Programs for problems created by continuous variable distributions in moderated multiple regression

Organizational Research Methods

Volumn 9, Issue 4, 2006, Pages 554-567

  O'Connor, Brian P ^a  

^a LAKEHEAD UNIVERSITY   (Canada)

Author keywords

Field research; Moderated regression; Multivariate normality; Statistical interactions

Indexed keywords

EID: 33748706639     PISSN: 10944281     EISSN: 15527425     Source Type: Journal    
DOI: 10.1177/1094428106286984     Document Type: Article

References (38)

1. Aguinis, H. (1995). Statistical power problems with moderated multiple regression in management research. Journal of Management, 21, 1141-1158.
2. Aguinis, H. (2002). Estimation of interaction effects in organization studies. Organizational Research Methods, 5, 207-211.
3. Aguinis, H. (2004). Regression analysis for categorical moderators. New York: Guilford.
4. Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90, 94-107.
5. Aguinis, H., Boik, R. J., & Pierce, C. A. (2001). A generalized solution for approximating the power to detect effects of categorical moderator variables using multiple regression. Organizational Research Methods, 4, 291-323.
6. Aguinis, H., Bommer, W. H., & Pierce, C. A. (1996). Improving the estimation of moderating effects by using computer-administered questionnaires. Educational and Psychological Measurement, 56, 1043-1047.
7. Aguinis, H., Petersen, S. A., & Pierce, C. A. (1999). Appraisal of the homogeneity of error variance assumption and alternatives to multiple regression for estimating moderating effects of categorical variables. Organizational Research Methods, 2, 315-339.
8. Aguinis, H., & Stone-Romero, E. F. (1997). Methodological artifacts in moderated multiple regression and their effects on statistical power. Journal of Applied Psychology, 82, 192-206.
9. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.
10. Champoux, J. E., & Peters, W. S. (1987). Form, effect size, and power in moderated regression analysis. Journal of Occupational Psychology, 60, 243-255.
11. Chaplin, W. F. (1991). The next generation of moderator research in personality psychology. Journal of Personality, 59, 143-178.
12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence
13. Erlbaum. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.
14. Cortina, J. M. (1993). Interaction, nonlinearity and multicollinearity: Implications for multiple regression. Journal of Management, 19, 915-922.
15. Cortina, J. M., & DeShon, R. P. (1998). Determining relative importance of predictors with the observational design. Journal of Applied Psychology, 83, 798-804.
16. Edgington, E. S. (1995). Randomization tests (pp. 1-16, 33-37, 41-47). New York: Marcel-Dekker.
17. Efron, B. T., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.
18. Evans, M. G. (1985). A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis. Organizational Behavior and Human Decision Processes, 36, 305-323.
19. Evans, M. G. (1991). The problem of analyzing multiplicative composites: Interactions revisited. American Psychologist, 46 (1). 6-15.
20. Fisicaro, S. A., & Tisak, J. (1994). A theoretical note on the stochastics of moderated multiple regression. Educational and Psychological Measurement, 54, 32-41.
21. Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51 (1). 115-134.
22. Ganzach, Y. (1997). Misleading interaction and curvilinear terms. Psychological Methods, 2, 235-247.
23. Good, P. (2000). Permutation tests: A practical guide to resampling methods for testing hypotheses. New York: Springer-Verlag.
24. Hoyle, R. H., & Robinson, J. C. (2003). Mediated and moderated effects in social psychological research. In C. Sansone, C. Morf, & A. T. Panter (Eds.), Handbook of methods in psychology (pp. 213-233). Thousand Oaks, CA: Sage.
25. Judd, C. M., & McClelland, G. H. (1989). Data analysis: A model comparison approach. New York: Harcourt Brace Jovanovich.
26. Lubinski, D., & Humphreys, L. G. (1990). Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability. Psychological Bulletin, 107, 385-393.
27. MacCallum, R. C., & Mar, C. M. (1995). Distinguishing between moderator and quadratic effects in multiple regression. Psychological Bulletin, 118, 405-421.
28. Manly, B. F. J. (1997). Randomization, bootstrap and Monte Carlo methods in biology. London: Chapman & Hall.
29. May, R. B., Masson, M. E. J., & Hunter, M. A. (1989). Randomization tests: Viable alternatives to normal curve tests. Behavior Research Methods, Instruments, & Computers, 21, 482-483.
30. McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376-390.
31. Morris, J. H., Sherman, J. D., & Mansfield, E. R. (1986). Failures to detect moderating effects with ordinary least squares-moderated regression: Some reasons and a remedy. Psychological Bulletin, 99, 282-288.
32. Noreen, E. W. (1989). Computer-intensive methods for testing hypotheses: An introduction. New York: John Wiley.
33. Shepperd, J. A. (1991). Cautions in assessing spurious moderator effects. Psychological Bulletin, 110, 315-317.
34. Stone-Romero, E. F., Alliger, G. M., & Aguinis, H. (1994). Type II error problems in the use of moderated multiple regression for the detection of moderating effects of dichotomous variables. Journal of Management, 20, 167-178.
35. Stone-Romero, E. F., & Anderson, L. E. (1994). Relative power of moderated multiple regression and the comparison of subgroup correlation coefficients for detecting moderating effects. Journal of Applied Psychology, 79, 354-359.
36. Stone-Romero, E. F., & Liakhovitski, D. (2002). Strategies for detecting moderator variables: A review of theory and research. Research in Personnel and Human Resources Management, 21, 333-372.
37. ter Braak, C. J. F. (1992). Permutation versus bootstrap significance tests in multiple regression and ANOVA. In K.-H. Jackel, G. Rothe, & W. Sendler (Eds.), Bootstrapping and related resampling techniques (pp. 79-86). Berlin: Springer-Verlag.
38. Villa, J. R., Howell, J. P., Dorfman, P. W., & Daniel, D. L. (2003). Problems with detecting moderators in leadership research using moderated multiple regression. Leadership Quarterly, 14 (1). 3-23.