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anonymous posted on Tuesday, April 24, 2018  11:32 am



Does anyone have examples of syntax for the twostep method for testing predictors of latent classes recently published http://stats.lse.ac.uk/kuha/Publications/Bakk_Kuha_Two_step_latent_class_analysis.pdf ? Can this be done while estimating missing data with maximum likelihood? 


Missing data is not a problem. Here is an example for you. This is the example from Section 3 in http://statmodel.com/download/webnotes/webnote15.pdf The syntax for step 1 is the same as the syntax for step 1 in the three stage procedure  Appendix D http://statmodel.com/download/AppendicesOct28.pdf The second step input file is this (much of it is automatically generated by Mplus if you add output:svalues; to the Appendix D run): variable: Names are u1u10 y x p1p3 n; usevar are u1u10 y x; categorical=u1u10; classes = c(3); data: file=man3step2.dat; Analysis: Type = Mixture; starts=0; Model: %OVERALL% Y on X; [ c#1@0.05439 ]; [ c#2@0.03804 ]; %C#1% Y on X; [ u1$1@0.93113 ]; [ u2$1@0.77363 ]; [ u3$1@0.85349 ]; [ u4$1@0.94545 ]; [ u5$1@0.66272 ]; [ u6$1@0.71896 ]; [ u7$1@0.93411 ]; [ u8$1@0.81936 ]; [ u9$1@0.95383 ]; [ u10$1@0.70511 ]; %C#2% Y on X; [ u1$1@0.82326 ]; [ u2$1@1.21277 ]; [ u3$1@0.76346 ]; [ u4$1@0.84224 ]; [ u5$1@1.17576 ]; [ u6$1@0.89084 ]; [ u7$1@0.87685 ]; [ u8$1@0.93901 ]; [ u9$1@0.99801 ]; [ u10$1@0.90357 ]; %C#3% Y on X; [ u1$1@1.28491 ]; [ u2$1@1.11949 ]; [ u3$1@1.12317 ]; [ u4$1@1.44773 ]; [ u5$1@0.83887 ]; [ u6$1@1.15671 ]; [ u7$1@1.27923 ]; [ u8$1@0.91518 ]; [ u9$1@1.13319 ]; [ u10$1@1.01133 ]; 


I would like to use the new twostep procedure suggested by Bakk & Kuha (2018, Psychometrika) for an LTA. For this purpose I have estimated two separate LCA’s (input and output files attached). However, when I feed the starting values from these (using the output from “svalues”) into a LTA, the classes change. To clarify: in this second step I am only interested in the structural parameter linking the two LCA’s (c1 and c2). I wonder if perhaps you could you provide some help with the correct syntax for the second step? Thank you very much. 


Does this article deal with LTA or are you trying to generalize it from LCA to LTA? 


The example in the article primarily deals with LCA. However, they note (on p. 877) that the method easily extends to more complicated cases. They write: "For instance, suppose that there are two latent class variables X1 and X2 with separate sets of indicators Y1 and Y2, and the structural model is of the form p(X1)p(Z1X1)p(X2Z1, X1)p(Z2X1, Z1, X2). In step 1, we would then estimate two separate latent class models, one for X1 and one for X2 (and both again without Z = (Z1, Z2)). Step 1 parameters θ1 would be the measurement probabilities of X1 and X2 and the parameters of p(X1), and step 2 parameters would be those of the rest of the structural model apart from p(X1)." 


(in the version of the paper linked to at the top of this discussion the quoted text is on p. 10) 


Yes, looks like LTA might be covered. I haven't tried out this approach but send your output from the first and second step to Support along with your license number and we'll try to help you. 

Sara Suzuki posted on Sunday, December 29, 2019  9:10 pm



Is the Bakk & Kuha method better than DCAT even for categorical distals? 


We haven't seen any studies that indicate this. The DCAT approach appears to work quite well, see Table 8 http://www.statmodel.com/download/3stepOct28.pdf In most situations, I would expect the DAT method, the twostep method, the threestep method and the BCH method to yield similar results. If you have an example where that is not the case send it to support@statmodel.com 

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