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From |
David Airey <david.airey@Vanderbilt.Edu> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sun, 2 Dec 2007 11:39:57 -0600 |

.

I have trouble understanding the translation of these three missing situations into when it is useful to impute.

-Dave

On Dec 2, 2007, at 11:35 AM, Maarten buis wrote:

--- Nick Cox <n.j.cox@durham.ac.uk> wrote:The problem here is that now you are talking about what is known in theMissingness 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.

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/

-----------------------------------------

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-- David C. Airey, Ph.D. Pharmacology Research Assistant Professor Center for Human Genetics Research Member Department of Pharmacology School of Medicine Vanderbilt University Rm 8158A Bldg MR3 465 21st Avenue South Nashville, TN 37232-8548 TEL (615) 936-1510 FAX (615) 936-3747 EMAIL david.airey@vanderbilt.edu URL http://people.vanderbilt.edu/~david.c.airey/dca_cv.pdf URL http://www.vanderbilt.edu/pharmacology * * 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**:**Re: st: RE: Re: Missing values test***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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