CCRAM 2022

CCRAM Sessions 2022

Presented by the Canadian Centre for Research Analysis and Methods. Sessions begin June 2022 in Calgary, Alberta.

Join the CCRAM Sessions - in person

Featuring Canada's leading research methodology experts, the CCRAM Sessions 2022 will be held in-person at the University of Calgary, downtown campus. Choose from two, three or five-day course blocks. Attendees should expect to enhance and broaden your skills in data analysis and research design. In addition, this is a great opportunity to expand your professional and personal network, and find new collaborators. CCRAM is an educational resource for burgeoning and veteran researchers in academic, government, and industry who rely on behavioural science methods – in whatever field and wherever in the world you are located – seeking to update and expand your training in research methods and data analysis.

Spots are limited, register today.

Reduced rates are available until June 1, 2022.

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Canadian Centre for Research Analysis and Methods (CCRAM) is the preeminent Canadian destination for academics and researchers to learn from the country’s leading behavioural science methodologists.

Scale Development and Psychometrics

Jessica Flake | McGill University

Three-day course (June 20-22)

Earlybird pricing: $1,495 CAD
After May 18: $1,720 CAD

Structural Equation Modeling Done Right

Rex Kline | Concordia University

Two-day course (June 23-24)

Earlybird pricing: $995 CAD
After May 18: $1,145 CAD

Mediation, Moderation and Conditional Process Analysis

Andrew Hayes | University of Calgary

Five-day course (June 26-30) (Course ends at noon on the 30th)

Earlybird pricing: $1,995 CAD
After May 18: $2,295 CAD

Visit Calgary

Experience Calgary

The CCRAM Sessions will be hosted at the University of Calgary, downtown location. Take advantage of this opportunity to visit Calgary, Alberta. Only an hour drive away from the extraordinary Canadian Rocky Mountains. Must-see destinations include Banff National Park, Jasper National Park and Canmore. In warmer months, you can canoe or kayak across the many beautiful lakes, hike and camp in the tall forests, and breathe in the fresh mountain air.

Explore Calgary's vibrant downtown. Calgary’s culinary scene delivers flavours from all over the world with hundreds of great restaurants to choose from. Rich in arts, culture, entertainment and leisure activities, there’s always something to do in Calgary.

Getting to Calgary

Calgary is easy to get to from various destinations around the world. With one of the world's most modern and welcoming airports, getting to Calgary by air is easy with commercial airline access and other options available to travellers.

CCRAM Session Details


All courses will be delivered in person at the University of Calgary’s downtown campus located at 906 8th Avenue SW, Calgary, Alberta, Canada.


Classes begin at 9.00AM and end between 4:00PM and 5:00pm each day (except for June 30th, when classes end at noon). All classes will take lunch for one hour at noon.


Lunch will be provided on course days.


If you require accommodations while attending the CCRAM summer courses, we recommend the following hotels in the surrounding area of the University of Calgary Downtown Campus. We have provided information on a selection of hotels, that provide a range of options to best fit your needs. Below you will find information on the hotels, such as price, distance from the campus as well as instructions on booking with the University of Calgary preferred rate. 

Price per night: $99

Distance from University of Calgary Downtown Campus: 200m, 2-minute walk

Instructions on booking: "Booking Instructions: Guests can make their own reservations online at and book directly with the following steps: 1. Select the Sandman Signature Calgary Downtown, the dates you require, number of rooms, and the number of guests in the rooms, then click on Book Now. In the event that the hotels general inventory is limited the hotel may show no availability after you click the Book Now button. There will be a space to Add a Code. 2. Use drop down menu in the promo code box and select Web Group Code, then enter 2206HASKAY in the box below and click add.

Alternatively, you can call our 24-hour Central Reservations office at 1-800-726-3626 / 1-800-SANDMAN. In order to receive the correct rates, callers must reference: Sandman Signature Calgary Downtown, quoting Block ID 35387 or Haskayne School of Business Group.

Price per night: $199

Distance from University of Calgary Downtown Campus: 700m, 9-minute walk, 2-minute drive

Last day to book: Monday May 16, 2022

Instructions on booking: Please have the guests in your group directly call Marriott Reservations at Hotel Toll-Free Phone Number +1 587-885- 2288 on or before Monday, May 16 2022, (the “Cutoff Date”) to make their sleeping room reservations. Please identify yourself as part of the University of Calgary Executive Education staying at the Residence Inn by Marriott Calgary Downtown / Beltline District, located at 610 - 10th Avenue SW Calgary, Alberta T2R 1M3.

