An article by Laura Stapleton came out in the most recent issue of the SEM journal which shows how to use SAS in conjunction with Mplus and replicate weights. We will eventually be adding this to Mplus. In the meantime, you can see that article.
I'm using the ECLS-K data and am confused after reading the above paper and the others by Tihomir on analyzing multilevel complex samples in Mplus -- about which weights to use for these data. The file for the longitudinal data comes with both PSU and Strata weights, and full sample weights and Jackknife2 replicate weights for the kids.
Since I want to take the unequal selection probabilities of the schools into account for my longitudinal analysis, I'm thinking that I should set up a growth model multivariate style, but as a multilevel model to take PSUs and strata into account.
So, do I use the Taylor linearization weights for the clusters and strata for the multilevel part, as well as the full sample and JK2 replicate weights for the kids?
Dan Li posted on Wednesday, June 10, 2015 - 8:27 pm
Hi Dr. Muthen,
I am using PISA data to do a SEM. PISA use Fay's replicate weights. I read your paper "Asparouhov, T., & Muthen, B. O. (2010). Resampling methods in Mplus for complex survey data. Los Angeles: Muthen & Muthen", you provide syntax for using replicate weights:
Hello, If I only have the weight (and not strata and cluster) for my data, and I want to get bias-corrected bootstrapped confidence intervals for indirect effects in a mediation model, is it correct to do the following under the analysis and output commands?
Is repse=bootstrap needed under analysis? Or can replicate weights not be calculated when we do not have strata and cluster?
Dear Dr Muthen, I am using PISA data to do SEM with one dichotomous variable and several continuous variables. I use WLSMV method of estimation. How can I get fit indices for the model? can I also test indirect effects in mediational part of the model? Thanks, Sylwia
1. Probably assuming the CLUSTER = b_weight; STATIFICATION = c_weight; really specify the clusters and the strata. These are not weights.
2 and 3. You can remove BOOTSTRAP = 2000; REPSE = BOOTSTRAP; and you will get fit indices. The bootstrap method concerns standard errors. It doesn't change the point estimates. The bootstrap version of chi-square (Bollen-Stine) is not available yet in these complex sampling settings.
For a follow-up question regarding your response on my Q2 and Q3. If I am testing indirect effects in my path model, can I keep these two commands (BOOTSTRAP = 2000; REPSE = BOOTSTRAP; ) in my syntax so that I can get a bootstrap estimation for indirect effects?