Handling Missing Data: Analysis of a Challenging Data Set Using Multiple Imputation

Autor/inn/en	Pampaka, Maria; Hutcheson, Graeme; Williams, Julian
Titel	Handling Missing Data: Analysis of a Challenging Data Set Using Multiple Imputation
Quelle	In: International Journal of Research & Method in Education, 39 (2016) 1, S.19-37 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2014.979146
Schlagwörter	Data Analysis; Statistical Inference; Error of Measurement; Computation; Multivariate Analysis; Research Problems; Educational Research; Regression (Statistics); Least Squares Statistics; Longitudinal Studies; Surveys; Data Collection; Models; Case Studies; Foreign Countries; Mixed Methods Research; Questionnaires; United Kingdom (England) + Suchen Sie Ihr Suchwort? Auswertung; Inferential statistics; Schließende Statistik; Messfehler; Multivariate Analyse; Forschungskritik; Bildungsforschung; Pädagogische Forschung; Regression; Regressionsanalyse; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Survey; Umfrage; Befragung; Data capture; Datensammlung; Analogiemodell; Case study; Fallstudie; Case Study; Ausland; Fragebogen
Abstract	Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their implications for educational research. We illustrate the issues with an educational, longitudinal survey in which missing data was significant, but for which we were able to collect much of these missing data through subsequent data collection. We thus compare methods, that is, step-wise regression (basically ignoring the missing data) and MI models, with the model from the actual enhanced sample. The value of MI is discussed and the risks involved in ignoring missing data are considered. Implications for research practice are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01