The Sensitivity of Small Area Estimates under Propensity Score Subclassification for Generalization

Autor/in	Chan, Wendy
Titel	The Sensitivity of Small Area Estimates under Propensity Score Subclassification for Generalization
Quelle	In: Journal of Research on Educational Effectiveness, 15 (2022) 1, S.178-215 (38 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chan, Wendy)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2021.1938312
Schlagwörter	Generalization; Probability; Sample Size; Statistical Analysis; Computation; Classification + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Statistische Analyse; Classification system; Klassifikation; Klassifikationssystem
Abstract	Over the past decade, statisticians have developed methods to improve generalizations from nonrandom samples using propensity score methods. While these methods contribute to generalization research, their effectiveness is limited by small sample sizes. Small area estimation is a class of model-based methods that address the imprecision due to small samples and prior research has shown its potential to improve precision in generalization studies with small samples. However, the validity of both small area methods and existing design-based methods depend on the assumptions of propensity score models, leaving open the question of whether a specific method is preferred when core assumptions are violated. This study compares the performance of design-based and model-based estimators of population parameters in generalization studies when the assumptions for propensity score models are violated. Using a simulation study and an empirical example, we assess the sensitivity of the estimators under various degrees of violations in assumptions, discuss the advantages and tradeoffs of each approach, and highlight the implications for generalization research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01