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This is the R_Code file. | 447 bytes |
This is the dataset. | 13.01 KB |
Dear Mike,
This is Wen-Ta from Department of Applied Foreign Languages, National Taiwan University of Science & Technology.
I am very interested in meta-analysis and now applying your metaSEM package to my research project.
May I possibly ask a question about handling missing data with moderators of more than tree categories:
After I created four indicator variables for the four categories of the moderator (Proficiency Level), and then I applied the 'meta3X' code and specified the level of analysis (x2), I failed to obtain the parameter estimates.
Adv <- ifelse(SA2$Level=="Advanced", yes=1, no=0)
Int <- ifelse(SA2$Level=="Intermediate", yes=1, no=0)
Base <- ifelse(SA2$Level=="Basic", yes=1, no=0)
Mix <- ifelse(SA2$Level=="Mixed", yes=1, no=0)
summary( Model2 <- meta3X(y=y, v=v, intercept.constraint=0, x2=cbind(Adv, Int, Base, Mix), cluster=studyID, data=SA2, model.name="Model 2") )
Where did I go wrong? I am wondering whether you can guide me the correct way of doing this?
I have attached the dataset, R codes, and output for your reference.
Best Regards,
Wenta
Dear Wenta,
meta3X() uses FIML to handle missing covariates. It means that the covariates are assumed multivariate normal. I don't think that it works for categorical variables.
One approach is to apply MI on the covariates first. Then you may use meta3() on the imputed datasets.
Best,
Mike