Paper Type |
Opinion |
Title |
A Comparison of Stepwise Multiple Regression and Hierarchical Multiple Regression |
Author |
Chupensri Wongbuddha and Putipong Bookkamana |
Email |
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Abstract: The stepwise multiple regression is a method of selecting independent variables (predictor variable) in order to construct the linear relation with the dependent variable (predicted variable). The independent variables, which have high correlation with the dependent variable, will have priority to enter the equation, which is beyond the control of the researcher or theoretical assumptions. Regularly, the researcher will use this method in exploratory research. The hierarchical multiple regression will be used in both exploratory and confirmatory researches. The hierarchical regression can control the independent variables or sets of independent variables entering the regression equation by the consideration of the researcher. Sometimes we use this method by entering the independent variables or sets of independent variables according to the theoretical assumptions. Nevertheless, many researchers use this method to explain the variation of the dependent variable after added the independent variable or sets of the independent variables as well.
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Start & End Page |
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Received Date |
1999-09-29 |
Revised Date |
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Accepted Date |
1999-11-12 |
Full Text |
Download |
Keyword |
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Volume |
Vol.26 No.2 (DECEMBER 1999) |
DOI |
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Citation |
Wongbuddha C. and Bookkamana P., A Comparison of Stepwise Multiple Regression and Hierarchical Multiple Regression, Chiang Mai J. Sci., 1999; 26(2): -. |
SDGs |
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