ordering

Abstract

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent characteristics. These models are well-suited for providing diagnostic and actionable feedback to support formative assessment efforts. Several DCMs have been developed and applied in different settings. This study proposes a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate that the proposed DCM has measurement properties akin to the Rasch and 1-parameter logistic item response theory models, including test score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We discuss the implications and limitations of these developments, as well as directions for future research.