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
11.4
Estimation of a conditional process model using PROCESS
We just fit the model in the last section.