Regularized Variational Estimation for Exploratory Item Factor Analysis

Autor/inn/en	Cho, April E.; Xiao, Jiaying; Wang, Chun; Xu, Gongjun
Titel	Regularized Variational Estimation for Exploratory Item Factor Analysis
Quelle	(2022), (62 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Factor Analysis; Item Response Theory; Mathematics; Computation; Longitudinal Studies; Monte Carlo Methods; Factor Structure; Statistical Distributions; National Education Longitudinal Study of 1988 (NCES) + Suchen Sie Ihr Suchwort? Faktorenanalyse; Item-Response-Theorie; Mathematik; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Monte-Carlo-Methode; Faktorenstruktur; Wahrscheinlichkeitsverteilung
Abstract	Item factor analysis (IFA), also known as Multidimensional Item Response Theory (MIRT), is a general framework for specifying the functional relationship between a respondent's multiple latent traits and their response to assessment items. The key element in MIRT is the relationship between the items and the latent traits, so-called item factor loading structure. The correct specification of this loading structure is crucial for accurate calibration of item parameters and recovery of individual latent traits. This paper proposes a regularized Gaussian Variational Expectation Maximization (GVEM) algorithm to efficiently infer item factor loading structure directly from data. The main idea is to impose an adaptive L[subscript 1]-type penalty to the variational lower bound of the likelihood to shrink certain loadings to 0. This new algorithm takes advantage of the computational efficiency of GVEM algorithm and is suitable for high-dimensional MIRT applications. Simulation studies show that the proposed method accurately recovers the loading structure and is computationally efficient. The new method is also illustrated using the National Education Longitudinal Study of 1988 (NELS:88) mathematics and science assessment data. [This paper will be published in "Psychometrika."] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01