[1] | Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage. |
[2] | Belsley DA. Multicollinearity: (1976). Diagnosing its presence and assessing the potential damage it causes least square estimation. NBER Working Paper, No W0154. |
[3] | Belsley. D.A., E. Knu, and R.-. Welsh,. (1980). Regression Diagnostics: Identifying Influential Obserwations and Sources of Collinearity, Wiley, NY. |
[4] | Butler, N. and Denham, M,. (2000). The Peculiar Shrinkage Properties of Partial Least Squares Regression, Journal of the Royal Statistical Society Ser. B 62(3): pp.585-593. |
[5] | Carnes BA, Slade NA. (1988). The Use of Regression for Detecting Competition with Multicollinear Data. Ecology, 69 (4): pp.1266–1274. |
[6] | Cronbach, L. J. (1987). Statistical tests for moderator variables: Flaws in analyses recently proposed. Psychological Bulletin, 102, pp.414-41. |
[7] | Dohoo I.R., Ducrot C, Fourichon C., (1996). An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies. Preventive Veterinary Medicine. 29:pp.221–239. |
[8] | Echambadi, R., & Hess, J. D. (2007). Mean centering does not alleviate collinearity problems in moderated multiple regression models. Marketing Science, 26(3), 438–445. |
[9] | George, E. and Oman, S., (1996). Multiple-Shrinkage Principal Component Regression, The Statistician 45(1): pp.111-124. |
[10] | Glantz, S.A., Slinker, B.K., (2001). Primer of Applied Regression and Analysis of Variance. New York: McGraw-Hill. |
[11] | Harleen Kaur., (2017). Efficacy of Centering Techniques for Creating Interaction Terms in Multiple Regression for Modeling Brand Extension Evaluation. International Journal of Research, 4 (7), pp. 1422 -1436. |
[12] | Hoerl, A. E. and Kennard, R. W. (1970). Ridge Regression: Application to non-orthogonal problems. Technometrics, 12, pp. 69-82. |
[13] | Iacobucci, D., Schneider, M.J., Popovich, D.L. and Bakamitsos, G.A., (2017). Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux. Behavior research methods, 49(1), pp.403-404. |
[14] | Iacobucci, D., Schneider, M.J., Popovich, D.L. and Bakamitsos, G.A., (2016). Mean centering helps alleviate “micro” but not “macro” multicollinearity. Behavior research methods, 48 (4),pp.1308-1317.” |
[15] | Irwin, J. R., & McClelland, G. H. (2001). Misleading heuristics and moderated multiple regression models. Journal of Marketing Research, 38(February), 100–109. |
[16] | Jaccard, J., Wan, C. K., & Turrisi, R. (1990). The detection and interpretation of interaction effects between continuous variables in multiple regression. Multivariate Behavioral Research, 25(4), pp.467–478. |
[17] | Kromrey, J. D., & Foster-Johnson, L. (1998). Mean centering in moderated multiple regression: Much ado about nothing. Educational and Psychological Measurement, 58(1), pp.42–67. |
[18] | kraemer, C. H. and Blasey, C.M., (2005), “Centring in regression analyses: a strategy to prevent errors in statistical inference”, International Journal of Methods Psychiatric Research, 13(3). |
[19] | Marquardt, D. W. (1980). You should standardize the predictor variables in your regression models. Journal of the American Statistical Association, 75 (369), 87–91. |
[20] | Marquardt, D. W. and Snee, R. D. (1975). Ridge regression in practice. Amer. Statist., 29, 3-19. |
[21] | McClelland GH, Irwin JR, Disatnik D, Sivan L (2016). Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos. Behav Res Methods. Aug 16. [Epub ahead of print] PubMed PMID: 27531361. |
[22] | Mc Donald, G and Galarneau, D., (1975). A Monte Carlo Evaluation of Some Ridge-Type Estimators, Journal of the American Statistical Association 70(350): 407-416. |
[23] | Mc Donald, G., (1980). Some Algebraic Properties of Ridge Coefficient, Journal of the Royal Statistical Society Ser. B 42(1): 31-34. |
[24] | Micheal, H.K., Christopher, J. N., John, N., and William L. (2005). Applied Linear Statistical Models (5thed.). McGraw Hill. pp 294-331. |
[25] | Ostertagova, E., (2012). Modelling Using Polynomial Regression, Procedia Engineering 48:500- 506. |
[26] | Smith, Kent W., and M. S. Sasaki. (1979). Decreasing multicollinearity: A method for models with multiplicative functions. Sociological Methods and Research, 8 (August 1979): 35-56. |
[27] | Stewart GW. Collinearity and Least Square Regression. Statistical Science. (1987), 2(1): 68–94. |
[28] | Shieh, G. (2009). Detecting interaction effects in moderated multiple regression with continuous variables: Power and sample size considerations. Organizational Research Methods, 12(3), 510–528. |
[29] | Shieh, G. (2010). On the misperception of multicollinearity in detection of moderating effects: Multicollinearity is not always detrimental. Multivariate Behavioral Research, 45(3), 483–507. |
[30] | Shieh, G. (2011). Clarifying the role of mean centring in multicollinearity of interaction effects. British Journal of Mathematical and Statistical Psychology, 64, 462–477. |
[31] | Smith, K. W., & Sasaki, M. S. (1979). Decreasing multicollinearity: A method for models with multiplicative functions. Sociological Methods & Research, 8(1), 35–56. |
[32] | Stone, E. F., & Hollenbeck, J. R. (1984). Some issues associated with the use of moderated regression. Organizational Behavior and Human Performance, 34, 195–213. |
[33] | Tu YK, Kellett M, Clerehugh V. (2005). Problems of correlations between explanatory variables in multiple regression analyses in the dental literature. British Dental Journal; 199 (7):457–461. |
[34] | Vasu E.S., Elmore P.B., (1975). The Effect of Multicollinearity and the Violation of the Assumption of Normality on the Testing of Hypotheses in Regression Analysis. Presented at the Annual Meeting of the American Educational Research Association; Washington, D.C. March 30–April 3. |