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ERIC Number: ED450148
Record Type: Non-Journal
Publication Date: 2001-Feb
Pages: 26
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
A Primer on Regression and Canonical Commonality Analyses: Partitioning Predicted Variance into Constituent Parts.
Cool, Angela L.
Multiple regression analysis is used with considerable frequency by researchers as a means of predicting the impact of predictor variables on a dependent variable. Regression predictors are typically correlated, often intentionally. To better understand the relative contribution of each independent variable in regression (and other) analyses, researchers can partition the squared multiple correlation (R squared) into constituent portions that can be attributed to the independent variables both uniquely and in various combinations with each other. The purpose of this paper is to explain and illustrate the use of this "commonality analysis." A small heuristic data set is used to outline the steps in this approach. An appendix contains R squared results for four predictors of depression from the example. (Contains 4 tables and 27 references.) (Author/SLD)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A