Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis

Akinwande Michael Olusegun; Hussaini Garba Dikko; Shehu Usman Gulumbe

doi:doi:10.11648/j.ajtas.20150405.22

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Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis

Akinwande Michael Olusegun, Hussaini Garba Dikko, Shehu Usman Gulumbe

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)

Received: 25 July 2015 Accepted: 6 August 2015 Published: 18 September 2015

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Abstract

In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)
DOI	10.11648/j.ajtas.20150405.22
Page(s)	414-419
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Stepwise Selection, Suppression Effect, Regressor Weights, Correlation

References

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[2]	Conger, A. J. (1974). A revised definition for suppressor variables: A guide to their identification and interpretation. Educational and Psychological Measurement , 35-46.
[3]	Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin , 161-182.
[4]	Horst, P. (1941). The prediction of personal adjustment. Social Science Research Council Bulletin , 431-436.
[5]	John, N., William, W., & Michael, H. K. (1983). Stepwise Selection. In N. John, W. William, & H. K. Michael, Applied Linear regression Models (pp. 430-434). Illinois: Richard D Irwin Inc.
[6]	Lancaster, B. P. (1999). Defining and interpreting suppressor effects: Advantages and limitations. Southwest Educational Research Association, San Antonio , 1-21.
[7]	Liebscher, G. (2012). A Universal Selection Method in Linear Regression Models. Open Journal of Statistics , 153-162.
[8]	Loukas, A. P. (2005). Early adolescent social and overt aggression: Examining the roles of social anxiety and maternal psychological control. Journal of Youth and Adolescence , 335-345.
[9]	Mendershausen, H. (1939). Clearing variates in confluence analysis. Journal of the American Statistical Association , 93-105.
[10]	Nathans, L. L. (2012). Interpreting Multiple Linear Regression: A Guidebook of Variable Importance. Practical Assessment, Research & Evaluation , 17, 123-136.
[11]	Pedhazur, E. J. (1997). Multiple regression in behavioral research. New York: Holt, Rinehart & Winston.
[12]	Shanta, P., & Williams, E. (2010). Suppressor Variables in Social Work Research: Ways to Identify in Multiple Regression Models. Journal of the Society for Social Work and Research , 28-40.
[13]	Shieh, G. (2006). Suppression situations in multiple linear regression. Educational and Psychological Measurement , 435-447.
[14]	Yao, J. (2013). Precision Analysis and Parameter Inversion in the Stepwise Deployment of a Mixed Constellation. Open Journal of Statistics , 390-397.

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APA Style

Akinwande Michael Olusegun, Hussaini Garba Dikko, Shehu Usman Gulumbe. (2015). Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. American Journal of Theoretical and Applied Statistics, 4(5), 414-419. https://doi.org/10.11648/j.ajtas.20150405.22

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ACS Style

Akinwande Michael Olusegun; Hussaini Garba Dikko; Shehu Usman Gulumbe. Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. Am. J. Theor. Appl. Stat. 2015, 4(5), 414-419. doi: 10.11648/j.ajtas.20150405.22

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AMA Style

Akinwande Michael Olusegun, Hussaini Garba Dikko, Shehu Usman Gulumbe. Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. Am J Theor Appl Stat. 2015;4(5):414-419. doi: 10.11648/j.ajtas.20150405.22

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@article{10.11648/j.ajtas.20150405.22,
  author = {Akinwande Michael Olusegun and Hussaini Garba Dikko and Shehu Usman Gulumbe},
  title = {Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {414-419},
  doi = {10.11648/j.ajtas.20150405.22},
  url = {https://doi.org/10.11648/j.ajtas.20150405.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.22},
  abstract = {In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.},
 year = {2015}
}

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TY  - JOUR
T1  - Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis
AU  - Akinwande Michael Olusegun
AU  - Hussaini Garba Dikko
AU  - Shehu Usman Gulumbe
Y1  - 2015/09/18
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.22
DO  - 10.11648/j.ajtas.20150405.22
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 414
EP  - 419
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.22
AB  - In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.
VL  - 4
IS  - 5
ER  -

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Author Information

Akinwande Michael Olusegun

Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Hussaini Garba Dikko

Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Shehu Usman Gulumbe

Department of Mathematics, Usman Danfodiyo University, Sokoto, Nigeria

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APA Style

Akinwande Michael Olusegun, Hussaini Garba Dikko, Shehu Usman Gulumbe. (2015). Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. American Journal of Theoretical and Applied Statistics, 4(5), 414-419. https://doi.org/10.11648/j.ajtas.20150405.22

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ACS Style

Akinwande Michael Olusegun; Hussaini Garba Dikko; Shehu Usman Gulumbe. Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. Am. J. Theor. Appl. Stat. 2015, 4(5), 414-419. doi: 10.11648/j.ajtas.20150405.22

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AMA Style

Akinwande Michael Olusegun, Hussaini Garba Dikko, Shehu Usman Gulumbe. Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. Am J Theor Appl Stat. 2015;4(5):414-419. doi: 10.11648/j.ajtas.20150405.22

Copy | Download

@article{10.11648/j.ajtas.20150405.22,
  author = {Akinwande Michael Olusegun and Hussaini Garba Dikko and Shehu Usman Gulumbe},
  title = {Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {414-419},
  doi = {10.11648/j.ajtas.20150405.22},
  url = {https://doi.org/10.11648/j.ajtas.20150405.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.22},
  abstract = {In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis
AU  - Akinwande Michael Olusegun
AU  - Hussaini Garba Dikko
AU  - Shehu Usman Gulumbe
Y1  - 2015/09/18
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.22
DO  - 10.11648/j.ajtas.20150405.22
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 414
EP  - 419
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.22
AB  - In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.
VL  - 4
IS  - 5
ER  -

Copy | Download