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Can I conduct a second order LCA on two sets of related but not identical sets behaviors? For example, risk behaviors and academic behaviors. I would expect them to be related to one another but not the same underlying construct. Membership in each of the 2 firstorder classes would be based on three binary indicators. For example, patterns of academic behaviors (c1) measured by 3 observed binary variables u11, u12, u13. Patterns of risk behaviors (c2) measured by and three observed binary variables u21, u22, u23. Could I do a second order LCA to determine classes (f) of joint patterns of academic behaviors and risk behaviors? Thanks, Samuel 


Yes, this can be done. You have an example of a secondorder LCA (actually a more complex LTA) in UG ex8.15. You have say 2 1storder latent class variables c1 and c2 and a 2ndorder latent class variable c. You relate them by saying c1 c2 on c; And you use the construction Model c1: .... Model c2: ... 

Alvin posted on Friday, June 05, 2015  3:59 pm



Hi Dr Muthen, I am looking at the dyadic profiles of characteristics of husbands and wives (having run two independent LCAs for each group and achieved a two and a threeclass solution for husbands, and I am looking at a joint LCA). In testing a secondorder LCA, I understand that class invariance will have to be established across the two groups (as in multigroup LCA)? Would it matter if one has two different class solutions in this case? Next, I'll look at predictors of jointclass memberships.. 


Perhaps you are saying that some classes are the same and some different across husbands and wifes? If so, you can hold measurement parameters equal for the same classes. 

Alvin posted on Friday, June 05, 2015  4:17 pm



yes that's what I meant  many thanks! 

Alvin posted on Monday, June 22, 2015  12:06 am



Hi Dr Muthen, I am running a second order LCA based on a dyadic dataset (in wide format), looking at joint profiles or distributions amongst husbands and wives living in the same household. Would I have to transform the data from wide to long (with a variable identifying the dyad, husband/wife) and set it up using classspecific commands for the analysis? Or would it be possible to analyze the data in wide format? Many thanks 


You can analyze it in wide format. The nonindependence of observations is handled by multivariate modeling. This is discussed in both the Topic 7 and Topic 8 course handouts and videos on the website. 

SRL posted on Friday, February 16, 2018  1:31 pm



Hi, I am interested in a higherorder latent class variable indicated by two firstorder latent class variables. I have specified an LTA relating the two latent class variables to each other and a higher order LCA with one class. When looking at the results of the two different models, I get different loglikelihoods and slightly different proportions across the joint distribution of the two latent class variables, when I expected them to be the same. Any insight into why they may be different? 


I assume the LTA model is c1 > c2. But that is the model that has "a higher order LCA with one class"? A latent class variable with one class is not a variable. 

SRL posted on Tuesday, February 20, 2018  3:13 pm



To clarify, I specified two different models. I have “c2 on c1” for the LTA model. For the higher order LCA,I specified classes "c(1) c2(2) c3(4);” and compared the joint distribution of the two latent class variables for the LTA model and higherorder. They were slightly different (see below) and I’m trying to make sense of this difference and if its meaningful. I cannot get the higher order latent class variable with more than one class to converge. 

SRL posted on Tuesday, February 20, 2018  3:14 pm



FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON THE ESTIMATED MODEL Latent Class Pattern 1 1 1 216.34917 0.06799 1 1 2 407.09808 0.12794 1 1 3 1607.95824 0.50533 1 1 4 317.52216 0.09979 1 2 1 53.73423 0.01689 1 2 2 101.11018 0.03178 1 2 3 399.36556 0.12551 1 2 4 78.86238 0.02478 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON THE ESTIMATED MODEL Latent Class Pattern 1 1 136.76939 0.04298 1 2 349.36717 0.10979 1 3 1722.88154 0.54145 1 4 339.90955 0.10682 2 1 133.31656 0.04190 2 2 158.83728 0.04992 2 3 284.44515 0.08939 2 4 56.47337 0.01775 


Send the output for the 2 models to Support along with your license number. 

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