Price per night: $209

Distance from University of Calgary Downtown Campus: 1.3 km, 16-minute walk, 5-minute drive

Last day to book: Thursday May 19, 2022

Price: $279

Distance from University of Calgary Downtown Campus: 1.4km, 18-minute walk, 5-minute drive

Cancellation Policy

If you need to cancel your registration or withdraw from your registered program, emailed notice must be submitted to a representative of Haskayne School of Business Executive Education.

Cancellation or withdrawal of your registration will incur the following fee:
• $100 for notice of cancellation/withdrawal from the program received 31 days or greater prior to the program start date
• The fee amount equivalent to 25% of the program cost, up to a maximum of $500, for notice of cancellation/withdrawal from the program received between 30 and 15 days prior to the program start date
• The fee amount equivalent to 100% of the program cost, for notice of cancellation/withdrawal from the program received 14 days or less prior to the program start date.

Should you be unable to attend a registered program due to acts of God, war, government regulations, disaster, strikes, civil disorder, curtailment of transportation facilities, pandemic, or other emergencies making it illegal or impossible to travel, emailed notice must be submitted to a representative of Haskayne School of Business Executive Education. You will be required to pay the $100 program deposit. All other cancellation fees will be waived.

If you have questions, please contact us at (403) 220-6600 or by email

Scale Development and Psychometrics

Researchers in the academic and private sectors often need to measure some aspect of people’s psychology be it their attitudes, satisfaction, motivation, or intentions. We assume that the numbers these scales, questionnaires, tests, and surveys produce are meaningful: that someone with a higher satisfaction score is in fact more satisfied than someone with a lower score. Because scale scores are used to make decisions like how to measure critical outcomes in a research study, develop a product, or admit a student or promote an employee, researchers need to thoroughly evaluate their validity. This short course will cover how to develop, evaluate, and refine scales using modern psychometric methods.

In this course, you will learn how to apply modern validity theory and psychometric methods to appropriately develop and use scales measuring psychological attributes.

  • Overview of construct validity theory and types of validity evidence
  • Item writing
  • Item content review and think-aloud protocol
  • Executing and interpreting item analysis
  • Overview of types of factor analysis
  • Executing and interpreting exploratory factor analysis
  • Executing and interpreting reliability analysis
  • Interpreting and evaluating validity evidence for scale selection and use

The course will focus on scale development and refinement with psychometric methods that can be implemented in many statistical software packages. Because this is a hands-on course, learners are encouraged to bring a laptop to class with a copy of R or SPSS installed. However, instruction will focus on demonstrating the statistical techniques in multiple software programs and it isn’t required that students be an expert in any specific software. Provided materials and examples will include examples from various software packages such as SPSS, SAS, and R.

This course will be helpful for researchers in any field —including psychology, sociology, education, business, human development, social work, public health, communication and others that rely on social science methodology —who want to develop and use scales to measure psychological attributes. Learners should have background knowledge in introductory statistics topics such as univariate statistical tests, descriptive statistics, and correlation. Ideally learners should be comfortable with multiple regression techniques. Though proficiency in a specific software isn’t required, ideally participants will have some familiarity with running analyses using some type of statistical software (e.g., R, SPSS, SAS, STATA).

Upon completing this course, you will

  • Be able to define construct validity and describe different forms of validity evidence
  • Evaluate scale items for poor, confusing, or problematic wording
  • Use descriptive statistics to quantitatively evaluate item properties
  • Use qualitative approaches to review item content
  • Compare different approaches to factor analysis
  • Compare different approaches to quantifying reliability
  • Execute and interpret an exploratory factor analysis
  • Execute and interpret a reliability analysis
  • Evaluate multiple sources of validity evidence to select a scale
  • Evaluate multiple sources of validity evidence to develop or refine a scale
Jessica Flake, Ph.D.

Instructor: Jessica Flake, Ph.D.

McGill University

Courses taught for CCRAM:

Scale Development and Psychometrics

Dr. Flake is an Assistant Professor of Quantitative Psychology and Modelling at McGill University. She received an MA in quantitative psychology from James Madison University and a a PhD in Measurement, Evaluation, and Assessment from the University of Connecticut. Her research develops and applies latent variable models for use in psychological research with an emphasis on improving measure development and use. Her work is highly cited and published in top methodological and substantive outlets such as Nature: Human Behavior, Psychological Methods, Advances in Methods and Practices in Psychological Science, Structural Equation Modeling, Psychological Science, and the Journal of Personality and Social Psychology. She was named an Association of Psychological Science Rising Star in 2021 and received a Society for the Improvement of Psychological Science Commendation in 2020 for her research into questionable measurement practices.

