Exploring Approaches for Estimating Parameters in Cognitive Diagnosis Models with Small Sample Sizes

Abstract

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters.

Editorial for the Special Issue “Computational Aspects and Software in Psychometrics II”

“…The article by Sorrel, Escudero, Nájera, Kreitchmann, and Vázquez-Lira [9], titled "Exploring approaches for estimating parameters in cognitive diagnosis models with small sample sizes", compares different estimation methods for cognitive diagnostic models (CDM, also referred to as diagnostic classification models (DCM)) in small samples. The study found that alternative estimation methods should be preferred over the usually employed marginal maximum likelihood (MML) estimation approach when estimating CDMs in small samples.…”