Bootstrap mediation test with complex... PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 Randall MacIntosh posted on Tuesday, September 06, 2011 - 9:56 pm
I would like to test for mediation using the INDIRECT, CINTERVAL and BOOTSTRAP commands. But I have a complex sample. Will the Bootstrap function properly with weighted clustered data?

Am I correct in assuming that I don't have to use the TYPE=COMPLEX specification because the bootstrap will provide the appropriate variance estimates?

 Linda K. Muthen posted on Wednesday, September 07, 2011 - 9:44 am
BOOTSTRAP cannot be used with COMPLEX. BOOTSTRAP does not take into account lack of independence of observations. You need COMPLEX for that.
 Sarah Ryan posted on Thursday, September 15, 2011 - 11:01 am
I am using data from the ELS:2002 data set (an NCES data set). Is it possible to use "Resampling Methods in Mplus for Complex
Survey Data" (Tihomir Asparouhov and Bengt Muthen, May 4, 2010) as a guide in obtaining bootstrap standard errors? If so, would bootstrap SE's also then be provided for the MODEL INDIRECT output?

Like the example given with ECLS data in this paper, the sampling structure is available in the ELS:2002 data, i.e., the strata and PSU (cluster) variable are available in the sample.
 Tihomir Asparouhov posted on Thursday, September 15, 2011 - 11:33 pm
Yes on both questions. You can obtain the bootstrap SE using
and "model indirect:" is available as well.
 Sarah Ryan posted on Friday, September 16, 2011 - 9:10 am
Excellent. Thank you.
 Stat posted on Saturday, April 19, 2014 - 1:32 pm
I try to do a mediation and compute indirect/direct effects using SEM with latent variables and a complex sample design (Cluster, Stratum and weight). Since I cannot use bootstrap with COMPLEX, I tried to use the "Resampling Methods in Mplus for Complex Survey Data". Since the p values changed slightly, I would like to be able to justify this choice.

1-Is the “Resampling Methods [bootstrap in my case] in Mplus for Complex Survey Data" give SE that are more “accurate”, or does the idea of the resampling methods was implemented to answer specific problems. In other words, when we have complex sample design, is it generally better to use this method, or only in certain (precise) cases?

2-Will it still be possible/adequate to use model constraints to compare strength of my direct and indirect effects?

Thank you
 Linda K. Muthen posted on Monday, April 21, 2014 - 10:03 am
You should use MLR or ML if you are using replicate weights. The indirect effect standard errors are typically okay unless the sample size is small. Bootstrap often does not make a difference.
 Stat posted on Monday, April 21, 2014 - 11:19 am
Thank you!
 Amanda Pollitt posted on Wednesday, June 14, 2017 - 8:06 am
Just for clarification, is it or is it not incorrect to use the "repe=bootstrap" command if you do not have replicate weights?

Said another way, if I have weights, strata, and clustering design effects but not replicate weights, it is appropriate to use "repse=bootstrap" to obtain bootstapped confidence intervals for indirect effects?
 Linda K. Muthen posted on Thursday, June 15, 2017 - 6:06 am
This option is for replicate weights only. I would not using it for any other purpose.
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