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ERIC Number: ED534385
Record Type: Non-Journal
Publication Date: 2007-Feb-7
Pages: 30
Abstractor: As Provided
Reference Count: 23
ISBN: N/A
ISSN: N/A
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
Online Submission, Paper presented at the Annual Meeting of the Southwest Educational Research Association (San Antonio, TX, Feb 2007)
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested in explaining the most variability in the dependent variable with the fewest possible predictors, perhaps as part of a cost analysis. Two approaches to determining the quality of predictors are (1) stepwise regression and (2) hierarchical regression. This paper will explore the advantages and disadvantages of these methods and use a small SPSS dataset for illustration purposes. Issues of (a) use of degrees of freedom, (b) identification of best predictor set of a prespecified size, and (c) replicability will be discussed. Several recent cases of hierarchical regression analysis in research will be presented and examples of when hierarchical regression may be used will be discussed. The following are appended: (1) Heuristic Regression Dataset; and (2) SPSS Syntax to Analyze Appendix A Data. (Contains 3 tables.)
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A