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Abstract
Although the all possible subsets regression procedure (or all possible regressions) has been a preferred method for selecting the “best” model in multiple regression, it might not have been the most frequently used method by SPSS users partly due to its time consuming nature of evaluating all possible combinations of multiple regression models. Starting with Version 19, however, IBM SPSS introduced a new procedure called Automatic Linear Modeling, enabling researchers to select best subsets automatically. While the arrival of this new procedure is highly welcomed by researchers, practitioners, and students, it has also raised a potential threat of misuse due to its apparent simplicity. The purpose of this paper is to provide brief information on all possible regressions and to provide a practical guide on how to make the best use of Automatic Linear Modeling.
Keywords
Regression, Model Fit, Variable Selection, Stepwise Selection, SPSS
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Oshima, T. Chris and Dell-Ross, Theresa, "All Possible Regressions Using IBM SPSS: A Practitioner’s Guide to Automatic Linear Modeling" (2016). Georgia Educational Research Association Conference. 1.
https://digitalcommons.georgiasouthern.edu/gera/2016/2016/1
Included in
All Possible Regressions Using IBM SPSS: A Practitioner’s Guide to Automatic Linear Modeling
Although the all possible subsets regression procedure (or all possible regressions) has been a preferred method for selecting the “best” model in multiple regression, it might not have been the most frequently used method by SPSS users partly due to its time consuming nature of evaluating all possible combinations of multiple regression models. Starting with Version 19, however, IBM SPSS introduced a new procedure called Automatic Linear Modeling, enabling researchers to select best subsets automatically. While the arrival of this new procedure is highly welcomed by researchers, practitioners, and students, it has also raised a potential threat of misuse due to its apparent simplicity. The purpose of this paper is to provide brief information on all possible regressions and to provide a practical guide on how to make the best use of Automatic Linear Modeling.
