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Detecting latent variable non-normality through the generalized Hausman test

Guastadisegni, Lucia, Moustaki, Irini, 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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Identification Number: 10.1007/978-3-031-27781-8_10


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: 19 May 2024 06:25

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