Mechanisms and Contingencies Lab
The work of the Mechanisms and Contingencies (MAC) Lab is guided by the principle that causal effects are best understood by establishing how those effects operate and the circumstances that facilitate or hinder them. That is, deeper understanding of a phenomenon is enhanced by quantification of and inference about the process or processes underlying a causal effect and its contingencies. Thus, the MAC lab focuses its work on developing, evaluating, and disseminating research and practical statistical methods and tools useful for understanding the processes by which causal effects operate (mediation) and the circumstances, contexts, or individual differences that influence the magnitude of those effects (moderation).
Mediation and moderation analysis have become staples of the curriculum in the graduate programs of disciplines that rely on social science methodologies. As a result, research in this area has exploded, and new methods are becoming increasingly sophisticated and precise but sometimes require a level of mathematical background to understand them or programming skills to implement them that many substantive researchers do not have. Most substantive researchers are too busy doing the work of the business to dedicate important resources to keeping up with all the nuances in methodology, statistical programming, and the like. For this reason, our work and writing is guided by the needs of the final user in mind rather than the expert methodologist. Members of the lab recognize that new methods take hold in a discipline when they are implemented in software that is widely used and are communicated to researchers in language that doesn't require advanced training in mathematics or statistics. We focus on data analysis problems substantive researchers encounter while offering statistical tools (usually in the form of macros or code) that make them easy to put into practice with software that most researchers are already familiar with, without requiring the expertise, guidance, or knowledge of a statistician or computer scientist.
Although the traditional peer reviewed journal article is and will always be an important means of disseminating the work of the MAC lab, length restrictions imposed by most journals often reduce how much detail and practical training can be provided through this medium. Outreach is an important component of this lab, and we are always happy to offer advice through email to people grappling with the implementation of methods described in our work. Indeed, such questions often inform us of holes in the literature and needs that exist that we may not be aware of. We also offer short courses and workshops as part of outreach, and over time we anticipate building an archive of white papers housed here that offer applied guidance to researchers. Bookmark this page and check back often for latest developments and publications.
If you would like to come work with Andrew Hayes the MAC lab as a Ph.D. student, you would do so by applying to the Ph.D. program in Management at the Haskayne School of Business at the University of Calgary. Contact him at andrew.hayes@ucalgary.ca to talk about the possibilities as a Ph.D. student as well as other collaboration opportunities.
Mediation and moderation analysis have become staples of the curriculum in the graduate programs of disciplines that rely on social science methodologies. As a result, research in this area has exploded, and new methods are becoming increasingly sophisticated and precise but sometimes require a level of mathematical background to understand them or programming skills to implement them that many substantive researchers do not have. Most substantive researchers are too busy doing the work of the business to dedicate important resources to keeping up with all the nuances in methodology, statistical programming, and the like. For this reason, our work and writing is guided by the needs of the final user in mind rather than the expert methodologist. Members of the lab recognize that new methods take hold in a discipline when they are implemented in software that is widely used and are communicated to researchers in language that doesn't require advanced training in mathematics or statistics. We focus on data analysis problems substantive researchers encounter while offering statistical tools (usually in the form of macros or code) that make them easy to put into practice with software that most researchers are already familiar with, without requiring the expertise, guidance, or knowledge of a statistician or computer scientist.
Although the traditional peer reviewed journal article is and will always be an important means of disseminating the work of the MAC lab, length restrictions imposed by most journals often reduce how much detail and practical training can be provided through this medium. Outreach is an important component of this lab, and we are always happy to offer advice through email to people grappling with the implementation of methods described in our work. Indeed, such questions often inform us of holes in the literature and needs that exist that we may not be aware of. We also offer short courses and workshops as part of outreach, and over time we anticipate building an archive of white papers housed here that offer applied guidance to researchers. Bookmark this page and check back often for latest developments and publications.
If you would like to come work with Andrew Hayes the MAC lab as a Ph.D. student, you would do so by applying to the Ph.D. program in Management at the Haskayne School of Business at the University of Calgary. Contact him at andrew.hayes@ucalgary.ca to talk about the possibilities as a Ph.D. student as well as other collaboration opportunities.
People
Chief Investigator
Andrew F. Hayes, Ph.D. Andy is Distinguished Research Professor at the Haskayne School of Business at the University of Calgary and an adjunct Professor in the Department of Psychology. He is also the Director of the Canadian Centre for Research Analysis and Methods that he founded in 2022.
