ijmeb Uluslararası Yönetim İktisat ve İşletme Dergisi 2147-9208 2147-9194 Zonguldak Bülent Ecevit Üniversitesi 10.17130/ijmeb.2017228690 DETERMINATION OF SUPPRESSION EFFECT AND COMPARISON OF INDEPENDENT VARIABLE’S RELATIVE IMPORTANCE IN MANAGEMENT SCIENCES AND MARKETING YÖNETİM BİLİMLERİ VE PAZARLAMA ALANINDA BAĞIMSIZ DEĞİŞKENLERİN KARŞILAŞTIRILMASI VE BASTIRICI ETKİ TESPİTİ Dogan Volkan Eskişehir Osmangazi Üniversitesi, İİBF Yilmaz Cengiz Orta Doğu Teknik Üniversitesi, İİBF 04 01 2017 13 2 385 406 Copyright © 2005, Uluslararası Yönetim İktisat ve İşletme Dergisi 2005 Uluslararası Yönetim İktisat ve İşletme Dergisi

The structure coefficients, Pratt measure, APS regression, commonality analysis, dominance analysis, and relative importance weights analysis was implemented in this study regarding the comparison of the levels of the prediction of dependent variables by the independent variables within the multiple linear regression models. Analysis carried out via R software, the determination of the suppression effect problem has been carried out. In addition, some implications have been provided regarding the determination of the variance level calculated in bias due to the suppression effect and the multi-collinearity problem and the statistical comparison of β coefficients of the independent variables in this study

Bu çalışmada; çoklu doğrusal regresyon modellerinde yer alan bağımsız değişkenlerinbağımlı değişkeni yordama düzeylerinin yani etki büyüklüklerinin β karşılaştırılmasına,bastırıcı etkinin tespitine ve bağımsız değişkenlerin korelasyon halinde olması durumuna ilişkinolarak R 3.0.2 yazılımı aracılığıyla; structure coefficients, pratt measure, APS regresyon allpossible subset regression , commonality analysis, dominance analysis, relative importanceweights analizlerinin uygulanması gerçekleştirilmiştir. R 3.0.2 yazılımı aracılığıylagerçekleştirilen ilgili analizler kapsamında, bastırıcı etki sorunu tespiti uygulamalı olarakgerçekleştirilmiştir. Ayrıca çalışma neticesinde bastırıcı etkiden ve çoklu-bağıntı sorunundankaynaklanarak yanlı olarak hesaplanan varyans miktarının tespitine ilişkin ve bağımsızdeğişkenlerin etki büyüklüklerinin β istatistiksel olarak karşılaştırılmasına ilişkin çıkarımlar sağlanmıştır.

