I have a data set where individuals, nested in groups were questioned at different points in time. I want to show the relationship between two variables, measured at the individual level (communication and team performance) on an intraindividual level (i.e., within the individuals over the multiple time points. Yet I still need to control the data's nestedness (in individuals as well as groups). Now, if I understood it correctly, this is what would classify as a three-level model. Moreover, I assume a quadratic relationship between the two variables.
This is what I assume my model should look like:
usevar = Team MemberID Communication TeamPerf sCommunication; missing = all(-99);
CLUSTER IS Team MemberID; WITHIN ARE Communication sCommunication; define: sCommunication = Communication*Communication;
MODEL: %WITHIN% TeamPerf on Communication; TeamPerf on sCommunication;
once again thanks for the quick response. I just realized I had overread your comment with regards to mean-centering ("In your Define command you want to subtract the mean before creating the product").
Would you always recommend centering before creating quadratic terms? In this case, this would refer to centering around the variable's grand mean, right?
Having done so, my linear term (which had reached significance before, as had my quadratic term) has become insignificant. I now fear that albeit the still significant quadratic term, I now cannot interpret the results. What would you say?
Hello! I see this sentence in the V8 Manual: "Complex survey features are not available for TYPE=THREELEVEL with categorical variables or TYPE=CROSSCLASSIFIED because these models are estimated using Bayesian analysis for which complex survey features have not been generally developed" (p. 262).
My questions are: 1) Does "categorical" above refer to independent variables, dependent variables, or both? 2) Does this mean that NO complex survey features can be used in TYPE=THREELEVEL with categorical variables, including both weights and strata? 3) Does the sentence above also hold true for Version 8.4?
I did notice, in running a three-level model with survey weights, however, that the SUBPOPULATION command is not available for TYPE=THREELEVEL.
Is this because this survey feature has not yet been developed for Mplus, or has it not been developed more generally (e.g., the underlying statistical work needed)?
Or, is it not needed for another reason, statistically, in the three-level case? Typically, subpopulation options ensure accurate standard errors when conducting a survey weighted analysis for a subpopulation.
Thank you again for your support and the wonderful Mplus software! Lisa