Working with Missing Data in Higher Education Research: A Primer and Real-World Example

Autor/inn/en	Cox, Bradley E.; McIntosh, Kadian; Reason, Robert D.; Terenzini, Patrick T.
Titel	Working with Missing Data in Higher Education Research: A Primer and Real-World Example
Quelle	In: Review of Higher Education, 37 (2014) 3, S.377-402 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-5748
DOI	10.1353/rhe.2014.0026
Schlagwörter	Data Analysis; Statistical Inference; Research Problems; Computation; Error of Measurement; Multivariate Analysis; College Students; Colleges; Higher Education; Educational Research; Researchers; Predictor Variables; Maximum Likelihood Statistics; Research Methodology; Regression (Statistics) + Suchen Sie Ihr Suchwort? Auswertung; Inferential statistics; Schließende Statistik; Forschungskritik; Messfehler; Multivariate Analyse; Collegestudent; College; Hochschule; Fachhochschule; Hochschulbildung; Hochschulsystem; Hochschulwesen; Bildungsforschung; Pädagogische Forschung; Researcher; Forscher; Prädiktor; Research method; Forschungsmethode; Regression; Regressionsanalyse
Abstract	Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across 33 institutions to document how one's approach to handling missing data can substantially affect statistical conclusions, researcher interpretations, and subsequent implications for policy and practice. We conclude with straightforward suggestions for higher education researchers looking to select an appropriate method for handling missing data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10