Affiliates and Alumni
Jacob J. Coutts, Ph.D. [web page] Jacob is a lecturer in the department of psychology at the University of Maryland . He completed a Master of Science in Quantitative in Psychology, a Master of Applied Statistics from The Ohio State University in 2021, and a PhD in quantitative psychology from The Ohio State University in 2023.
Tao Jiang, Ph.D. Tao did a Ph.D. in Social Psychology at The Ohio State University and is now a postdoc at Northwestern University.
Nicholas J. Rockwood, Ph.D. [personal web page]. Nick graduated in 2019 with a Ph.D. in Quantitative Psychology after first completing a Masters in Applied Statistics. He is now a Senior Psychometrician at RTI Health.
Amanda K. Montoya, Ph.D. [personal web page][UCLA web page] Amanda graduated from The Ohio State University in 2018 with a Ph.D. in Quantitative Psychology and a Masters of Science in Statistics. She is now an Associate Professor at the University of California at Los Angeles.
Elizabeth Page-Gould, Ph.D. [web page] Liz is a Professor in the Department of Psychology at the University of Toronto.
Kristopher J. Preacher, Ph.D. [web page] Kris is a professor of quantitative methods in Psychology and Human Development at Vanderbilt University.
Recent Publications
Coutts, J. J., & Hayes, A. F. (2023). Questions of value, questions of magnitude: An exploration and application of methods for comparing indirect effects in multiple mediator models. Behavior Research Methods, 55, 3372-3385. [PDF]
Rockwood, N. J., & Hayes, A. F. (2022). Multilevel mediation analysis. In A. A. O'Connell, D. B. McCoach, and B. Bell (Eds). Multilevel modeling methods with introductory and advanced applications. Information Age Publishing.
Igartua, J.-J. & Hayes, A. F. (2021). Mediation, moderation, and conditional process analysis: Concepts, computations, and some common confusions. Spanish Journal of Psychology, 24, e49. [PDF]
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. [PDF]
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, 64, 19-54.
Rockwood, N. J., & Hayes, A. F. (2020). Mediation, moderation, and conditional process analysis: Regression-based approaches for clinical research. In A. G. C. Wright and M. N. Hallquist (Eds.) Handbook of research methods in clinical psychology. Cambridge University Press.
Coutts, J., Hayes, A. F, & Jiang, T. (2019). Easy statistical mediation analysis with distinguishable dyadic data. Journal of Communication, 69, 612-649 [PDF]
Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85, 4-40. [paper and supplement]
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd Edition). New York: The Guilford Press [web page] [purchase]
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. [paper and data]
Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New York: The Guilford Press [web page][purchase].
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 [paper]
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 [paper and data] This paper is supported in part by work that appears in Amanda's MA thesis, which you can download here.
Montoya, A. K., & Hayes, A. F. (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22, 6-27. [paper] [MEMORE macro][SPSP2016]
Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50, 1-22. [email me for a copy] [PDF]
Hayes, A. F., & Agler, R. A. (2014). On the standard error of the difference between independent regression coefficients in moderation analysis. Multiple Linear Regression Viewpoints, 40 (2), 16-27.
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. [PDF] DOI: 10.1111/bmsp.12028 [online supplement]
Hayes, A. F., & Preacher, K. J. (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). Charlotte, NC: Information Age Publishing [at the publisher's page][PDF]
Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24, 1918-1927.[email me for a copy] DOI:10.1177/0956797613480187
White Papers, In Review, and Macros
Hayes, A. F., & Montoya, A. K. (working paper). Mediation analysis in the two-condition pretest-posttest design: Treatment as moderator of time effects. [Request a PDF]
Hayes, A. F. (2015). Hacking PROCESS for estimation and probing of linear moderation of quadratic effects and quadratic moderation of linear effects. Unpublished Technical Report available at the Resource Hub at the Canadian Centre for Research Analysis and Methods.
Hayes, A. F. (2022). Couterfactual/potential outcomes "causal mediation" analysis allowing for treatment by mediator interaction using PROCESS. Unpublished Technical Report available at the Resource Hub at the Canadian Centre for Research Analysis and Methods.
PROCESS for SPSS and SAS can be found here.
MEMORE for SPSS and SAS can be found here.
MLMED for SPSS can be found here.
MEDYAD for SPSS and SAS can be found here.
OGRS for SPSS and SAS can be found here.
RLM for SPSS and SAS can be found here.
OMEGA for SPSS and SAS can be found here.
