Estimating Heterogeneous Treatment Effects within Latent Class Multilevel Models: A Bayesian Approach

Autor/inn/en	Lyu, Weicong; Kim, Jee-Seon; Suk, Youmi
Titel	Estimating Heterogeneous Treatment Effects within Latent Class Multilevel Models: A Bayesian Approach
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 1, S.3-36 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lyu, Weicong) ORCID (Kim, Jee-Seon) ORCID (Suk, Youmi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221115446
Schlagwörter	Hierarchical Linear Modeling; Bayesian Statistics; Causal Models; Statistical Inference; Probability; Classification; Achievement Tests; Foreign Countries; Elementary Secondary Education; International Assessment; Science Achievement; Mathematics Tests; Mathematics Achievement; Science Tests; Computation; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Kausalanalyse; Inferential statistics; Schließende Statistik; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Classification system; Klassifikation; Klassifikationssystem; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz
Abstract	This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and outcome models so that misclassification does not obstruct estimation of treatment effects. Simulation demonstrates that the proposed method finds the correct number of latent classes, estimates class-specific treatment effects well, and provides proper posterior standard deviations and credible intervals of ATEs. We apply this method to Trends in International Mathematics and Science Study data to investigate the effects of private science lessons on achievement scores and then find two latent classes, one with zero ATE and the other with positive ATE. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01