Partitioning Predicted Variance into Constituent Parts [electronic resource] : A Primer on Regression Commonality Analysis / Alfred J. Amado.
Commonality analysis is a method of decomposing the R squared in a multiple regression analysis into the proportion of explained variance of the dependent variable associated with each independent variable uniquely and the proportion of explained variance associated with the common effects of one or...
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| Format: | Electronic eBook |
| Language: | English |
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[S.l.] :
Distributed by ERIC Clearinghouse,
1999.
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| Summary: | Commonality analysis is a method of decomposing the R squared in a multiple regression analysis into the proportion of explained variance of the dependent variable associated with each independent variable uniquely and the proportion of explained variance associated with the common effects of one or more independent variables in various combinations. Unlike other variance partitioning methods (e.g., stepwise regression) that distort the results, commonality analysis considers all possible orders of entry into the model and does not depend on a priori knowledge to arrange the predictors. However, traditionally commonality analyses have been underutilized in research. The purpose of this paper is to introduce commonality analysis as an accurate and efficient method for partitioning variance. A data set is used to provide a heuristic example that explains the steps and guidelines necessary for performing a commonality analysis. Tables provide visual aids. (Contains 2 tables and 15 references.) (Author/SLD) |
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| Item Description: | ERIC Document Number: ED426099. ERIC Note: Paper presented at the Annual Meeting of the Southwest Educational Research Association (San Antonio, TX, January 21-23, 1999). |
| Physical Description: | 18 p. |