Conference-Related
Coutts, J., Hayes, A. F., & Jiang, T. (2019, May). MEDYAD: A computational tool for mediation analysis with distinguishable dyadic data. Presented at the annual convention of the Association for Psychological Science, Washington DC. [PDF]
Hayes, A. F. (2019, May). Easy statistical mediation analysis with distinguishable dyadic data. Invited presentation at the annual convention of the Association for Psychological Science, Washington DC. [PDF]
Hayes, A. F., & Coutts, J. J., & Jiang, T. (2019, April). Easy statistical mediation analysis with distinguishable dyadic data. Workshop presented at the annual convention of the Midwestern Psychological Association, Chicago, IL.
Hayes, A. F., & Montoya, A. K. (2018, March). Using PROCESS v3.0: New features, editing, and building models. Workshop presented at the annual convention of the Society for Personality and Social Psychology, Atlanta, GA
Montoya, A. K. (2017, May). Simple slopes and Johnson-Neyman probing methods extended to two-condition within-subject designs. Presented at the annual convention of the Association for Psychological Science, Boston, MA. [PDF]
Rockwood, N. J., & Hayes, A. F. (2017, May). MLMED: An SPSS Macro for Multilevel Mediation and Conditional Process Analysis. Presented at the annual convention of the Association for Psychological Science, Boston, MA. Go to MLMED.
Creedon, P. J., Hayes, A. F., & Preacher, K. J. (2016, January). Omnibus tests of the indirect effect in statistical mediation analysis with a multicategorical independent variable. Presented at the annual convention of the Society for Personality and Social Psychology, San Diego, CA. [PDF]
Creedon, P .J., & Hayes, A. F. (2015, May). Small sample mediation analysis: How far can you push the bootstrap? Presented at the Annual conference of the Association for Psychological Science, New York, NY. [PDF]
Montoya, A. K., & Hayes, A. F. (2015, May). Estimating and testing indirect effects in within-subject mediation analysis: A path-analytic framework. Presented at the Annual conference of the Association for Psychological Science, New York, NY. [PDF]
Here are the slides from our symposium "Advances in Repeated Measures Mediation Analysis" at the Society for Personality and Social Psychology in San Diego, January 2016. [PDF]
Coutts, J., Hayes, A. F., & Jiang, T. (2019, May). MEDYAD: A computational tool for mediation analysis with distinguishable dyadic data. Presented at the annual convention of the Association for Psychological Science, Washington DC. [PDF]
Hayes, A. F. (2019, May). Easy statistical mediation analysis with distinguishable dyadic data. Invited presentation at the annual convention of the Association for Psychological Science, Washington DC. [PDF]
Hayes, A. F., & Coutts, J. J., & Jiang, T. (2019, April). Easy statistical mediation analysis with distinguishable dyadic data. Workshop presented at the annual convention of the Midwestern Psychological Association, Chicago, IL.
Hayes, A. F., & Montoya, A. K. (2018, March). Using PROCESS v3.0: New features, editing, and building models. Workshop presented at the annual convention of the Society for Personality and Social Psychology, Atlanta, GA
Montoya, A. K. (2017, May). Simple slopes and Johnson-Neyman probing methods extended to two-condition within-subject designs. Presented at the annual convention of the Association for Psychological Science, Boston, MA. [PDF]
Rockwood, N. J., & Hayes, A. F. (2017, May). MLMED: An SPSS Macro for Multilevel Mediation and Conditional Process Analysis. Presented at the annual convention of the Association for Psychological Science, Boston, MA. Go to MLMED.
Creedon, P. J., Hayes, A. F., & Preacher, K. J. (2016, January). Omnibus tests of the indirect effect in statistical mediation analysis with a multicategorical independent variable. Presented at the annual convention of the Society for Personality and Social Psychology, San Diego, CA. [PDF]
Creedon, P .J., & Hayes, A. F. (2015, May). Small sample mediation analysis: How far can you push the bootstrap? Presented at the Annual conference of the Association for Psychological Science, New York, NY. [PDF]
Montoya, A. K., & Hayes, A. F. (2015, May). Estimating and testing indirect effects in within-subject mediation analysis: A path-analytic framework. Presented at the Annual conference of the Association for Psychological Science, New York, NY. [PDF]
Here are the slides from our symposium "Advances in Repeated Measures Mediation Analysis" at the Society for Personality and Social Psychology in San Diego, January 2016. [PDF]
Links
Aptech Systems, producer of GAUSS.
The MikTeX project, LaTeX typesetting made fun.
WinEdt (for easy editing and production of LaTeX documents)
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