Using SPSS and R for Mediation
Analyses
Matt Baldwin
Lucas Keefer
We will cover…
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Simple and simultaneous mediation
Sequential mediation
Moderated mediation
Three models using PROCESS for SPSS
R-code for those models
MAYBE: Monte-Carlo estimator online
Terms
a
X
M
b
c’
Indirect effect: a * b ≠ 0
Y
Terms
• Simple mediation
– One predictor
– One outcome
– One or more mediators in parallel
• Sequential mediation
– One predictor
– One outcome
– More than one mediator in sequence
Terms
• Moderated mediation: strength of indirect effect
depends on one or more moderators
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One predictor
One outcome
One or more mediators (not in sequence)
One or more moderators
• Bootstrapping: estimating a parameter from
repeated resampling of the data
– Approximates sampling distribution
– Uses standard error to calculate confidence interval
for indirect effect (a*b)
PROCESS: SPSS
• Andrew Hayes, Ph.D.
• http://afhayes.com/introduction-tomediation-moderation-and-conditionalprocess-analysis.html
Installing PROCESS
PROCESS: Models
• Templates PDF file: templates.pdf
Model 4
• Simple mediation
• Multiple mediators in parallel
Model 4
Model 4 Output
Model 4 Output
• Remember, if the confidence interval does NOT
include zero, the indirect effect is significant!
Model 6
• Sequential mediation
• Multiple mediators in sequence
Model 6
Model 6 Output
Model 7
• Moderated mediation
• Multiple mediators in parallel
Model 7
Model 7
Model 7 Output
Bootstrapping Mediation in R
The boot package
• Install the boot package and dependencies
• What does it do?
The boot package
Data
Model
Number of
Resamples
boot(model, data, R = #)
Data
• Whatever object contains the data you are
analyzing
• If there are filters to apply, do so beforehand:
– med_data <- subset(data, filters)
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Model
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The model must be specified manually:
mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Simple Mediation
Simple Mediation
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mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Simple Mediation
• boot(model, data, R = #)
• obj <- boot(mediation, med_data, R = 10000)
• boot.ci(obj)
Moderated Mediation
Moderated Mediation
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mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M~X+W+WX, data=d)
model2<-lm(Y~M+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]
return(as.numeric(ab))
}
Sequential Mediation
Sequential Mediation
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mediation<-function(med_data,i){
d <- med_data[i,]
model1<- lm(M1~X, data=d)
model2<-lm(M2~M1+X, data=d)
model3<-lm(Y~M2+M1+X, data=d)
ab <- coef(model1)[2]*coef(model2)[2]*
coef(model3)[2]
• return(as.numeric(ab))
• }
Final Pointers
• Want to add model covariates? Just add them
into all the model commands (NOT as first
predictor)
Final Pointers
• Want to add model covariates? Just add them
into all the model commands (NOT as first
predictor)
• Because you are specifying the model
manually, triple check your work!
Final Pointers
• Want to add model covariates? Just add them
into all the model commands (NOT as first
predictor)
• Because you are specifying the model
manually, triple check your work!
– It won’t catch misspecification
Final Pointers
• Want to add model covariates? Just add them
into all the model commands (NOT as first
predictor)
• Because you are specifying the model
manually, triple check your work!
– It won’t catch misspecification
– Make sure it is storing the right coefficient
Thank you
Monte-Carlo Estimator
• Similar to bootstrapping method
• Calculates indirect effect from a, b, and
standard error
• http://www.quantpsy.org/medmc/medmc.ht
m
Thank You
• Please feel free to ask us questions now or
later!
• Matt’s email: mwbaldwin@ku.edu
• Lucas’ email: lkeefer1@ku.edu
• These slides can be found at
http://matthewbaldwin.yolasite.com/tools.php