Faculty

Related Links
KU Quantitative Program
Quantpsy.org

Research Interests
My research aims to bridge the gap between theory construction and model specification and assessment. Much of my research involves finding ways to improve upon the application of multivariate statistical techniques in order to address complex hypotheses with longitudinal and correlational data. Specific interests include factor analysis, structural equation modeling, multilevel (hierarchical linear) modeling, growth curve analysis, model fit, and the assessment of mediation and moderation effects.

One of my programs of research involves model evaluation and selection. After appropriate models are constructed to test theoretical predictions, models are evaluated or ranked in terms of fit and generalizability. The practice of model selection is crucial to the progress of science. However, little is known about the factors that determine the success or failure of a given model, or about the factors that influence the relative success of rival models.

A second program of research involves developing methods to address mediation and/or moderation hypotheses. Mediation analysis examines how changes in one variable can elicit changes in another variable through intervening variables. Moderation, on the other hand, involves modeling relationships among variables as functions of other variables.

My research philosophy is that quantitative psychology and applied research are symbiotic endeavors. The questions raised by psychologists in applied settings can lead to exciting advances in quantitative methods, and vice versa. I believe that advances in quantitative methodology are of limited value unless they are incorporated into routine practice by applied researchers. To foster synergy between quantitative and applied psychology, I participate in both methodological and substantive conferences, offer workshops to application-oriented audiences, conduct and publish research in methodological and applied journals, and create and maintain online resources to be used by applied researchers.

Selected Publications

Model evaluation and selection

Preacher, K. J., Cai, L., & MacCallum, R. C. (2007). Alternatives to traditional model comparison strategies for covariance structure models. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 33-62). Mahwah, NJ: Lawrence Erlbaum Associates.

Preacher, K. J. (2006). Quantifying parsimony in structural equation modeling. Multivariate Behavioral Research, 41, 227-259.

MacCallum, R. C., Browne, M. W., & Preacher, K. J. (2002). Comments on the Meehl-Waller procedure for appraisal of path analysis models. Psychological Methods, 7, 301-306.

Mediation and moderation

Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144-164.

Zhang, Z., Zyphur, M. J., & Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12, 695-719.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.

Preacher, K. J., & Hayes, A. F. (in press). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods.

Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. In A. F. Hayes, M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.

Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227.

Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448.

Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142-163.

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717-731.

Model specification

Zhang, G., Preacher, K. J., & Luo, S. (in press). Bootstrap confidence intervals for ordinary least squares factor loadings and correlations in exploratory factor analysis. Multivariate Behavioral Research.

Franke, G. R., Preacher, K. J., & Rigdon, E. E. (2008). Proportional structural effects of formative indicators. Journal of Business Research, 61, 1229-1237.

Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent growth curve modeling. Thousand Oaks, CA: Sage Publications.

Franke, G. R., Preacher, K. J., & Rigdon, E. E. (in press). Proportional structural effects of formative indicators. Journal of Business Research.

Little, T. D., Preacher, K. J., Selig, J. P., & Card, N. A. (2007). New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31, 357-365.

Preacher, K. J. (2006). Testing complex correlational hypotheses using structural equation modeling. Structural Equation Modeling, 13, 520-543.

Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178-192.

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding Statistics, 2, 13-32.

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.

Preacher, K. J., & MacCallum, R. C. (2002). Exploratory factor analysis in behavior genetics research: Factor recovery with small sample sizes. Behavior Genetics, 32, 153-161.

MacCallum, R. C., Browne, M. W., & Preacher, K. J. (2002). Comments on the Meehl-Waller procedure for appraisal of path analysis models. Psychological Methods, 7, 301-306.

MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.