• Welcome
    • Why this?
    • My assumptions about you
    • How to use and understand this project
  • I FUNDAMENTALS
  • 1 Introduction
    • References
    • Session info
  • 2 Fundamentals of Linear Regression
    • 2.1 Correlation and prediction
    • 2.2 The simple linear regression model
      • 2.2.1 Interpretation of the constant and regression coefficient
      • 2.2.2 The standardized regression model
      • 2.2.3 Simple linear regression with a dichotomous antecedent variable.
    • 2.3 Alternative explanations for association
    • 2.4 Multiple linear regression
      • 2.4.1 The standardized regression model.
    • 2.5 Measures of model fit
    • 2.6 Statistical inference
      • 2.6.1 Testing a null hypothesis.
      • 2.6.2 Interval estimation.
      • 2.6.3 Testing a hypothesis about a set of antecedent variables.
    • 2.7 Multicategorical antecedent variables
    • 2.8 Assumptions for interpretation and statistical inference
      • 2.8.1 Linearity.
      • 2.8.2 Normality.
      • 2.8.3 Homoscedasticity.
      • 2.8.4 Independence.
    • Reference
    • Session info
  • II MEDIATION ANALYSIS
  • 3 The Simple Mediation Model
    • 3.1 Estimation of the direce, indirect, and total effects of \(X\)
    • 3.2 Example with dichotomous \(X\): The influence of presumed media influence
      • 3.2.1 Estimation of the model in PROCESS for SPSS and SAS.
    • 3.3 Statistical inference
      • 3.3.1 Inference about the total effect of \(X\) on \(Y\).
      • 3.3.2 Inference about the direct effect of \(X\) on \(Y\).
      • 3.3.3 Inference about the indirect of \(X\) on \(Y\) through \(M\).
    • 3.4 An example with continuous \(X\): Economic stress among small-business owners
    • Reference
    • Session info
  • 4 Causal Steps, Confounding, and Causal Order
    • 4.1 What about Barron and Kenny?
    • 4.2 Confounding and causal order
      • 4.2.1 Accounting for confounding and epiphenomenal association.
    • 4.3 Effect size
      • 4.3.1 The partially standardized effect.
      • 4.3.2 The completely standardized effect.
      • 4.3.3 Some (problematic) measures only for indirect effects.
    • 4.4 Statistical power
    • 4.5 Multiple \(X\)s or \(Y\)s: Analyze separately or simultaneously?
      • 4.5.1 Multiple \(X\) variables.
      • 4.5.2 Estimation of a model with multiple \(X\) variables in PROCESS brms.
      • 4.5.3 Multiple \(Y\) variables.
    • References
    • Session info
  • 5 More Than One Mediator
    • 5.1 The parallel multiple mediator model
    • 5.2 Example using the presumed media influence study
    • 5.3 Statistical inference
      • 5.3.1 Inference about the direct and total effects.
      • 5.3.2 Inference about specific indirect effects.
      • 5.3.3 Pairwise comparisons between specific indirect effects.
      • 5.3.4 Inference about the total indirect effect.
    • 5.4 The serial multiple mediator model
      • 5.4.1 Direct and indirect effects in a serial multiple mediator model.
      • 5.4.2 Statistical inference.
      • 5.4.3 Example from the presumed media influence study.
    • References
    • Session info
  • 6 Mediation Analysis with a Multicategorical Antecedent
    • 6.1 Relative total, direct, and indirect effects
    • 6.2 An example: Sex discrimination in the workplace
    • 6.3 Using a different group coding system
    • References
    • Session info
  • III MODERATION ANALYSIS
  • 7 Fundamentals of Moderation Analysis
    • 7.1 Conditional and unconditional effects
    • 7.2 An example: Climate change disasters and humanitarianism
      • 7.2.1 Estimation using PROCESS brms.
      • 7.2.2 Interpreting the regression coefficients.
      • 7.2.3 Variable scaling and the interpretation of \(b_{1}\) and \(b_{3}\).
    • 7.3 Visualizing moderation
    • 7.4 Probing an interaction
      • 7.4.1 The pick-a-point approach.
      • 7.4.2 The Johnson-Neyman technique.
    • 7.5 The difference between testing for moderation and probing it
    • 7.6 Artificial categorization and subgroups
    • References
    • Session info
  • 8 Extending the Fundamental Principles of Moderation Analysis
    • 8.1 Moderation with a dichotomous moderator
      • 8.1.1 Visualizing and probing the interaction.
    • 8.2 Interaction between two quantitative variables
