Advertisement

Abstract

Multiple regression is a commonly used analytic method in the behavioral, educational, and social sciences because it provides a way to model a quantitative outcome variable from regressor variables.1 Multiple regression is an especially important statistical model to understand because special cases and generalizations of multiple regression are many of the most commonly used models in empirical research.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.Google Scholar
  2. Algina, J., & Olejnik, S. (2000). Determining sample size for accurate estimation of the Squared multiple correlation coefficient. Multivariate Behavioral Research, 35, 119–136.CrossRefGoogle Scholar
  3. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. The Journal of Personality and Social Psychology, 51, 1173–1182.CrossRefGoogle Scholar
  4. Cassady, J. C. (2001). The stability of undergraduate students’ cognitive test anxiety levels. Practical Assessment, Research & Evaluation, 7(20).Google Scholar
  5. Cassady, J. C. (2004). The influence of cognitive test anxiety across the learning-testing cycle. Learning and Instruction, 14(6), 569–592.CrossRefGoogle Scholar
  6. Cassady, J. C., & Holden, J. E. (2012). Manuscript currently being written.Google Scholar
  7. Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic procrastination. Contemporary Educational Psychology, 27, 270–295.CrossRefGoogle Scholar
  8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Erlbaum.Google Scholar
  9. Cohen, J, Cohen, P, West, S, Aiken, Leona S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum Associates.Google Scholar
  10. Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577.CrossRefGoogle Scholar
  11. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge.Google Scholar
  12. Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 243–259). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  13. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–Statistical controversy. Psychology, Public Policy, and Law, 2, 293–323.CrossRefGoogle Scholar
  14. Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment interventions. Evaluation Review, 5, 602–619.CrossRefGoogle Scholar
  15. Kahane, L. H. (2008). Regression basics (2nd ed.). Sage: Thousand Oaks, CA.Google Scholar
  16. Kelley, K. (2007a). Methods for the behavioral, educational, and social science: An R package. Behavior Research Methods, 39, 979–984.CrossRefGoogle Scholar
  17. Kelley, K. (2007b). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1–24.CrossRefGoogle Scholar
  18. Kelley, K. (2008). Sample size planning for the squared multiple correlation coefficient: Accuracy in parameter estimation via narrow confidence intervals. Multivariate Behavioral Research, 43, 524–555.CrossRefGoogle Scholar
  19. Kelley, K., & Lai, K. (2010). MBESS (Version 3.0 and greater) [computer software and manual]. Accessible from http://cran.r-project.org/web/packages/MBESS/.
  20. Kelley, K., & Maxwell, S. E. (2008). Power and accuracy for omnibus and targeted effects: Issues of sample size planning with applications to multiple regression. In P. Alasuuta, L. Bickman, & J. Brannen (Eds.), Handbook of social research methods (pp. 166–192). Newbury Park, CA: Sage.CrossRefGoogle Scholar
  21. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Earlbaum.Google Scholar
  22. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheet, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83–104.CrossRefGoogle Scholar
  23. Mendoza, J. L., & Stafford, K. L. (2001). Confidence intervals, power calculations, and sample size estimation for the squared multiple correlation coefficient under the fixed and random regression models: A computer program and useful standard tables. Educational and Psychological Measurement, 61, 650–667.CrossRefGoogle Scholar
  24. Mosier, C. I. (1951). Problems and designs of cross-validation. Educational and Psychological Measurement, 11, 5–11.CrossRefGoogle Scholar
  25. Mulaik, S. A. (2009). Linear causal modeling with structural equations. New York, NY. CRC Press.CrossRefGoogle Scholar
  26. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Harcourt Brace.Google Scholar
  27. Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93–115.CrossRefGoogle Scholar
  28. Rencher, A. C., & Schaalje, G. B. (2008). Linear models in statistics. John Wiley & Sons.Google Scholar
  29. Sarason, I. G. (1984). Stress, anxiety, and cognitive interference: Reactions to tests. Journal of Personality and Social Psychology, 46, 929–938.CrossRefGoogle Scholar
  30. Seber, G. A. F., & Wild, C. J. (1989). Nonlinear regression. New York, NY: John Wiley & Sons.CrossRefGoogle Scholar

Copyright information

© Sense Publishers 2013

Authors and Affiliations

  • Ken Kelley
  • Jocelyn H. H. Bolin

There are no affiliations available

Personalised recommendations