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Autor/inn/enSchlomer, Gabriel L.; Bauman, Sheri; Card, Noel A.
TitelBest Practices for Missing Data Management in Counseling Psychology
QuelleIn: Journal of Counseling Psychology, 57 (2010) 1, S.1-10 (10 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0022-0167
DOI10.1037/a0018082
SchlagwörterMaximum Likelihood Statistics; Counseling Psychology; Researchers; Data Collection; Simulation; Research Methodology; Editing; Regression (Statistics); Computer Software
AbstractThis article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation. (Contains 1 table and 3 footnotes.) (As Provided).
AnmerkungenAmerican Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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