Message/Author 

Keith posted on Monday, December 12, 2005  12:34 pm



As title, can MPLUS analyze a general threelevel model (e.g., students nested within classrooms nested within schools)? Do you have any example syntax for analyzing this type of dataset? Thanks! 


No, with crosssectional data only two levels are allowed. 


I was wondering if it remains true for MPlus version 6.12 that only two levels are allowed in crosssectional data, or if three levels can now be analyzed? 


Version 6.12 can handle two crosssectional levels. Three crosssectional levels will be available in Version 7. 


Hi Linda and Bengt, 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 threelevel 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; %BETWEEN MemberID% TeamPerf; %BETWEEN Team% [TeamPerf]; Does this look right to you? Thanks! 


Yes, you can do this as a 3level model  or, as a 2level wide model. Your 3level setup looks ok except I think you want TeamPerf variance also on the Team level. In your Define command you want to subtract the mean before creating the product. 


Great, thanks for the quick response! As a followup: I would like the aforementioned relationship (only the quadratic term) to be moderated by time: define: sCommunication = Communication*Communication; mod = sCommunication *time; ANALYSIS: TYPE IS THREELEVEL; MODEL: %WITHIN% TeamPerf on Communication; TeamPerf on sCommunication; TeamPerf on time; TeamPerf on mod; %BETWEEN MemberID% TeamPerf; %BETWEEN Team% TeamPerf; [TeamPerf]; Would this be the way to go? 


Right. 


Hi Bengt, once again thanks for the quick response. I just realized I had overread your comment with regards to meancentering ("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? 


Centering is discussed in Structural Equation Modeling: A Multidisciplinary Journal, 22: 617–630, 2015 TEACHER’S CORNER The Role of Centering for Interaction of Level 1 Variables in Multilevel Structural Equation Models Ehri Ryu In line with regression, look at the combined effect of the linear and quadratic terms using a plot giving significance regions as shown in the Table 1.8 runs from our book at http://www.statmodel.com/mplusbook/chapter1.shtml 


Thanks. One more question: In my last model, which includes the interaction of the quadratic term with time point, this would mean (just on the within level): define: Communication_c = Communication  mean; sCommunication = Communication_*Communication_c; time_c = time  mean; mod = sCommunication *time_c; %WITHIN% TeamPerf on Communication_c; TeamPerf on sCommunication; TeamPerf on time_c; TeamPerf on mod; Right? (with the name "mean" obviously replaced by the variable's mean value). Or would I also have to center the quadratic term (even though it's the product of centered variables)? 


Seems right the way you have it. You typically don't center quadratic terms. 

Back to top 