Her work focuses on technical and applied aspects of psychological measurement including scale development, psychometric modelling, and scale use and replicability. She is a top-rated professor in the Department of Psychology at McGill University, regularly teaching measurement and statistics courses as well as workshops at international conferences. Further, she routinely works in applied psychometrics as a technical advisory panel member for the Enrollment management Association, a non-profit that develops educational assessments, and serves as the Assistant Director for Methods at the Psychological Science Accelerator, a laboratory network that conducts large-scale studies.

Luong, R. & Flake, J.K. (in press). Measurement invariance testing using confirmatory factor analysis and alignment optimization: A tutorial for transparent analysis planning and reporting. Psychological Methods.

Flake, J. K., Shaw, M., & Luong, R. (in press). Addressing a crisis of generalizability with large-scale construct validation. Behavioral and Brain Sciences.

Flake, J.K. (2021). Strengthening the foundation of educational psychology by integrating construct validation into open science reform. Educational Psychologist. 56, 132-141.

Beymer, P.N., Ferland, M., & Flake, J.K. (2021). Validity evidence for a short scale of college students’ perceptions of cost. Current Psychology, 1-20.

Hwang, H., Cho, G., Jung, K., Falk, C., Flake, J.K., & Jin, M. (2021). An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods, 26, 273–294

Flake, J.K., & Fried, E.I. (2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3, 456-465.

Shaw, M., Cloos, L., Luong, R., Elbaz, S. & Flake, J.K. (2020). Measurement practices in large-scale replications: Insights from Many Labs 2. Canadian Psychology, 61, 289-298.

Hehman, E., Calanchini, J., Flake, J. K., & Leitner, J. B. (2019). Establishing construct validity evidence for regional measures of explicit and implicit racial bias.  Journal of Experimental Psychology: General. 148 (6) 1022-1040.

Flake, J.K., & McCoach, D.B. (2018). An investigation of the alignment method with polytomous indicators under conditions of partial measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 25 (1), 56-70.

Flake, J.K., Pek, J., & Hehman, E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science, 8, 370-378

Flora, D. & Flake, J.K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioural Science, 49, 78-88.

Goldstein, J., & Flake, J.K. (2016). Towards a framework for the validation of early childhood assessment systems. Educational Assessment, Evaluation and Accountability, 23, 273-293 .

Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244.

Structural Equation Modeling Done Right

The technique of structural equation modeling (SEM) is widely used in many disciplines including psychology, education, communication, biology, medicine, and others. Unfortunately, many—and possibly most—published SEM studies have at least one flaw so severe that it compromises the scientific merit of the work. This is because there are certain poor practices in SEM that are relatively common, some of which are maintained by statistical myths about the conduct of SEM or about the interpretation of analysis results. These problems are compounded by widespread deficiencies in reporting apparent in the literature.

The point of this course is to reinforce best practices in SEM and thereby assist participants to avoid common pitfalls and shortcomings in the area. Four topics are emphasized: (1) How to report the results in ways that are transparent, complete, and respect updated reporting standards for SEM studies by the American Psychological Association. (2) How to avoid confirmation bias by directly addressing the phenomenon of equivalent models that fit the data just as well as the researcher’s target model but with contradictory hypotheses about causation. (3) How to properly and thoroughly evaluate model fit, a critical part of deciding whether to retain or to reject a model. (4) Preregistration of the analysis plan is also described as a best practice when a more exploratory phase of the analysis is expected.

In this course, you will learn about how to follow the best practices in SEM as just summarized. Course topics include

  • Review of the content in APA reporting standards for SEM studies
  • The proper role of significance testing versus model indexing in evaluating global model fit
  • Identification of myths about model fit statistics, especially about thresholds of approximate fit indexes that supposedly signal “good” model fit
  • The role of evidence for local model fit, ignored in too many studies, in the form of residuals is explained
  • Types of residuals are defined, including covariance, correlation, standardized, and normalized residuals
  • How to generate equivalent structural or measurement models is described
  • How to plan, organize, and describe the analysis plan—including preregistration of that plan—in clear and transparent ways

Best practices covered in this course do not rely on the use of any particular computer tool or software package for SEM. Instead, the concepts and skills are those that any researcher should know or have mastered regardless of whether they use Mplus, lavaan, LISREL, Amos, or other any computer program. Thus, the course is about ideas, not about computer skills.

