Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R2, sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity; however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).
Cite this paper
Akinwande, M. , Dikko, H. and Samson, A. (2015) Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics
, 754-767. doi: 10.4236/ojs.2015.57075
 Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. (2013) Applied Multiple Regression/Correlation Analysis for the Behavioral Science. Routledge, New York.
 Henard, D. (1998) Suppressor variable effects: Toward understandingan elusive data dynamic. SouthwestEducational Research Association, Houston.
 Morrow-Howell, N. (1994) The M Word: Multicollinearity in Multiple Regression. Social Work Research, 18, 247-251.
 Conger, A.J. (1974) A Revised Definition for Suppressor Variables: A Guide to Their Identification and Interpretation. Educational and Psychological Measurement, 34, 35-46. http://dx.doi.org/10.1177/001316447403400105
 McFatter, R.M. (1979) The Use of Structural Equation Models in Interpreting Regression Equations Including Suppressor and Enhancer Variables. Applied Psychological Measurement, 3, 123-135. http://dx.doi.org/10.1177/014662167900300113
 Courville, T. (2001) Use of Structure Coefficients in Published Multiple Regression Articles: β Is Not Enough. Educational & Psychological Measurement, 61, 229-248.
 Bertrand, P.V. (1988) A Quirk in Multiple Regression: The Whole Regression Can Be Greater than the Sum of Its Parts. The Statistician, 37, 371-374. http://dx.doi.org/10.2307/2348761
 Lutz, J.G. (1983) A Method for Constructing Data Which Illustrate Three Types of Suppressor Variables. Educational and Psychological Measurement, 43, 373-377. http://dx.doi.org/10.1177/001316448304300206
 Akinwande, M. O., Dikko H. G., &Gulumbe S. U (2015) Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. American Journal of Theoretical and Applied Statistics, 4, 414-419
 Velicer, W.F. (1978) Suppressor Variables and the Semipartial Correlation Coefficient. Educational and Psychological Measurement, 38, 953-958. http://dx.doi.org/10.1177/001316447803800415
 Walker, D.A. (2003) Suppressor Variable(s) Importance within a Regression Model: An Example of Salary Compression from Career Services. Journal of College Student Development, 44, 127-133. http://dx.doi.org/10.1353/csd.2003.0010
 Tzelgov, J. and Henik, A. (1991) Suppression Situations in Psychological Research: Definitions, Implications, and Applications. Psychological Bulletin, 109, 524-536. http://dx.doi.org/10.1037/0033-2909.109.3.524
 Rosenberg, M. (1973) The Logical Status of Suppressor Variables. Public Opinion Quarterly, 37, 359-372. http://dx.doi.org/10.1086/268098
 Nathans, L.L., Oswald, F.L. and Nimon, K. (2012) Interpreting Multiple Linear Regression: A Guidebook of Variable Importance. Practical Assessment, Research & Evaluation, 17, 123-136.
 Paulhus, D.L., Robins, R.W., Trzesniewski, K.H. and Tracy, J.L. (2004) Two Replicable Suppressor Situations in Personality Research. Multivariate Behavioral Research, 39, 303-328. http://dx.doi.org/10.1207/s15327906mbr3902_7
 Fox, J. (1991) Regression Diagnostics. Sage, Beverly Hills.
 MacNeill, S.E., Lichtenberg, P.A. and LaBuda, J. (2000) Factors Affecting Return to Living Alone after Medical Rehabilitation: A Cross-Validation Study. Rehabilitation Psychology, 45, 356-364. http://dx.doi.org/10.1037/0090-5522.214.171.1246
 Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. (2003) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Lawrence Erlbaum, Mahwah.
 Pedhazur, E.J. (1997) Multiple Regression in Behavioral Research: An Explanation and Prediction. Holt, Rinehart & Winston, New York.
 Smith, R.L., Ager Jr., J.W. and Williams, D.L. (1992) Suppressor Variables in Multiple Regression/Correlation. Educational and Psychological Measurement, 52, 17-29. http://dx.doi.org/10.1177/001316449205200102