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.
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.
So: 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?
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?