Detecting latent variable non-normality through the generalized Hausman test
Guastadisegni, L., Moustaki, I.
, Vasdekis, V. & Cagnone, S.
(2023).
Detecting latent variable non-normality through the generalized Hausman test.
In
Wiberg, M., Molenaar, D., González, J., Kim, J. & Hwang, H.
(Eds.),
Quantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022
(pp. 107-118).
Springer Netherlands.
https://doi.org/10.1007/978-3-031-27781-8_10
, Vasdekis, V. & Cagnone, S.
(2023).
Detecting latent variable non-normality through the generalized Hausman test.
In
Wiberg, M., Molenaar, D., González, J., Kim, J. & Hwang, H.
(Eds.),
Quantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022
(pp. 107-118).
Springer Netherlands.
https://doi.org/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.