Transcript Mediation - Matthew Baldwin
Using SPSS and R for Mediation Analyses Matt Baldwin Lucas Keefer
We will cover… • • • • • • 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 M
a b
X
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 – – 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-to mediation-moderation-and-conditional process-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 • • • • • • • •
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 • • • • • • • •
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 • • • • • • • •
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 • • • • • • • •
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 • • • • • • • •
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 • • • • • • • •
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 • • • • • • •
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 • • • • • • •
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 • • • • • • • •
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: [email protected]
Lucas’ email: [email protected]
These slides can be found at http://matthewbaldwin.yolasite.com/tools.php