Behavior Research Methods

, Volume 41, Issue 3, pp 924–936 | Cite as

Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations

Article

Abstract

Researchers often hypothesize moderated effects, in which the effect of an independent variable on an outcome variable depends on the value of a moderator variable. Such an effect reveals itself statistically as an interaction between the independent and moderator variables in a model of the outcome variable. When an interaction is found, it is important to probe the interaction, for theories and hypotheses often predict not just interaction but a specific pattern of effects of the focal independent variable as a function of the moderator. This article describes the familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models and introduces macros for SPSS and SAS to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression. A script version of the SPSS macro is also available for users who prefer a point-and-click user interface rather than command syntax.

References

  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
  2. Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40, 373–400.CrossRefGoogle Scholar
  3. Bissonnette, V., Ickes, W., Bernstein, I., & Knowles, E. (1990). Personality moderating variables: A warning about statistical artifact and a comparison of analytic techniques. Journal of Personality, 58, 567–587.CrossRefGoogle Scholar
  4. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.Google Scholar
  5. Cronbach, L. J. (1987). Statistical tests for moderator variables: Flaws in analyses recently proposed. Psychological Bulletin, 102, 414–417.CrossRefGoogle Scholar
  6. Darlington, R. B. (1990). Regression and linear models. New York: McGraw-Hill.Google Scholar
  7. Hayes, A. F. (2005). Statistical methods for communication science. Mahwah, NJ: Erlbaum.Google Scholar
  8. Irwin, J. R., & McClelland, G. H. (2001). Misleading heuristics and moderated multiple regression models. Journal of Marketing Research, 38, 100–109.CrossRefGoogle Scholar
  9. Jaccard, J., & Turrisi, R. (2003). Interaction effects in multiple regression (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
  10. Johnson, P. O., & Fay, L. C. (1950). The Johnson-Neyman technique, its theory and application. Psychometrika, 15, 349–367.CrossRefPubMedGoogle Scholar
  11. Johnson, P. O., & Neyman, J. (1936). Tests of certain linear hypotheses and their application to some educational problems. Statistical Research Memoirs, 1, 57–93.Google Scholar
  12. Karpman, M. B. (1983). The Johnson-Neyman technique using SPSS or BMDP. Educational & Psychological Measurement, 43, 137–147.CrossRefGoogle Scholar
  13. Karpman, M. B. (1986). Comparing two non-parallel regression lines with the parametric alternative to analysis of covariance using SPSS-X or SAS—the Johnson-Neyman technique. Educational & Psychologicals Measurement, 46, 639–644.CrossRefGoogle Scholar
  14. Kromrey, J. D., & Foster-Johnson, L. (1998). Mean centering in moderated multiple regression: Much ado about nothing. Educational & Psychological Measurement, 58, 42–67.CrossRefGoogle Scholar
  15. MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19–40.CrossRefPubMedGoogle Scholar
  16. Newsom, J. T., Prigerson, H. G., Schulz, R., & Reynolds, C. F., III (2003). Investigating moderator hypotheses in aging research: Statistical, methodological, and conceptual difficulties with comparing separate regressions. International Journal of Aging & Human Development, 57, 119–150.CrossRefGoogle Scholar
  17. O’Connor, B. P. (1998). SIMPLE: All-in-one programs for exploring interactions in moderated multiple regression. Educational & Psychological Measurement, 58, 836–840.CrossRefGoogle Scholar
  18. Potthoff, R. F. (1964). On the Johnson-Neyman technique and some extensions thereof. Psychometrika, 29, 241–256.CrossRefGoogle Scholar
  19. Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational & Behavioral Statistics, 31, 437–448.CrossRefGoogle Scholar
  20. Reineke, J. B., & Hayes, A. F. (2007, November). Reporting on campaign finance success: Effects on perceptions of political candidates. Paper presented at the annual meeting of the National Communication Association, Chicago.Google Scholar
  21. Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307–321.CrossRefGoogle Scholar
  22. Stone-Romero, E. F., & Anderson, L. E. (1994). Relative power of moderated multiple regression and the comparison of subgroup correlation coefficients for detecting moderator effects. Journal of Applied Psychology, 79, 354–359.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2009

Authors and Affiliations

  1. 1.School of CommunicationOhio State UniversityColumbus
  2. 2.University of ZurichZurichSwitzerland

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