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 stats787 posted on Tuesday, June 14, 2011 - 2:23 pm
Dear Dr. Muthen,
I ran LCA for a set of 6 binary variables using 3-class model. I obtained the output, but I am not sure how do I group the variables based on the output. So based on the following output, can I assume latent class 1 contain V1, V2, V3, V5 and V6 since they have p-values < 0.05 in the last column.
Thank you very much.
P
Latent Class 1
V1
Category 1 0.858 0.038 22.463 0.000
Category 2 0.142 0.038 3.710 0.000
V2
Category 1 0.935 0.027 34.526 0.000
Category 2 0.065 0.027 2.405 0.016
V3
Category 1 0.844 0.035 24.097 0.000
Category 2 0.156 0.035 4.441 0.000
V4
Category 1 0.855 0.074 11.601 0.000
Category 2 0.145 0.074 1.967 0.049
V5
Category 1 0.845 0.027 30.746 0.000
Category 2 0.155 0.027 5.650 0.000
V6
Category 1 0.944 0.025 38.329 0.000
Category 2 0.056 0.025 2.273 0.023
 Bengt O. Muthen posted on Tuesday, June 14, 2011 - 11:43 pm
No, don't go by the p-values. The easiest way to understand which items measure which classes well is to plot the item profiles - that is, the item probabilities for each class. You can use the PLOT command of Mplus to do that.

Also see the handout and video of Topic 5 on our web site regarding LCA.
 stats787 posted on Wednesday, June 15, 2011 - 12:19 am
appreciated your advice. Thanks Dr. Muthen!
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