Nonparametric Item Response Models: A Comparison on Recovering True Scores

Modelos de Resposta ao Item Não-Paramétricos: Comparando a Recuperação de Escores Verdadeiros

Modelos De Respuesta al Item No-Paramétricos: Comparando la Recuperación de Puntuaciones Verdaderas

Abstract

Nonparametric procedures are used to add flexibility to models. Three nonparametric item response models have been proposed, but not directly compared: the Kernel smoothing (KS-IRT); the Davidian-Curve (DC-IRT); and the Bayesian semiparametric Rasch model (SP-Rasch). The main aim of the present study is to compare the performance of these procedures in recovering simulated true scores, using sum scores as benchmarks. The secondary aim is to compare their performances in terms of practical equivalence with real data. Overall, the results show that, apart from the DC-IRT, which is the model that performs the worse, all the other models give results quite similar to those when sum scores are used. These results are followed by a discussion with practical implications and recommendations for future studies.

Keywords:
Nonparametric item response model; Bayesian modeling; Monte Carlo simulation