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From |
Maarten buis <[email protected]> |

To |
[email protected] |

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

Date |
Sun, 2 Dec 2007 17:58:44 +0000 (GMT) |

--- Maarten buis wrote: > > 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. --- David Airey <[email protected]> wrote: > I have trouble understanding the translation of these three missing > situations into when it is useful to impute. True, that is hard. If you have not many missing values on your dependend variable (explained variable, or y), than you can create a dummy for missingness on the independend variables (explanatory variables, or x-s) and see of that dummy is related to the y. If this is not the case than no imputation is needed, you will get correct estimates and inference if you use only the fully observed observations. If this is not the case, than you are dependent on theory alone. If you think, and can convince your readers, that the probability of missingness depends only on your variables that do not contain missing values than you can do Multiple Imputation (you are convinced that the MAR assumption is satisfied). Notice that this list of variables without missing values is likely differ across individuals in your data, making this a pretty weird assumption. If none of these situations apply, than you are in trouble. -- 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/

**References**:**Re: st: RE: Re: Missing values test***From:*David Airey <[email protected]>

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