The asymptotic power of the Lagrange multiplier tests for misspecified IRT models

Guastadisegni, Lucia, Cagnone, Silvia, Moustaki, Irini and Vasdekis, Vassilis (2021) The asymptotic power of the Lagrange multiplier tests for misspecified IRT models. In: Wiberg, Marie, Molenaar, Dylan, González, Jorge, Böckenholt, Ulf and Kim, Jee-Seon, (eds.) Quantitative Psychology: The 85th Annual Meeting of the Psychometric Society, Virtual. Springer Proceedings in Mathematics and Statistics. Springer Berlin / Heidelberg, Virtual, Online, 275 - 284. ISBN 9783030747718

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Abstract

This article studies the power of the Lagrange Multiplier Test and the Generalized Lagrange Multiplier Test to detect measurement non-invariance in Item Response Theory (IRT) models for binary data. We study the performance of these two tests under correct model specification and incorrect distribution of the latent variable. The asymptotic distribution of each test under the alternative hypothesis depends on a noncentrality parameter that is used to compute the power. We present two different procedures to compute the noncentrality parameter and consequently the power of the tests. The performance of the two methods is evaluated through a simulation study. They turn out to be very similar to the classic empirical power but less time consuming. Moreover, the results highlight that the Lagrange Multiplier Test is more powerful than the Generalized Lagrange Multiplier Test to detect measurement non-invariance under all simulation conditions.

Item Type:	Book Section
Official URL:	https://www.springer.com/gb/book/9783030747718
Additional Information:	© 2021 The Authors, under exclusive license to Springer Nature Switzerland AG
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics B Philosophy. Psychology. Religion > BF Psychology
Date Deposited:	09 Sep 2021 16:00
Last Modified:	18 Mar 2024 22:36
URI:	http://eprints.lse.ac.uk/id/eprint/111891

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