This course about best practices should benefit a range of participants, from researchers-in-training, such as graduate students, up thorough current researchers more experienced with SEM and who seek to upgrade their knowledge. The overall goal is to help participants distinguish their work whether submitted as a thesis or dissertation to a research committee or manuscripts with SEM analyses submitted to journals. Participants should have some prior exposure to SEM, such as in a course or through its application in research projects. The best practices covered in the course do not require expert-level knowledge of SEM. By the end of course participants will have learned some key ways to improve their future applications of SEM.

Upon completing this course, you will

  • Understand the contents of reporting standards for SEM studies including the need to describe both global fit and local fit, or the residuals, in written summaries
  • Know how to interpret residuals of different types, including covariance, correlation, standardized, or normalized residuals
  • Avoid common false interpretations of global fit statistics, including the model chi-square and approximate fit indexes
  • Understand that failure to directly acknowledge the existence of equivalent or near-equivalent models is a form of confirmation bias
  • Be able to generate for your readers at least a few equivalent models and appreciate that rational argument, not statistical analysis, is the only way to prefer one equivalent model over another
  • Understand the role of preregistration as way to reduce hypothesizing after the results are known, or harking, which is the undisclosed presentation of exploratory analyses as though they were confirmatory
Rex Kline, Ph.D.

Instructor: Rex Kline, Ph.D.

Concordia University

Courses taught for CCRAM:

Structural Equation Modeling Done Right

Dr. Kline received a PhD in Clinical Psychology with a doctoral minor in Statistics and Measurement from Wayne State University in Detroit, Michigan. He is currently a Professor in the Department of Psychology at Concordia University in Montréal, Québec, Canada. He has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. Dr. Kline is the author of Principles and Practice of Structural Equation Modeling, which through four editions (1998, 2005, 2011, 2016) has been one of the widely cited introductory-level text books in the area. The fifth edition is forthcoming soon. Recently, Dr. Kline was a member of the Publications and Communications Board Task Force of the American Psychological Association that revised journal article reporting standards for quantitative studies and introduced updated reporting standards for SEM studies.


Kline, R. B. (2020). Becoming a behavioral science researcher: A guide to producing research that matters (2nd ed.). New York: Guilford Press.

Kline, R. B. (2019). 구조방정식모형. (Principles and Practice of Structural Equation Modeling, 4th ed., Korean trans.). Seoul, Korea: Hakjisa Publisher.

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Press.

Kline, R. B. (2013). Beyond significance testing: Statistics reform in the behavioral sciences (2nd ed.). Washington, DC: American Psychological Association.


Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board Task Force report. American Psychologist, 73, 3–25.


Kline, R. B. (in press). Post p-value education in graduate statistics: Preparing tomorrow’s psychology researchers for a post-crisis future. Canadian Psychology.

Zhang, M. F., Dawson, J., & Kline, R. B. (in press). Evaluating the use of covariance-based structural equation modelling with reflective measurement in organisational and management research: A review and recommendations for best practice. British Journal of Management.

Sauvé, G., Kline, R. B., Shah, J. L., Joober, R., Malla, A., Brodeur, M. B., & Lepage, M. (2019). Cognitive capacity similarly predicts insight into symptoms in first- and multiple-episode psychosis. Schizophrenia Research, 206, 236–243.

Nicolakakis, N., Stock, S. R., Abrahamowicz, M., Kline, R., & Messing, K. (2017). Relations between work and upper extremity musculoskeletal problems (UEMSP) and the moderating role of psychosocial work factors on the relation between computer work and UEMSP. International Archives of Occupational and Environmental Health, 90, 751–764.

Goodboy, A. K., & Kline, R. B. (2017). Statistical and practical concerns with published communication research featuring structural equation modeling. Communication Research Reports, 34, 1–10.

Kline, R. B. (2015). The mediation myth. Basic and Applied Social Psychology, 37, 202–213.


Kline, R. B. (in press). Structural equation modeling. In R. Tierney, F. Rizvi, K. Ercikan, & G. Smith (Eds.), International encyclopedia of education (4th ed.). Oxford, United Kingdom: Elsevier.

