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From |
Frauke Rudolf <FRAUKE.RUDOLF@ki.au.dk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
SV: st: exploratory factor analysis with dichotomous and continuous data |

Date |
Thu, 22 Nov 2012 06:31:19 +0000 |

Thank you for your answer, Jay. 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. Frauke -----Oprindelig meddelelse----- Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af JVerkuilen (Gmail) Sendt: 21. november 2012 13:29 Til: statalist@hsphsun2.harvard.edu Emne: Re: st: exploratory factor analysis with dichotomous and continuous data 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. Jay * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: exploratory factor analysis with dichotomous and continuous data***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: exploratory factor analysis with dichotomous and continuous data***From:*Frauke Rudolf <FRAUKE.RUDOLF@ki.au.dk>

**Re: st: exploratory factor analysis with dichotomous and continuous data***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

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