A Comparison of Bias Reduction Methods: Clustering versus Propensity Score Subclassification and Weighting

Autor/inn/en	D'Attoma, Ida; Camillo, Furio; Clark, M. H.
Titel	A Comparison of Bias Reduction Methods: Clustering versus Propensity Score Subclassification and Weighting
Quelle	In: Journal of Experimental Education, 87 (2019) 1, S.33-54 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (D'Attoma, Ida)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2017.1391161
Schlagwörter	Statistical Bias; Regression (Statistics); Probability; Weighted Scores; Classification; Quasiexperimental Design; Multivariate Analysis + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Classification system; Klassifikation; Klassifikationssystem; Multivariate Analyse
Abstract	Propensity score (PS) adjustments have become popular methods used to improve estimates of treatment effects in quasi-experiments. Although researchers continue to develop PS methods, other procedures can also be effective in reducing selection bias. One of these uses clustering to create balanced groups. However, the success of this new method depends on its efficacy compared to that of the existing methods. Therefore, this comparative study used experimental and nonexperimental data to examine bias reduction, case retention, and covariate balance in the clustering method, PS subclassification, and PS weighting. In general, results suggest that the cluster-based methods reduced at least as much bias as the PS methods. Under certain conditions, the PS methods reduced more bias than the cluster-based method, and under other conditions the cluster-based methods were more advantageous. Although all methods were equally effective in retaining cases and balancing covariates, other data-specific conditions may likely favor the use of a cluster-based approach. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01