Kline, R. B. (in press). Structural equation modeling in neuropsychology research. In G. Brown, B. Crosson, K. Haaland, & T. King (Eds.), APA handbook of neuropsychology. Washington DC: American Psychological Association.

Kline, R. B. (in press). Psychometrics. In P. Atkinson, S. Delamont, M. Hardy, & M. Williams (Eds.), Encyclopaedia of Social Research Methods (2nd ed.). Thousand Oaks, CA: Sage.

Kline, R. B. (2017). Mediation analysis in leadership studies: New developments and perspectives. In B. Schyns, R. J. Hall, & P. Neves (Eds.), Handbook of methods in leadership research (pp. 173–194). Northhampton, MA: Elgar.

Kline, R. B. (2015). Path models. In D. S. Dunn (Ed.), Oxford bibliographies in psychology. New York: Oxford University Press.

Kline, R. B. (2013). Reverse arrow dynamics: Feedback loops and formative measurement. In G. R. Hancock and R. O. Mueller, (Eds.), Structural equation modeling: A second course (2nd ed.) (pp. 39–76). Greenwich, CT: Information Age Publishing.

Kline, R. B. (2013). Exploratory and confirmatory factor analysis. In Y. Petscher & C. Schatschneider (Eds.), Applied quantitative analysis in the social sciences (pp. 171-207). New York: Routledge.


Mediation analysis in cross-sectional designs. La Société Statistique de Montréal (SSM) et Collectif pour le développement et les applications en mesure at évaluation de la Faculté des sciences de l’éducation de l’UQÀM, March 16, 2018.

New developments in mediation analysis. SSM et Collectif pour le développement et les applications en mesure at évaluation de la Faculté des sciences de l’éducation de l’UQÀM, November 25, 2016.

Living statistics reform. SSM et Collectif pour le développement et les applications en mesure at évaluation de la Faculté des sciences de l’éducation de l’UQÀM, March 24, 2016.

Becoming a behavioral science researcher. Southwest Educational Research Association, Presidential invited address, February 10, 2016.

Hello, statistics reform. Nebraska Academy for Methodology, Analytics and Psychometrics, University of Lincoln–Nebraska, Nov 10, 2015; School of Psychology, University of Ottawa, Sept 25, 2014; Department of Psychology, Concordia University, Sept 26, 2013.

New developments in structural equation modeling. Methodology, Analytics & Psychometrics Academy, University of Nebraska–Lincoln. Nov 10, 2014.


Advanced topics in structural equation modeling. Quebec Inter-University Centre for Social and Statistics (QICSS), Montréal, May 13–15, 2019; April 25–27, 2016; April 27–29, 2015; May 12–14, 2014; May 8–19, 2013; May 14–16, 2012.

Introduction to structural equation modeling. QICSS, Montréal, May 6–10, 2019; May 14−18, 2018; April 17–21, 2017; April 18–22, 2016; April 20–24, 2015; April 28–May 2, 2014; April 22–26, 2013; February 20-24, 2012; May 2–6, 2011; February 21–25, 2011; May 17–24, 2010; February 22–26, 2010; May 25–29, 2009; June 9–13, 2008; December 1–5, 2008; May 22–25, 2007.

Structural equation modeling. Istanbul Quantitative Lectures, School of Business, Istanbul University, July 6–11, 2015; August 25–31, 2014; July 1–12, 2013.

Introduction to structural equation modeling. Portland State University, Summer Quantitative Methods Series, Portland, OR, June 16–17, 2014; June 15–16, 2012.

Structural equation modeling. Axe Santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec, Université Laval, October 28-29, 2013.

Structural equation modeling. Ted Rogers School of Management, Ryerson University, May 13–14, 2013.

Mediation, Moderation, and Conditional Process Analysis

Statistical mediation and moderation analyses are among the most widely used data analysis techniques in social science, health and business research. Mediation analysis is used to test hypotheses about various intervening mechanisms by which causal effects operate. Moderation analysis is used to examine and explore questions about the contingencies or conditions of an effect, also called “interaction.”  Increasingly, moderation and mediation are being integrated analytically in the form of what has become known as “conditional process analysis,” used when the goal is to understand the contingencies or conditions under which mechanisms operate. An understanding of the fundamentals of mediation and moderation analysis is in the job description of almost any empirical scholar. In this course, you will learn about the underlying principles and the practical applications of these methods using ordinary least squares (OLS) regression analysis and the PROCESS macro for SPSS, SAS and R, invented by the course instructor and widely used in the behavioral sciences. This course is a companion to the instructor’s book Introduction to Mediation, Moderation, and Conditional Process Analysis, published by The Guilford Press. A copy of the book is not required to benefit from the course, but it could be helpful to reinforce understanding.

