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One LC variable moderating effect of ... |
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I have a latent class variable CN (measured by u1-u10) predicting distal outcome Y. I want to examine if another latent class variable CP (measured by u11-u15) moderates this relationship. Would it be correct to do: Step 1a: LCA for CN on u1-u10, save class membership N1 and get classification error logits Step 1b: LCA for CP on u11-u15; save class membership N2 and get logits Step 3: Use syntax: VARIABLE: NOMINAL = N1 N2; CLASSES = CN(4) CP(3); ANALYSIS: TYPE = MIXTURE; MODEL: %OVERALL% Y on CN; %CN#1% (also for %CN#2%, %CN#3%, %CN#4%) [N1#1@logit] [N1#2@logit] [N1#3@logit] [N1#4@logit] %CP#1% (also for %CP#2%, %CP#3%) [N2#1@logit] [N2#2@logit] [N2#3@logit] Y ON CN; Thanks much for helping me figure this out! |
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You need to add Model cn:... and Model cp: ... See UG examples with more than one latent class variable. Also, you can't have Y ON N because Mplus doesn't regress on nominal variables. Instead, simply say [Y]; to let the mean of Y vary across the classes. Web Note 15 points out, however, that the class percentages may change when you include Y as a DV in the model. You should check for that. This change is what the new DCON option tries to avoid. But DCON can be used only with one latent class variable. |
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Thank you, Dr. Bengt Muthen. I'll definitely check class switching. As for the syntax, in addition to [Y] under %OVERALL%, I am not sure whether to put [Y] under MODEL CN or MODEL CP, or both. I am interested in whether CP moderates the association between CN and Y. Please let me know which I should do. Thank you! MODEL: %OVERALL% [Y]; MODEL CN: %CN#1% (also for %CN#2%, %CN#3%, %CN#4%) [N1#1@logit] [N1#2@logit] [N1#3@logit] [N1#4@logit] MODEL CP: %CP#1% (also for %CP#2%, %CP#3%) [N2#1@logit] [N2#2@logit] [N2#3@logit] [Y] |
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I would actually delete [y] from Model cp, in which case I think the y mean will vary across all the combinations of classes (check this). This is the interaction you are looking for. |
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Thank you. That makes sense! I'll try it. |
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