Advantages of Monte Carlo Confidence Intervals for Indirect Effects

Abstract

Monte Carlo simulation is a useful but underutilized method of constructing confidence intervals for indirect effects in mediation analysis. The Monte Carlo confidence interval method has several distinct advantages over rival methods. Its performance is comparable to other widely accepted methods of interval construction, it can be used when only summary data are available, it can be used in situations where rival methods (e.g., bootstrapping and distribution of the product methods) are difficult or impossible, and it is not as computer-intensive as some other methods. In this study we discuss Monte Carlo confidence intervals for indirect effects, report the results of a simulation study comparing their performance to that of competing methods, demonstrate the method in applied examples, and discuss several software options for implementation in applied settings.

ACKNOWLEDGEMENTS

The authors wish to thank Nurit Tal-Or, Jonathan Cohen, Yariv Tsfati, and Albert C. Gunther for use of their data in illustrative examples. We also thank Andrew Hayes for helpful comments.

Notes

¹In order for the results of a mediation analysis to have a causal interpretation, several additional assumptions must be met. For overviews see Imai, Keele, and Tingley (2010), Muthén (2011), and Pearl (2010).

²See http://quantpsy.org/

³See http://afhayes.com/