 Çoklu Doğrusal Regresyon Etki Büyüklükleri Karşılaştırma Bastırıcı Değişken Multiple Linear Regressions Comparison of Β Coefficients Suppression Effect Abdi, H. (2007). Part (semi partial) and partial regression coefficients. In salkind, N. J. (Eds.), Encyclopedia of measurement and statistics (pp. 736-740). Thousand Oaks, CA: Sage. Aiken, L. S., West, S. G., & Pitts, S. C. (2003). Multiple linear regression. In Schinka, J. A., Velicer, N. F. (Eds.), Research Methods in Psychology. New Jersey: John Wiley & Sons. Ailawadi, K. L., Neslin, S. A., & Gedenk, K. (2001). Pursuing the value-conscious consumer: Store brands versus national brand promotions. Journal of Marketing, 65(January), 71-89. Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8, 129-148. Bagozzi, R. P. (1980). Performance and satisfaction in an ındustrial sales force: An examination of their antecedents and simultaneity. Journal of Marketing, 44(Spring), 65-77. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(2), 74-94. Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114, 542-551. Canty, A., & Ripley, B. (2011). Boot: Bootstrap R (S-Plus) functions (R package version 1.3-2). Erisim tarihi: 12.1.2016, https://scholar.google.com/scholar?q=boot%3A+Bootstrap+R+%2 8S-Plus%29+functions+ripley&btnG=&hl=en&as_sdt=0%2C23. Capraro, R. M., & Capraro, M. M. (2001). Commonality analysis: Understanding variance contributions to overall canonical correlation effects of attitude toward mathematics on geometry achievement. Multiple Linear Regression Viewpoints, 27, 16-23. Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65, 81-93. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences. New Jersey, U.S: Hillsdale. Courville, T., & Thompson, B. (2001). Use of structure coefficients in published multiple regression articles: β is not enough. Educational & Psychological Measurement, 61, 229-248. Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161-182. Field, A. (2013). Discovering statistics using IBM SPSS Statistics. London, UK: Sage. Genizi, A. (1993). Decomposition of R2 in multiple regression with correlated regressors. Statistica Sinica, 3, 407-420. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Prentice Hall, NJ: Englewood Cliffs. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). Pls-sem: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151. Henard, D. H. (1998). Suppressor variable effects: Toward understanding an elusive data dynamic. Paper presented at the annual meeting of the Southwest Educational Research Association, Houston, TX. Henson, R. K. (2002). The logic and interpretation of structure coefficients in multivariate general linear model analyses. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA. Heppner, P. P., Kivlighan, D. M., & Wampold, B. E. (1992). Research design in counseling. Brooks/ Cale, CA: Pacific Grove. Horst, P. (1941). The prediction of personnel adjustment. Social Science Research and Council Bulletin, 48, 431-436. Huck, S. (2011). Reading statistics and research. Boston, MA: Pearson Education. Kraha, A., Turner, H., Nimon, K., Zientek, L., & Henson, R. (2012). Tools to support interpreting multiple regression in the face of multicollinearity. Frontiers in Psychology, 3(44), 1-16. Lancaster, B. P. (1999). Defining and interpreting suppressor effects: Advantages and limitations. In Thompson, B. (Ed.), Advances In Social Science Methodology (pp. 139-148), Stamford, CT: JAI Press. Linacre, J. M. (1993, April). Generalizability theory and many facet Rasch measurement. Paper presented at the Annual Meeting of the American Educational Research Association, Atlanta, GA. Nimon, K., & Roberts, J. K. (2009). Yhat: Interpreting regression effects (R Package Version 1.0-3). Erisim tarihi: 12.1.2016, http://CRAN.R-project.org/package=yhat. Nimon, K., & Reio, T. (2011). Regression commonality analysis: A technique for quantitative theory building. Human Resource Development Review, 10, 329-340. Nimon, K., & Roberts, J. K. (2012). Yhat: Interpreting regression effects (R package version 1.0-5) [Computer software]. Erisim tarihi: 12.1.2016, http://CRAN.R-project.org/package=yhat. Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficient. Organizational Research Methods, 16(4), 650-674. Onwuegbuzie, A. J., & Daniel, L. G. (2003). Typology of analytical and interpretational errors in quantitative and qualitative educational research. Current Issues in Education, 6(2). Pandey, S., & Elliot, W. (2010). Suppressor variables in social work research: Ways to ıdentify in multiple regression models. Journal of the Society for Social Work and Research, 1(1), 28-40. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. Fort Worth, TX:HarcourtBrace. Pratt, J. W. (1987). Dividing the indivisible: Using simple symmetry to partition variance explained. In Pukkila, T., Puntanen, S. (Eds.), Second International Tampere Conference in Statistics (pp. 245-260). Finland: Tampere. Preacher, K., & Leonardelli, G. (2003). Calculation for the Sobel test: An interactive calculation tool for mediation tests. Erisim tarihi: 12.1.2016, http://www.unc.edu/preacher/sobel/sobel. htm. Salinas, E. A., & Perez, J. M. P. (2009). Modeling the brand extensions’ influence on brand image. Journal of Business Research, 62, 50-60. Seber, G. A. F., & Wild, C. J. (1989). Nonlinear regression. New York: Wiley. Sharma, S., Durand, R. M., & Gurarie, O. (1981). Identification and analysis of moderator variables. Journal of Marketing Research, 18(3), 291-300. Tabachnick, B. G., & Fidell, L. S. (2011). Using multivariate statistics. Boston: Pearson Education. Thomas, D. R., Hughes, E., & Zumbo, B. D. (1998). On variable importance in linear regression. Social Indicators Research, 45, 253-275. Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York, US: Guilford Press. Tranmer, M., & Elliot, M. (2008). Multiple linear regression. Erisim tarihi: 12.1.2016, http://www. cmist.manchester.ac.uk/medialibrary/archive-publications/working-papers/2008/2008-19- multiple-linear-regression.pdf. Woolley, K. K. (1997). How variables uncorrelated with the dependent variable can actually make excellentn predictors: The important suppressor variable case. Paper Presented at the Annual Meeting of the Southwest Educational Research Association, Austin, TX.