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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
st: RE: Factor analysis(?) question - missing data |

Date |
Tue, 22 Apr 2008 19:42:39 +0100 |

To state the obvious, missing data are always problematic and your case seems worst than most in that the optimal way to impute depends on the structure of relationships that a factor analysis is (presumably) intended to discover -- or to test. (Apologies; factor analysis isn't my kind of religion and I may not get the wording right.) The risk of circular argument seems very great. Others will no doubt suggest currently standard solutions but in this case perhaps there is scope for a tailored iterative approach. Factor analysis on complete observations may suggest weights for imputing the variable with least missing values. Factor analysis on (the ideally then greater) set of observations may then suggest weights for imputing the next least problematic variable. And so on. In general, keeping track of weights as they will change will highlight stable and unstable characteristics. That doesn't rule just averaging what you have as a stark comparison. More generally, looking for an optimal solution to this kind of problem seems less appropriate than trying two or three different solutions and seeing what agreement you get. Nick n.j.cox@durham.ac.uk Glenn Hoetker This is perhaps more of a statistical questions than a Stata question. My situation is this. I have a large dataset in which there are 5-6 indicators each for a bunch of latent variables. Let me take as an example having 5 measures for innovative output, x1-x5. The problem is that very few observations have all 5 measures; some are missing x1, some x2, etc. Almost every observation has at least 3 measures and most 4. Is there anyway to optimally combine these indicators to measure the underlying construct of innovative output that would use all available measures for a given observation, i.e., x1-x4 for one observation, [x1- x3,x5] for another, etc. If I thought these were equally weighted, I could just average over the available variables in each, setting aside issues of measurement error. However, I'm not convinced they are equally weighted and would like to do this in a more rigorous fashion. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Factor analysis(?) question - missing data***From:*Glenn Hoetker <ghoetker@uiuc.edu>

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