Detecting latent variable non-normality through the generalized Hausman test

Guastadisegni, Lucia, Moustaki, Irini ORCID: 0000-0001-8371-1251, Vasdekis, Vassilis and Cagnone, Silvia (2023) Detecting latent variable non-normality through the generalized Hausman test. In: Wiberg, Marie, Molenaar, Dylan, González, Jorge, Kim, Jee-Seon and Hwang, Heungsun, (eds.) Quantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022. Springer Proceedings in Mathematics and Statistics. Springer Netherlands, pp. 107-118. ISBN 9783031277801

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Abstract

This paper extends the generalized Hausman test to detect non-normality of the latent variable distribution in unidimensional IRT models for binary data. To build the test, we consider the estimator obtained from the two-parameter IRT model, that assumes normality of the latent variable, and the estimator obtained under a semi-nonparametric framework, that allows for a more flexible latent variable distribution. The behaviour of the test is evaluated through a simulation study. The results highlight the good performance of the test in terms of both Type I error rates and power with many items and large sample sizes.

Item Type:	Book Section
Additional Information:	© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics H Social Sciences > HA Statistics
Date Deposited:	28 Jul 2023 13:48
Last Modified:	11 Dec 2024 18:09
URI:	http://eprints.lse.ac.uk/id/eprint/119864

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