On 9/5/18 6:16 AM, Spring wrote:
> Hi there, I'm fitting a CFA model with categorical data. Apart from the
> overall fit indices, I was told to look at the residuals, and then I
> found the following advice "if you have categorical indicator variables,
> you’ll want to look at expected vs. observed counts in each level
> instead of residual correlations. You can get that with |lavTables(fit)|".
> So, I looked at the output of lavTables(fit), which gives a two-way
> table showing the frequencies. Yet, how do I suppose to use this to
> evaluate the model fit?? How large should the stats (like X2 and G2) be?
> What rule should I use?
Strictly speaking, X2 or G2 values larger than 3.0 are already 'large'.
But I would first look at the 'largest' values.
A discussion of this can be found in eg
Joreskog, K.G. & Moustaki, I. (2001). Factor analysis of ordinal
variables: A comparison of three approaches. Multivariate
Behavioral Research, 36, 347-387.
> Also, when I include p.values in the function, like lavTables(fit, 2L,
> stat="G2", p.value=TRUE), it doesn't provide a p-value. Any idea why?
Yes: this is a bug (well, more an oversight). Will fix this soon.