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Good Afternoon, I am trying to conduct a latent profile analysis using the items of a scale as the 10 indicator variables. Some of these items are zero-inflated; others are normally distributed. To run a zero-inflated model, is the only addition to my syntax the COUNT statement: AUXILIARY = bya (e) typ (e) drug (e) h1 (e); COUNT = slp1-slp10; classes = slp(2); ANALYSIS: type = mixture; If only some of the indicators are zero-inflated, is there a different strategy that would be more beneficial? Thanks! |
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Are you variables counts? How exactly are they measurement. |
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The variables are on a 5-point scale with "Never" as one of the anchors, and "Always" on the other anchor. A few of the items have a typical zero-inflated distribution, with a large stack on the "Never." |
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You should treat this variable as a categorical variable using the CATEGORICAL option. Categorical data methodology can handle floor or ceiling effects. |
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In an LPA model, can some indicators be categorical and some continuous? Or is it better to just treat all the items on the scale as categorical if a couple of them need to be modeled as categorical to handle floor effects? |
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You can have a blend of indicator types: binary, ordinal, continuous, count. Plus you can also have indicators that are latent variables as we show in one of our course handouts. |
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