Dr. Andrew Hayes
230 Lazenby Hall
1827 Neil Ave
- B.A., Psychology, San Jose State University, 1991
- Ph.D., Psychology, Cornell University, 1996
Quantitative Professor; Ph.D., Cornell University, 1996 - Resampling based methods of inference (such as bootstrapping and permutation tests), small sample data analysis, consequences of assumption violations in linear models as well as mediation and moderation analysis.
My primary research interests revolve around linear regression analysis and structural equation modeling. Recently, this research has focused on the estimation and interpretation of of intervening variable models ("mediation analysis") and models with interaction effects ("moderation analysis"), as well as their combination ("conditional process modeling"). I am also deeply interested in computationally-intensive statistical methods for inference, such as bootstrapping and randomization tests, and facilitating their implementation and use in popularly-used statistical software.
Hayes, A. F., & Rockwood, N. J. (in press). Conditional process analysis: Concepts, computation, and advances in the modeling of contingencies of mechanisms. American Behavioral Scientist.
Coutts, J. J., Hayes, A. F., & Jiang, T. (in press). Easy statistical mediation analysis with distinguishable dyadic data. Journal of Communication.
Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, interference, and interpretation. Communication Monographs, 85, 4-40.
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression based approach (2nd Edition). New York: The Guilford Press [Publishers page]
Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25, 76-81.
Hayes, A. F., & Rockwood, N. J. (2017). Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behaviour Research and Therapy.
Hayes, A. F. & Montoya, A. K. (2017). A tutorial on testing, visualizing, and probing interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures
Montoya, A. K., & Hayes, A. F. (2017). Two condition within-participant statistical mediation analysis: A path analytic framework. Psychological Methods
Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New York: The Guilford Press.
Hayes, A. F. (2015). An index of test of linear moderated mediation. Multivariate Behavioral Research, 50, 1-22.
Hayes, A. F., and Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470
Hayes, A. F., & Sharkow, M. (2013). The relative trustworthiness of tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24, 1918-1927.
Hayes, A. F. (2013). Conditional process modeling: Using structural equation modeling to examine contingent causal processes. In G. R. Hancock and R. O. Mueller (Eds.) Structural equation modeling: A second course (2nd Ed). Greenwich, CT: Information Age Publishing.
Hayes, A. F., Glynn, C. J., & Huge, M. E. (2012). Cautions regarding the interpretation of regression coefficients and hypothesis tests in linear models with interactions. Communication Methods and Measures, 6, 1-11.
Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45, 627-660.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408-420.
Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behavior Research Methods, 41, 924-936.
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.
Cai, L., & Hayes, A. F. (2008). A new test of linear hypotheses in OLS regression under heteroscedasticity of unknown form. Journal of Educational and Behavioral Statistics, 33, 21-40.
Hayes, A. F., & Cai, L. (2007). Using heteroscedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39, 709-722.
Hayes, A. F., & Cai, L. (2007). Further evaluating the validity of the conditional decision rule for comparing two independent means. British Journal of Mathematical and Statistical Psychology, 60, 217-244.
Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1, 77-89.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227.
Hayes, A. F. (2006). A primer on multilevel modeling. Human Communication Research, 32, 385-410.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36, 717-731.
Darlington, R. B., & Hayes, A. F. (2000). Combining independent p-values: Extensions of the Stouffer and binomial methods. Psychological Methods, 5, 496-515.
Hayes, A. F. (2000). Randomization tests and the homoscedasticity assumption when comparing group means. Animal Behaviour, 59, 653-656.
Hayes, A. F. (1998). Within-study meta-analysis: Pooling the significance of doubly-nonindependent ("nonoverlapping") correlations. Psychological Methods, 3, 32-45.
Hayes, A. F. (1997). Cautions in testing variance equality with randomization tests. Journal of Statistical Computation and Simulation, 59, 25-31.
Hayes, A. F. (1996). The permutation test is not distribution-free: Testing H0: rho = 0. Psychological Methods, 1, 184-198.