I am planning to do a multigroup SEM analysis. I gathered survey data and calculated a survey weight. Some of my variables have item nonresponse (mostly around 5% missings).
I´ve decided to use multiple imputation to handle the missing data. First, i used
LittleMCAR() test to check for the missingness mechanism. I also used
TestMCARNormality() from Jamshidian et al. which has a nonparametric test of MCAR for homogenity of covariances. The latter didn´t reject MCAR, the
LittleMCAR test did (p=8.3%). Because i assume my data to be MAR, my data was split in men/women and I applied the
LittleMCAR() test for each subgroup. This time MCAR was not rejected in both subgroups.
I´ve read (see: Enders, C., & Gottschall, A. (2011). Multiple Imputation Strategies for Multiple Group Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 35-54.) that if I plan to do a multigroup SEM analysis, I should do a separate multiple imputation for each group (in this case: men/women). The R package
MICE will be used for the imputation.
Now my questions:
1.) Should use the default "massive imputation" predictormatrix from MICE
predictorMatrix = (1 - diag(1, ncol(data)), that uses all variables from the dataset as predictors for the imputation model, or should i use
quickpred() to generate a
quickpred uses some criteria (like correlation of predictor and target-variable) to select a set of predictors for each variable, that will be imputed.
quickpred(datensatz_gender_0, include=c("weight_trunc"),exclude=c("ID","X","gender"),mincor = 0.1)
2.) Should I include the survey weight in the predictor matrix?
After imputation, the list of imputed datasets will be given to the survey()-package (for weighting purposes), then i will use the
lavaan to specify my model, which will use the imputed data survey object. This lavaan model will then be passed to
lavaan.survey(), so I can use the survey weights together with the imputed data. As far, as I´ve understood,
lavaan.survey will then pool the results...
It would be great, if somebody can give me an answer to this question. Thank you!