Using SPSS and R for Mediation Analyses

1. Using SPSS and R for Mediation Analyses Matt Baldwin Lucas Keefer

2. 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

3. Terms M b a Y X c’ Indirect effect: a * b ≠ 0

4. 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

5. 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)

6. PROCESS: SPSS • Andrew Hayes, Ph.D. • http://afhayes.com/introduction-to-mediation-moderation-and-conditional-process-analysis.html

9. Installing PROCESS

11. PROCESS: Models • Templates PDF file: templates.pdf

12. Model 4 • Simple mediation • Multiple mediators in parallel

13. Model 4

15. Model 4 Output

16. Model 4 Output • Remember, if the confidence interval does NOT include zero, the indirect effect is significant!

17. Model 6 • Sequential mediation • Multiple mediators in sequence

18. Model 6

20. Model 6 Output

21. Model 7 • Moderated mediation • Multiple mediators in parallel

22. Model 7

23. Model 7

24. Model 7 Output

25. Bootstrapping Mediation in R

26. The boot package • Install the boot package and dependencies • What does it do?

27. The boot package

28. Data • Whatever object contains the data you are analyzing • If there are filters to apply, do so beforehand: • med_data <- subset(data, filters)

29. 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)) • }

30. 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)) • }

31. 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)) • }

32. 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)) • }

33. 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)) • }

34. 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)) • }

35. Simple Mediation

36. 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)) • }

37. Simple Mediation • boot(model, data, R = #) • obj <- boot(mediation, med_data, R = 10000) • boot.ci(obj)

38. Moderated Mediation

39. 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)) • }

40. Sequential Mediation

41. 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)) • }

42. Final Pointers • Want to add model covariates? Just add them into all the model commands (NOT as first predictor)

43. 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!

44. 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

45. 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

46. Thank you

47. Monte-Carlo Estimator • Similar to bootstrapping method • Calculates indirect effect from a, b, and standard error • http://www.quantpsy.org/medmc/medmc.htm

48. 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