If somebody had some experience of how to deal with the following missing data situation: We have a survey that use the systematic sampling and there are total of 258 records. There are 98 cases that have at least some missing data on the 3 outcome measures. There are 33 cases that have more than 10% of the items of the outcome variables missing. Here comes the analysis, we have different ideas and don't know which one is correct: One idea is to delete those 33 cases from the inferential analyses... Then, the next step is to "replace" the random items that are missing on the other 65 cases. The question is; 1. should we do the imputation for this analysis? Or just delete the missing records? 2. if we do the imputation, should we do just once or should we do the ITERATE? We are argued that the entire idea of imputation of missing values is to ITERATE through replacement. Thus, we would replace once, do regression, replace again, do regression, etc., and then average over the results. If you only replace missing values once, your confidence intervals are incorrect. If you are going to replace values by the mean/median of local results, you have to somehow account for this in subsequent analysis. you can't just then analyze the "replaced" data as if they were the true results. Because there is no reason why anyone's record should be related to anyone else's. Any ideas? Julia Zhu, MS ASSOCIATE SERVICE FELLOW Statistics CDC/OD/OWCD/Science Office (Office) 404-498-2382 (Fax) 404-498-6365 MS E-94 (Email) [email protected] -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20090225/4c4b12bd/attachment.htm From drj10 <@t> psu.edu Thu Feb 26 19:44:38 2009 From: drj10 <@t> psu.edu (David R Johnson) Date: Thu Feb 26 19:44:43 2009 Subject: [Impute] The new Multiple Imputation procedure in SPSS 17 Message-ID: <[email protected]> Has anyone used the new multiple imputation module in SPSS? We have been trying it out and it seems to be pretty fast, but the documentation is very sparse. We would be interested in hearing other folks experiences with this module. David J. -- David R. Johnson Professor of Sociology, Human Development and Family Studies, and Demography Department of Sociology 713 Oswald Tower The Pennsylvania State University University Park, PA 16802 tel: 814-865-9564