      • 8.2.1 Visualizing and probing the interaction.
    • 8.3 Hierarchical versus simultaneous entry
    • 8.4 The equivalence between moderated regression analysis and a 2 X 2 factorial analysis of variance
      • 8.4.1 Simple effects parameterization.
      • 8.4.2 Main effects parameterization.
      • 8.4.3 Conducting a 2 X 2 between-participants factorial ANOVA using PROCESS another regression model with brms.
    • References
    • Session info
  • 9 Some Myths and Additional Extensions of Moderation Analysis
    • 9.1 Truths and myths about mean-centering
      • 9.1.1 The effect of mean-centering on multicollinearity and the standard error of \(b_{3}\).
      • 9.1.2 The effect of mean-centering on \(b_{1}\), \(b_{2}\), and their standard errors posterior \(SD\)s.
      • 9.1.3 The centering option in PROCESS.
    • 9.2 The estimation and interpretation of standardized regression coefficients in a moderation analysis
      • 9.2.1 Variant 1.
      • 9.2.2 Variant 2.
    • 9.3 A caution on manual centering and standardization
    • 9.4 More than one moderator
      • 9.4.1 Additive multiple moderation.
      • 9.4.2 Moderated moderation.
    • 9.5 Comparing conditional effects
      • 9.5.1 Comparing conditional effects in the additive multiple moderation model.
      • 9.5.2 Comparing conditional effects in the moderated moderation model.
      • 9.5.3 Implementation in PROCESS brms.
    • References
    • Session info
  • 10 Multicategorical Focal Antecedents and Moderators
    • 10.1 Moderation of the effect of a multicategorical antecedent variable
    • 10.2 An example from the sex disrimination in the workplace study
    • 10.3 Visualizing the model
    • 10.4 Probing the interaction
      • 10.4.1 The pick-a-point approach.
      • 10.4.2 The Johnson-Neyman technique.
    • 10.5 When the moderator is multicategorical
      • 10.5.1 An example.
      • 10.5.2 Probing the interaction and interpreting the regression coefficients.
    • References
    • Session info
  • IV CONDITIONAL PROCESS ANALYSIS
  • 11 Fundamentals of Conditional Process Analysis
    • 11.1 Examples of conditional process models in the literature
    • 11.2 Conditional direct and indirect effects
    • 11.3 Example: Hiding your feelings from your work team
    • 11.4 Estimation of a conditional process model using PROCESS
    • 11.5 Quantifying and visualizing (conditional) indirect and direct effects.
      • 11.5.1 Visualizing the direct and indirect effects.
    • 11.6 Statistical inference
      • 11.6.1 Inference about the direct effect.
      • 11.6.2 Inference about the indirect effect.
      • 11.6.3 Probing moderation of mediation.
    • References
    • Session info
  • 12 Further Examples of Conditional Process Analysis
    • 12.1 Revisiting the disaster framing study
    • 12.2 Moderation of the direct and indirect effects in a conditional process model
      • 12.2.1 Estimation using PROCESS.
      • 12.2.2 Quantifying direct and indirect effects.
      • 12.2.3 Visualizing the direct and indirect effects.
      • 12.2.4 Bonus: Let’s replace sapply() with map().
    • 12.3 Statistical inference
      • 12.3.1 Inference about the direct effect.
      • 12.3.2 Inference about the indirect effect.
      • 12.3.3 Pruning the model.
    • 12.4 Mediated moderation
      • 12.4.1 Mediated moderation as the indirect effect of a product.
      • 12.4.2 Why mediated moderation is neither interesting nor meaningful.
    • References
    • Session info
  • 13 Conditional Process Analysis with a Multicategorical Antecedent
    • 13.1 Revisiting sexual discrimination in the workplace
    • 13.2 Looking at the components of the indirect effect of \(X\)
      • 13.2.1 Examining the first stage of the mediation process.
      • 13.2.2 Estimating the second stage of the mediation process.
    • 13.3 Relative conditional indirect effects
    • 13.4 Testing and probing moderation of mediation
      • 13.4.1 A test of moderation of the relative indirect effect.
      • 13.4.2 Probing moderation of mediation.
    • 13.5 Relative conditional direct effects
    • References
    • Session info

recoding Introduction to Mediation, Moderation, and Conditional Process Analysis

Reference

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