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# Re: st: exploratory factor analysis with dichotomous and continuous data

 From Nick Cox To statalist@hsphsun2.harvard.edu Subject Re: st: exploratory factor analysis with dichotomous and continuous data Date Thu, 22 Nov 2012 08:25:14 +0000

```I guess the only other answers are that this variable is unsuitable
for your method or that the method is unsuitable for your data.

Nick

On Thu, Nov 22, 2012 at 6:31 AM, Frauke Rudolf <FRAUKE.RUDOLF@ki.au.dk> wrote:

> I guess, judging from your description, that it is a structural zero. Hemoptysis means coughing up blood, which isn´t really possible without coughing.
> However those are not the only variables, so is there a way to loop this problem in the analysis?
> I don´t think it makes sense to add pseudo-cases.

JVerkuilen (Gmail)

> On Wed, Nov 21, 2012 at 5:37 AM, Frauke Rudolf <FRAUKE.RUDOLF@ki.au.dk> wrote:
>>
>> I found some useful threads on the net, so now I know why I get the message; It is due to one of the dichotomous variables having 0 observations in one of the 2x2 tables:
>> haemoptysi |         cough
>>          s |         1          2 |     Total
>> -----------+----------------------+----------
>>          1 |       168          0 |       168
>>          2 |       896         53 |       949
>> -----------+----------------------+----------
>>      Total |     1,064         53 |     1,117
>>
>> What I could not find, was a solution on, how to deal with this in order to be able to run an EFA.
>> I hope you can help me with this.
>
> First of all is this a sampling zero or a structural zero, i.e.,
> something that is impossible (silly example: male patients of an
> OB/GYN)? I simply don't know the substance to be able to judge. If
> it's a structural zero you need to decide if the EFA model is even
> appropriate. I ask because this is a pretty big sample and thus a
> sampling zero seems unlikely, but I really don't know.
>
> If not, you can add a certain number of pseudo-cases to all cells in
> your contingency table. In the loglinear model literature this is
> called "flattening" and is often necessary to get reasonable
> estimates.
>
> Essentially you have to do this in small doses, adding one, then two
> then three, cases, to make sure that the resulting correlations don't
> shift dramatically.

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