What Would It Take to Change an Inference? Using Rubin's Causal Model to Interpret the Robustness of Causal Inferences

Autor/inn/en	Frank, Kenneth A.; Maroulis, Spiro J.; Duong, Minh Q.; Kelcey, Benjamin M.
Titel	What Would It Take to Change an Inference? Using Rubin's Causal Model to Interpret the Robustness of Causal Inferences
Quelle	In: Educational Evaluation and Policy Analysis, 35 (2013) 4, S.437-460 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3737
DOI	10.3102/0162373713493129
Schlagwörter	Causal Models; Inferences; Research Methodology; Robustness (Statistics); Educational Research; Statistical Analysis; Reading Achievement; Kindergarten; Academic Persistence; Comparative Analysis; Equations (Mathematics); Reading Programs; Test Bias + Suchen Sie Ihr Suchwort? Kausalanalyse; Inference; Inferenz; Research method; Forschungsmethode; Widerstandsfähigkeit; Bildungsforschung; Pädagogische Forschung; Statistische Analyse; Leseleistung; Equations; Mathematics; Gleichungslehre; Testkritik
Abstract	We contribute to debate about causal inferences in educational research in two ways. First, we quantify how much bias there must be in an estimate to invalidate an inference. Second, we utilize Rubin's causal model to interpret the bias necessary to invalidate an inference in terms of sample replacement. We apply our analysis to an inference of a positive effect of Open Court Curriculum on reading achievement from a randomized experiment, and an inference of a negative effect of kindergarten retention on reading achievement from an observational study. We consider details of our framework, and then discuss how our approach informs judgment of inference relative to study design. We conclude with implications for scientific discourse. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10