University of Calgary

Andrew Hayes

  • Professor

Research Interests

Organizational Behaviour and Human Resources:

Bio

Andrew F. Hayes is a quantitative methodologist and holds a PhD in Psychology from Cornell University as well as a BA in Psychology from San Jose State University.  His research and writing on applied statistical methods has been published in such journals as Psychological Methods, Multivariate Behavioral Research, Behavior Research Methods, British Journal of Mathematical and Statistical Psychology, Psychological Science, Journal of Educational and Behavioral Statistics, American Behavioral Scientist, Communication Monographs, Journal of Communication, and Australasian Marketing Journal, among many others.   

He is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis (2018) and Regression Analysis and Linear Models (2017), both published by The Guilford Press, and Statistical Methods for Communication Science (2005), published by Routledge.  He also invented the PROCESS macro for SPSS, SAS, and R (processmacro.org), widely used by researchers examining the mechanisms and contingencies of effects.  He teaches courses on applied data analysis and also conducts online and in-person workshops on statistical analysis to multidisciplinary audiences throughout the world, most frequently to faculty and graduate students in business schools but also in education, psychology, social work, communication, public health, and researchers in government.  His work has been cited over 125,000 times according to Google Scholar, and he has been designated a Highly Cited Researcher by Clarivate Analytics in 2019 and 2020. He can be located in cyberspace at www.afhayes.com.

Representative Publications

Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than Cronbach's alpha for quantifying reliability. But... Communication Methods and Measures, 14, 1-24.

Hayes, A. F., & Rockwood, N. J. (2020). Conditional process analysis: Concepts, computation, and advances in the modeling of the contingencies of mechanisms. American Behavioral Scientist, 16, 19-54.

Coutts, J. J., Hayes, A. F., & Jiang, T. (2019). Easy statistical mediation analysis with distinguishable dyadic data. Journal of Communication, 69, 612-649.

Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs85, 4-40.

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based perspective (2nd edition). New York, NY: The Guilford Press.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, application, and implementationNew York: The Guilford Press.

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, 98, 39-57. 

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., & Montoya, A. K. (2017). A tutorial on testing, visualizing, and probing interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11, 1-30 

Montoya, A. K., & Hayes, A. F. (2017). Two condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22, 6-27. 

Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50, 1-22.

Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470.

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