Using Propensity Score Weighting to Reduce Selection Bias in Large-Scale Data Sets

Autor/inn/en	Bishop, Crystal D.; Leite, Walter L.; Snyder, Patricia A.
Titel	Using Propensity Score Weighting to Reduce Selection Bias in Large-Scale Data Sets
Quelle	In: Journal of Early Intervention, 40 (2018) 4, S.347-362 (16 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1053-8151
DOI	10.1177/1053815118793430
Schlagwörter	Probability; Weighted Scores; Statistical Bias; Data Analysis; Early Intervention; Longitudinal Studies; Young Children; Computation; Quasiexperimental Design + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Auswertung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Frühe Kindheit
Abstract	Data sets from large-scale longitudinal surveys involving young children and families have become available for secondary analysis by researchers in a variety of fields. Researchers in early intervention have conducted secondary analyses of such data sets to explore relationships between nonmalleable and malleable factors and child outcomes, and to address issues of measurement. Survey data have been used to a lesser extent to examine plausible causal relationships between variables, perhaps due to the increased likelihood of selection bias that results with nonexperimental data. In this article, we use National Early Intervention Longitudinal Study data to demonstrate the use of inverse probability of treatment weighting, a quasi-experimental methodology based on propensity scores that can be used to reduce selection bias and examine plausible causal relationships. We discuss the advantages and disadvantages of this approach, and implications for its use in early intervention research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01