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.

## Selected Publications

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.