Welcome to CARMA’s Webcast and
Topic Interest Group (SEM)

Dr. David Mackinnon, Arizona State

Introduction to Casual Mediation Analysis

February 19th, 2021 / 12:00 – 1:30 pm ET (Reception to Follow)

PowerPoint Slides

David P. MacKinnon, Ph.D., has been developing, evaluating, and applying methods to assess how interventions work for over 30 years.  He is a Foundation Professor of Psychology at Arizona State University.  He received his undergraduate degree from Harvard University in 1989, and his Ph.D. in Measurement and Psychometrics from UCLA in 1986. In 2011, he received the Nan Tobler Award from the Society for Prevention Research for his book on statistical mediation analysis. Dr. MacKinnon has been Principal Investigator on several National Institute on Health grants. He received the Merit Research in Time Award from the National Institute on Drug Abuse for his research on mediation analysis. He has given numerous workshops in the United States and Europe, has served on federal grant review committees and as a consulting editor, and is a Web of Science highly cited researcher. He is a Fellow of the Association for Psychological Science, Society for Prevention Research, and American Psychological Association Quantitative and Qualitative Methods Division. He is president of the Society for Multivariate Experimental Psychology was president of the American Psychological Association Division 5.


This talk describes recent advances in causal mediation analysis based on the potential outcomes (counterfactual) framework. Many fields apply mediating variable theory and traditional linear regression mediation analysis. The potential outcomes framework is a new and comprehensive approach to conceptualizing and testing questions about causal inference. I describe the motivation and general background for the potential outcomes framework for causal analysis. The potential outcomes framework is applied to mediation analysis, including the specification and identification of causal quantities and methods to estimate causal mediation effects. Similarities and differences between traditional and causal mediation analysis are highlighted. Both traditional and causal methods are applied to real data. The presentation ends with future directions for mediation theory and statistical analysis.

Topic Interest Group – Structural Equation Methods (SEM)

February 19, 2021 / 1:30 pm ET

  • Panel Session
  • Ask the Experts Session and Reception

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Upcoming CARMA Events

  • Mar. 3 – Eight Webcast of the 2020-21 Academic Year (Dr. Michael Howard, Network Analysis)
  • Mar. 31 – Ninth Webcast of the 2020-21 Academic Year (Dr. Lisa Harlow, Multivariate Analysis with R)
  • Apr. 9 – Tenth Webcast of the 2020-21 Academic Year (Dr. Jason Colquitt, Content Validation)

Other CARMA Recordings on Similar Topics

  • Longitudinal SEM – Dr. Le Zhou
  • Partial Least Squares Structural Equation Modeling (PLS-SEM) – Dr. Joe Hair
  • Causal Indicators and Measurement Models – Dr. Ken Bollen
  • Parcel Indicators in SEM – Dr. Todd Little
  • Condition 9 and 10 Tests of Model Confirmation with SEM Techniques – Dr. Larry Williams
  • Structural Equation Models for Cooperative Small Group Context: The Interplay of theory and Method in Goal-Directed Behavior – Dr. Richard P. Bagozzi
  • Multilevel Mediation – Dr. Zhen Zhang
  • Partial Least Squares Path Modeling: Past and Future – Dr. Ed Rigdon
  • Alternative Approaches to Modeling MTMM Data in Organizational Research – Dr. David Woehr
  • Growth Mixture Modeling – Dr. Mo Wang
  • Meta analysis and Structural Equation Modeling – Dr. Ron Landis
  • Formative Constructs Implemented via Common Factors – Dr. Peter Bentler
  • New Approaches for Evaluating Latent Variable Relations – Dr. Larry Williams
  • Marker Variables and Method Variance: Models and Processes – Dr. Larry Williams
  • Mean Structure Models with Latent Variables – Dr. Greg Hancock
  • The Meaning of Latent Variables in Social and Behavioral Science Research – Dr. Ken Bollen
  • Detecting and Diagnosing Model Misspecification – Dr. Deborah Bandalos
  • Measurement Models for Linking Latent Variables and Indicators: A Review of Alternatives for Organizational Researchers – Dr. Larry Williams
  • Measurement Invariance and Applied Research – Dr. Roger Millsap
  • Goodness of Fit and Structural Equation Models – Dr. Jose Cortina
  • The World Is Flat, the Earth is the Center of the Universe, and Mediating Effects Can Be Tested Using Data from Nonexperimental Research – Dr. Eugene F. Stone-Romero
  • Multilevel Structural Equation Methods – Dr. Robert Vandenberg
  • Moderation in Structural Equation Modeling: Specification, Estimation, and Interpretation Using Quadratic Structural Equations – Dr. Jeffrey Edwards