**About Us**

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.

**People**

**Chief Investigator****Andrew F. Hayes, Ph.D**. Andy is Professor of Quantitative Psychology in the Department of Psychology at The Ohio State University

__Quantitative Psychology students__**Note:**The MAC Lab is at capacity and is not accepting applications for Ph.D. study

**Amanda Montoya**. [web page] Amanda is a third-year Ph.D. student in Quantitative Psychology at OSU (Advisor: Andrew Hayes). She graduated from the University of Washington in 2013 with a B.S. in Psychology and a minor in Mathematics.

**Nicholas Rockwood**. Nick is a second-year Ph.D. student in the Quantitative Psychology program at OSU (Advisor: Andrew Hayes). He completed a Bachelor's degree in Psychology at California State University at San Bernardino.

__(ongoing collaborators)__

**Affiliates****Elizabeth Page-Gould, Ph.D.**[web page] Liz is an associate professor in the Department of Psychology at the University of Toronto. Project: Multilevel mediation analysis.

**Kristopher J. Preacher, Ph.D.**[web page] Kris is an associate professor of quantitative methods in Psychology and Human Development at Vanderbilt University. Project: Omnibus inference about indirect effects.

**Recent Publications**

Hayes, A. F., & Montoya, A. K. (in press). A tutorial on testing, visualizing, and probing interaction involving a multicategorical variable in linear regression analysis.

*Communication Methods and Measures*. [PDF]. 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. (in press). Two-condition within-participant statistical mediation analysis: A path-analytic framework.

*Psychological Methods.*[PDF][email for a copy] [MEMORE macro][SPSP2016]

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. (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. (2013).

*Introduction to mediation, moderation, and conditional process analysis: A regression-based approach*. New York: The Guilford Press [web page] [purchase]

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., & Rockwood, N. J. (2016). Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation.

*Invited submission under review*. [PDF]

Hayes, A. F. (2016). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation.

*Working Paper.*[PDF]

Hayes, A. F. (2015). Hacking PROCESS for estimation and probing of linear moderation of quadratic effects and quadratic moderation of linear effects.

*White paper*. [PDF]

Hayes, A. F. (2015). Hacking PROCESS for bootstrap inference in moderation analysis.

*White paper*[PDF]

Hayes, A. F. (2014). Comparing conditional effects in moderated multiple regression: Implementation using PROCESS for SPSS and SAS.

*White paper.*[PDF]

OGRS for SPSS and SAS can be found here.

RLM for SPSS and SAS can be found here.

**PROCESS**for SPSS and SAS can be found here.**MEMORE**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.

**Conference-Related**

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]

**Appearing Soon**

**...**

October 28-29, 2016.

**Andrew Hayes**will be teaching a two day course on the topic of mediation, moderation, and conditional process analysis in Los Angeles. This course is open to the public. [enroll]

January 5-7, 2017.

**Andrew Hayes**will be teaching a two day "second course" on the topic of mediation, moderation, and conditional process analysis in Los Angeles. This course is open to the public [details]

January 2017.

**Amanda Montoya**will be conducting a workshop on within-subject mediation and moderation analysis at the annual meeting of the Society for Personality and Social Psychology in San Antonio, Texas.

May 26, 2017.

**Andrew Hayes**will be giving an invited address titled "What's Coming in PROCESS v3.0" at the annual meeting of the Association for Psychological Science, Boston, MA.

June 2017.

**Andrew Hayes**will be teaching two 5-day courses on moderation, mediation, and conditional process analysis using PROCESS at the Global School in Empirical Research Methods at the University of St. Gallen, Switzerland. These courses will be open to the public. The enrollment window at http://gserm.ch/stgallen opens in January 2017.

**Links**

Aptech Systems, producer of GAUSS.

The MikTeX project, LaTeX typesetting made fun.

WinEdt (for easy editing and production of LaTeX documents)

Mac's Cafe, the official MAC lab pub.

Facebook page, various lab-related announcements