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Model fit with both binary & continuo... |
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Hello, I am running a 4-factor CFA using both binary and continuous indicators (ML estimation). Three of the latent factors have continuous indicators while one latent factor has a set of binary indicators. I am interested in the overall fit of the model. The output, however, only gives relative fit indices (the H0 value for -2loglikelihood model comparisons, AIC, BIC) and a chi-square test that appears to be valid for only the categorical portion of the model ("Chi-Square Test of Model Fit for the Binary and Categorical Outcomes"). I am concerned that, although this model may fit better than an alternative (nested) model (e.g., a 3 factor model), the overall fit to the data may be lousy. Is there a means of obtaining an index of absolute fit (e.g., TLI, CFI, RMSEA, Chi-square) for the overall model using both continuous and categorical predictors? If not, how do you suggest I evaluate overall model fit in this case? |
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If you want overall fit measures, you can use the default estimator WLSMV for categorical outcomes for CFA. It sounds like you are using maximum likelihood. |
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Frank Martin posted on Tuesday, September 02, 2008 - 10:40 am
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To follow-up on this question. Is it permissible to have a latent factor with two continuous indicators and one categorical (binary) indicator? Are there any major issues or concerns? I will use a WLS estimator. For the structural model, the latent will be an endogenous variable. Several exogenous variables will be included that are continuous or categorical. Thanks. |
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Yes, a factor can have a combination of categorical and continuous indicators. This is not problematic. I would use WLSMV. |
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