In this course, you will learn about the underlying principles and the practical applications of mediation, moderation and conditional process analysis. It covers six broad topics:

  1. Direct, indirect, and total effects in a mediation model
  2. Estimation and inference in single mediator models using ordinary least squares regression
  3. Estimation and inference in mediation models with more than one mediator
  4. Moderation or “interaction” in ordinary least squares regression
  5. Testing, interpreting, probing, and visualizing interactions
  6. The integration of mediation and moderation: Conditional process analysis

Computer applications will focus on the use of ordinary least squares regression and the PROCESS macro for SPSS, SAS and R, developed by the instructor, that makes the analyses described in this class much easier than they otherwise would be. This is a hands-on course, so maximum benefit results when learners can follow along with analyses using a laptop or desktop computer with a recent version of SPSS Statistics (version 23 or later), SAS (release 9.2 or later, with PROC IML installed) or R (version 3.6 or later; base module only. No packages are used in this course). Learners can choose which statistical package they prefer to use. STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for STATA.

This course will be helpful for researchers in any field – including psychology, sociology, education, business, human development, social work, public health, communication and others that rely on social science methodology – who want to learn how to apply the methods of moderation, mediation, and conditional process analysis using widely-used software such as SPSS, SAS and R.

Learners are recommended to have familiarity with the practice of multiple regression analysis and elementary statistical inference. No knowledge of matrix algebra is required or assumed, nor is matrix algebra used in the delivery of course content. Learners should also have some experience with the use of SPSS, SAS or R, including opening and executing data files and programs.

Upon completing this course, you will be able to:

  • statistically partition one variable’s effect on another into its primary pathways of influence, direct and indirect
  • understand modern approaches to inference about indirect effects in mediation models
  • test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators
  • estimate and interpret mediation models with mediators operating in serial
  • estimate and interpret relative direct, indirect and total effects in a mediation model with a multi-categorical (more than 2 groups) independent variable
  • understand how to build flexibility into a regression model that allows a variable’s effect to be a function of another variable in a model
  • visualize and probe interactions in regression models (e.g. using the simple slopes/spotlight analysis and Johnson-Neyman/floodlight analysis approaches)
  • test, visualize, probe and interpret moderation in a model with a multi-categorical independent variable or moderator
  • integrate models involving moderation and mediation into a conditional process model
  • estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects
  • determine whether a mechanism is dependent on a moderator variable
  • conduct a conditional process analysis with models with more than one mediator (serial and parallel)
  • understand the concept of differential dominance and appreciate its value in theory and research
  • conduct a conditional process analysis with a multi-categorical independent variable
  • apply the methods discussed in this course using the PROCESS procedure for SPSS, SAS and R
  • talk and write in an informed way about the mechanisms and contingencies of causal effects

In this course, we focus primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. We do not cover complex models involving dichotomous outcomes, latent variables, nested data (i.e., multilevel models) or the use of structural equation modeling.

Andrew F. Hayes, Ph.D.

Instructor: Andrew F. Hayes, Ph.D.

University of Calgary CCRAM Academic Director

Courses taught for CCRAM:

Mediation, Moderation, and Conditional Process Analysis

Dr. Hayes received his Ph.D. in Social Psychology from Cornell University. Practicing primarily as a quantitative methodologist, he is currently a Distinguished Research Professor at the Haskayne School of Business at the University of Calgary with an adjunct appointment in the Department of Psychology. He is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis (2022) 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, 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 government researchers. His work has been cited well over 140,000 times according to Google Scholar and he has been designated a Highly Cited Researcher by Clarivate Analytics in 2019, 2020, and 2021.

Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd edition). New York: The Guilford Press.

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.

Hayes, A. F., & Rockwood, N. J. (2020). Conditional process analysis: Concepts, computations, and advances in the modeling of the contingencies of mechanisms. American Behavioral Scientist, 64, 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 Monographs, 85, 4-40.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New 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. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470.

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.

Hayes, A. F., & Preacher, K. J. (2010). Estimating and testing indirect effects in simple mediation models with 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.

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.

Want more information on the Canadian Centre for Research Analysis and Methods? CCRAM Website