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Jon Elhai posted on Friday, November 30, 2012 - 6:13 pm
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Linda, Do you have sample input syntax for a CFA measurement model, whereby the observed variables are count variables, with zero-inflated negative binomial regression paths as factor loadings? I could not find zero-inflated syntax examples in the Mplus Manual. |
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Use the regression example ex3.8 as guide. |
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Jon Elhai posted on Saturday, December 01, 2012 - 11:25 am
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Bengt. Would I simply substitute "BY" for the "ON" statements in example 3.8? Or is there something else needed to transfer the syntax in 3.8 to a CFA model? |
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I think using BY is all you have to do. |
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Jon Elhai posted on Tuesday, December 04, 2012 - 7:59 am
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Bengt. You mentioned that taking example 3.8 and substituting BY for the ON statements would give me neg binomial or poisson (or zero-inflated) regression paths within CFA. But in 3.8, the variable preceding the word "ON" is the count dependent variable. But in a "BY" command for CFA, the variable preceding the word "BY" would not be the count variable, but rather would be the latent factor; the variables after the "BY" would be the count variables. So how I would turn 3.8 into a CFA with something like negative binomial factor loadings estimated? |
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f BY y; is the same as y ON f; So you just specify u1-u10, say as count negbin: COUNT = u1-u10(nb); and then say f BY u1-u10; |
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Tom Booth posted on Wednesday, June 12, 2013 - 4:13 am
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Dear Linda/Bengt, I am fitting a CFA model with 5 indicators. 3 are ordered categorical and 2 are count variables with a high proportion of zeros. The model is fit with MLR and numerical integration with logit link. I have a number of questions about this model: 1) Is it reasonable to fit zero-inflated parameters within CFA? 2) If the answer to (1) is yes, does the inflation parameter get included as a factor indicator? e.g. f by u1 u1#1 u2 u2#1 .... 3) I am also not sure which combination of standardizations I would need to report in this model for the values to be comparable. For example, I assume the inflation params would be STDY as this is binary. Having run a couple of variates of this model, I tend to receive estimates and p-Values in the raw and STD solutions, but estimates of 1.00 and associated -999 for the count variables in the STDYX results. Any assistance would be warmly received. As always, apologies if this has been answered elsewhere but I have struggled to find it. Thanks Tom |
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1. Yes. 2. You would have factors with count indicators and factors with inflation indicators. You would not use them in the same factors. 3. Standardization is not done with count variables. |
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Tom Booth posted on Wednesday, June 12, 2013 - 2:17 pm
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Thanks Linda. Can I ask why you would fit difference factors for the inflation indicators. Is this because the zero inflation models assume 2 processes are underlying the patterns of responses in the count variables and thus 2 latent factors are required? Best Tom |
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What influences the inflation probabilities may be different from what influences the number of counts among those in the non-zero class. |
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Tom Booth posted on Wednesday, June 12, 2013 - 11:27 pm
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Thanks both. In sum: 1) Its fine to model inflation params in CFA. 2) Model them on a separate factor to model different influences. 3) Report unstandardized values. Sorry for what may be a further very simplistic question, but is there a good reference/reading for why counts are not standardized? thanks |
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In a model where a residual variance is not an estimated parameter, standardization with respect to y cannot be done. You can standardize with respect to x. I know of no article that addresses this. |
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Rob Dvorak posted on Monday, April 21, 2014 - 8:35 am
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Hi there, I'm running some CFA models using behavioral observation data where the variables are counts (the number of X type of utterance), but unlike most count variables, their distribution does not really approach a Poisson or Negative Binomial distribution because it's extremely skewed (e.g., the median may be 2 or 3, but valid cases have counts over 50). In fact, when I run count models in Mplus, it gives a warning that counts exceed 50 and perhaps a continuous model would be better. My sense is that there is no ideal model for data distributed this way (count data that are extremely positively skewed), but I figured I would ask if you have any recommendations. |
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Is there a substantive reason for the counts over 50. Do they represent a different subpopulation? |
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Dear Linda and Bengt, I am fitting a CFA model with four indicators: two are continuous variables and two are zero inflated count variables. The estimator is MLR. I understand from the posts above that it is reasonable to fit zero-inflated parameters within CFA, but that I should model the count and inflation indicators in separate factors. I'm unsure of how to model this. Should I model one factor with all four indicators and a separate one for the inflation indicators? If so should these factors then load on to an overall factor as below? COUNT ARE u3 (i) u4 (i); USEVARIABLES u1 u2 u3 u4; MODEL: f1 BY u1 u2 u3 u4; f2 by u3#1 u4#1; f3 BY f1 f2; I would appreciate any advice you might have. Many thanks. |
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Q1: That's fine. Q2: No - you need at least 3 first-order factors to identify a second-order factor. It is sufficient that f1 and f2 correlate. |
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Dear Bengt, Thank you very much for your reply. I have one further question. I understand that I should report unstandardized factor loadings for the binary variables in this CFA. Should I also report unstandardized coefficients when using this latent variable as a predictor in a regression model? Many thanks in advance. |
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Use STD for the loading - this gives it for factor variance 1. I would use standardized coefficients for the factor as a predictor. The only issue is that you don't want to standardize with respect to Y when Y is a count DV. |
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