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From |
"Hoogendoorn, Adriaan" <A.Hoogendoorn@ggzingeest.nl> |

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

Subject |
RE: st: Little's MCAR test |

Date |
Wed, 18 Nov 2009 17:02:14 +0100 |

Dear Maarten, Some powerful lines you wrote there (again). Tnx. Please note that Little's test (and my question) concern the stronger MCAR assumption. This is obviously easier to test than the MAR assumption. Still, your suggestion seems to make sense even for this situation. Yet, Little's MCAR test is somewhat more general, since it tests the MCAR assumption over several variables with missing values simultaneously. I've seen the test implemented in other statistical software. Kind regards, Adriaan ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis [maartenbuis@yahoo.co.uk] Sent: Wednesday, November 18, 2009 11:42 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Little's MCAR test --- On Wed, 18/11/09, Hoogendoorn, Adriaan wrote: > I use Stata's facilities for Multiple Imputation to solve > my missing data problem. > I'm motivated to do so, since I "think" that the missing > data pattern is not Missing Completely At Random (MCAR). > I'd like to sustain my "thought" by testing MCAR and read > in the literature about a test for this purpose. > Do you know of a package to do Little's MCAR test? I don't know this particular test, but one thing you can do very easily is test whether the probability of having a missing value on one of the explanatory variables is associated with the explained variable. The logic is that missing values only influence the results if the probability of missingness is associated with the dependent variable. A simple proof can be found in footnote 1 of Allison (2002). You obiviously cannot test whether the probability of missingness on the dependent variable depends on its own unobserved value, but you can perform this test for the explanatory variables, as is shown in the example below for a continuous dependent variable (wage) and a dichotomous dependent variable (collgrad). *------------ begin example -------------- sysuse nlsw88, clear gen byte miss = missing(union, tenure) ttest wage, by(miss) tab miss collgrad, row chi2 *------------- end example --------------- Hope this helps, Maarten Allison, Paul D. Missing Data. Quantitative Applications in the Social Sciences, nr. 136. Thousand Oaks: Sage. -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Little's MCAR test***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: Little's MCAR test***From:*"Hoogendoorn, Adriaan" <A.Hoogendoorn@ggzingeest.nl>

**Re: st: Little's MCAR test***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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