Re: st: SEM with categorical variables

[Stas Kolenikov <skolenik@gmail.com>]

-polychoric- recognizes continuous variables (I think I used a cut-off
of 10 or more values; I don't remember if I hard-coded it or whether
you can change that via an option), and computes Pearson correlations
for them. Most fit indices are a function of a few likelihoods (the
saturated, fitted and independence models), and hence of one another,
except for the RMSEA and a couple of others. Stata does not offer much
in terms of the weighted least squares in -sem-, except for ADF...
which is what Mplus may call weighted least squares -- if Cam McIntosh
would stumble upon this thread, he may be able to tell what their
defaults are, and what the p-value corrections are (Satorra-Bentler?)

On Fri, Aug 31, 2012 at 5:23 AM, Duru <duru80@gmail.com> wrote:
> Thanks for the comments, and this code works well. But, when I feed
> that correlation matrix into SEM in Stata it does not take into
> account that the data is ordinal while calculating fit indices. I
> don't know if this is really a problem, but Mplus' black box procedure
> uses weighted least squares and probit regressions while estimating
> the model and they mention that they make adjustments to obtain a
> correct p-value etc.
>
> Another thing is that my model will also include continuous predictors
> then I need to calculate those correlations separately and combine it
> with the polychoric correlation matrix which seems to require some
> craftiness -as yours- with macros.
>
> Best,
>
> Duru
>
>
> On Tue, Aug 28, 2012 at 7:37 PM, Stas Kolenikov <skolenik@gmail.com> wrote:
>>
>> So do you know exactly what Mplus does? Having the software decide for
>> you what to do is good for exploratory phases, but for real research,
>> you have to know what you are doing, and make sure the suggested
>> default is appropriate for your analysis.
>>
>> Incidentally, I've been doing some polychoric-to-sem work earlier
>> yesterday. Here's the outline:
>>
>> local thevars <varlist of the variables of interest>
>> polychoric `thevars'
>> mat polychR = r(R)
>> forvalues i=1/`: word count `thevars' ' {
>>   forvalues j=1/`i' {
>>     local setcor `setcor' `=polychR[`i',`j']'
>>   }
>>   if `i' < `: word count `thevars' ' local setcor `setcor' \
>> }
>> local N = _N
>> clear
>> ssd init `thevars'
>> ssd set obs `N'
>> ssd set cor `setcor'
>> sem ( your model structure comes here)
>>
>> Not terribly elegant, but that's way better than nothing. (Don't
>> assume this will work as a black box; you would be much better off
>> understanding what each line does.)
>>
>> On Tue, Aug 28, 2012 at 3:41 AM, Duru <duru80@gmail.com> wrote:
>> > Dear all,
>> > I am trying to estimate a structural equations model with Stata 12. I
>> > have ordinal items for my latent dependent variable and categorical
>> > manifest independent variables.
>> > I am not clear on how to handle this. I have read somewhere on the web
>> > that estimating polychoric correlations and feeding them into SEM
>> > could be an option or specifying another estimation method (than ML)
>> > would suffice? In Mplus I just need to specify those variables as
>> > categorical and it chooses the proper method by default.
>> >
>> > Many thanks,
>> >
>> > Duru
>> > *
>> > *   For searches and help try:
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>> > *   http://www.ats.ucla.edu/stat/stata/
>>
>>
>>
>> --
>> -- Stas Kolenikov  ::  http://stas.kolenikov.name
>> -- Senior Survey Statistician, Abt SRBI  ::  work email kolenikovs at
>> srbi dot com
>> -- Opinions stated in this email are mine only, and do not reflect the
>> position of my employer
>> *
>> *   For searches and help try:
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>> *   http://www.ats.ucla.edu/stat/stata/
> *
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-- 
-- Stas Kolenikov  ::  http://stas.kolenikov.name
-- Senior Survey Statistician, Abt SRBI  ::  work email kolenikovs at
srbi dot com
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/