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

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

Subject |
RE: st: RE: Re: Missing values test |

Date |
Sun, 2 Dec 2007 17:48:16 -0000 |

Indeed. I don't find the idea of variables you don't have and that have no connections with any variables you do have that compelling or congenial scientifically, but I bow to any superior wisdom here. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: 02 December 2007 17:36 To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: Re: Missing values test --- Nick Cox <n.j.cox@durham.ac.uk> wrote: > Missingness can always be represented by a dummy. So the structure of > missing data can always be explored by logit regression with > missingness on something as response w.r.t. various predictors, which > may well include missingness on some other things as dummy predictors. The problem here is that now you are talking about what is known in the missing data literature as the Missing Completely At Random (MCAR) assumption. Often three types of missing data are distinguished in this literature: Missing Completely At Random (MCAR), Missing At Random (MAR), and Not Missing At Random (NMAR). Multiple Imputation is based on the MAR assumption. MCAR assumes that every individual has the probability of getting a missing value, i.e. the probability of missingness is not influenced by any variable. This assumption can be investigated for the observed data, in a way suggested by Nick. If you have MCAR or if you can show that the probability of missingness does not depend on your dependent variable, than the safe thing to do is just use the observed cases, as those will give unbiased estimates with correct inference. MAR assumes that the probability of missingness may differ from person to person, but these differences are only caused by observed variables. In order to show that the MAR holds you need to show that the unobserved values of the missing variables do not influence the probability of missingess, which is self-defeating: if you had those unobserved values those values wouldn't be missing. So this assumption is fundamentally untestable. NMAR assumes that the probability of missingness is influenced by both observed and unobserved information. For instance say that persons with a very high or very low income are less inclined to reveal their income in a questionair. -- Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- __________________________________________________________ Sent from Yahoo! - the World's favourite mail http://uk.mail.yahoo.com * * 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/ * * 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/

**Follow-Ups**:**RE: st: RE: Re: Missing values test***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: RE: Re: Missing values test***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: Re: Missing values test***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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