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From |
"Anne Jurczok" <jurczok@uni-potsdam.de> |

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

Subject |
st: remaining missings after multiple imputation |

Date |
Mon, 19 Apr 2010 14:39:29 +0200 |

Hi, I am currently trying to do a multiple imputation for a dataset about affluence and wealth. My sample consists of 472 households. I started off with ice but switched to the mi-commands of Stata 11. The following problem occurred for which I would kindly ask for your advice and help: Not all cases of my dataset are considered for the imputation. Explanation: The main variable of interest is ?lnvermgen? (variable of the asset for one household). However, some predictor variables which I chose (according to van Buuren/Bohuizen/Knook 1999) have missing data as well. I created a logarithm of the continuous variables to relaxe the assumption of multivariate normality. Additionally, I transformed the categorical variables into dummies in order to be able to use mi impute mvn (suggested by Allison 2002). I decided against using ice, since I have different types of missings in my dataset (hard and soft missings) and I couldn?t find any literature about different types of missings handled by ice. Therefore, my model is based on the mi impute mvn command, because my data are MAR, non-monotone and have multivariate missings. mi impute mvn lnvermgen v1_14_p v1_16_p_5 v1_16_p_7 stib_gen_b v3_3_b v3_5 v3_9 /// v3_16_b v3_28 v3_29 erbsumme = v1_14_b v1_16_b v3_1_2 v3_2 v3_8_2 v3_8_4 v3_8_7 /// v3_40, add(20) force I receive the following output: Multivariate imputation Imputations = 10 Multivariate normal regression added = 10 Imputed: m=1 through m=10 updated = 0 Prior: uniform Iterations = 1000 burn-in = 100 between = 100 Observations per m Variable complete incomplete imputed total lnvermgen 232 95 73 327 v1_14_p 320 7 5 327 v1_16_p_3 323 4 3 327 v1_16_p_5 323 4 3 327 v1_16_p_6 323 4 3 327 v1_16_p_7 323 4 3 327 stib_gen_b 325 2 1 32 v3_3_b 289 38 23 327 v3_5 304 23 18 327 v3_9 288 39 28 327 _16_b 321 6 1 327 v3_28 323 4 3 327 v3_29 311 16 11 327 erbsumme 322 5 4 327 (complete + incomplete = total; imputed is the minimum across m of the number of filled in observations.) PROBLEM: Not all cases of my dataset are considered for the imputation. E.g. lnvermgen had 137 missings in 472 cases, only 73 of them were imputed, same happened with the other imputed variables I researched in the handbook and the statalist archive; moreover, I searched google, but couldn?t find a hint on this specific problem. Also, I rechanged the dataset and the model in different ways, but received the same output. Therefore my question: does anybody know why not all missings are considered in the process of imputation? And how can I solve this problem? Thank you in advance, Anne Jurczok -- Anne Jurczok Universitaet Potsdam Humanwissenschaftliche Fakultaet Department for Education * * 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: remaining missings after multiple imputation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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