Multiple Imputation: A Righteous Approach to Handling Missing Data 

Glad to have helped!

Hello Bevan, Please look at Dr. Kumar's response on this link: www.researchgate.net/post/How_can_one_create_a_pooled_dataset_in_SPSS_for_further_analysis There is a part where he states to pool the 5 imputed variables by summing: V1+V2+v3+V4+V5 for instance. However he forgot to say to divide the sum by the number of imputations, in this case 5. So basically you can take the average of the imputed values to derive the pooled values. I am not sure if SPSS now gives pooled standard deviations, and so you may have to do some averaging with standard deviations and standard errors too.

Hello Beautywdbrain, which article did you want? Please give me the name and I will see if I can find a copy. The example I did was for linear regression with a continuous variable outcome. Of course there will be different ways of working with different models. More than I can explain here. Goggle is your friend, do some searching and you will find info. :)

@@omegastatistics thank you for reply. I wanted to know reference of article to quote if I perform the same method as you did for research. Hope this clears

Hello Talal, I am not a Dr., but thanks for thinking so :) The most important is that your data is not MNAR. If your data is MNAR, then imputation and other methods are not going to return good estimates.(read more about missingness and basics of imputation here: www.ncbi.nlm.nih.gov/pmc/articles/PMC2818781/ In this presentation I showed MI using linear regression, which is used for continuous variables. You can also impute nominal variables with logistic regression imputation or discriminant analysis imputation. Here is a link to a paper about imputation of nominal variables: support.sas.com/resources/papers/proceedings/proceedings/sugi30/113-30.pdf

Look at slide 34 in the presentation at this link I think this may help. rmc.ehe.osu.edu/files/2018/02/0.0-Workshop_missing-data-with-SPSS_Finalaudience.pdf

Correlations can be spurious, and caused more by a latent variable. What I meant by not knowing for sure, is that you really don't typically know for sure why something is missing because in many cases you can't go back to ask the respondent or data source what happened. Of course if you know something is systematic then you know. But often you won't know for sure.

If you would like syntax and output, please email me at info@omegastatistics.com with your request. Thanks!

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@Wells Memphis Instablaster =)

@Baylor Tucker thanks so much for your reply. I got to the site on google and I'm waiting for the hacking stuff atm. Looks like it's gonna take a while so I will reply here later when my account password hopefully is recovered.

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You're welcome!

Hi Lidia, Here is a link to start with. There are many references at the end which you can check out too: www.bmj.com/content/338/bmj.b2393#:~:text=Multiple%20imputation%20is%20a%20general,obtained%20from%20each%20of%20them.

Hi Oleg. I know it would be nice, but I am an applied statistician by trade, so although I've been taught the theory, my expertise is in the work. In most of my presentations I provide references that are often more theory based. As for being inconsistent with the literature, things like restraining the limits etc. I wanted to show that it could be done. Many studies are not like the textbooks, not as pretty, and sometimes it is necessary to relax assumptions or allow for limitations. I hope you still enjoyed the presentation.

Thanks for watching Oscar!

Hi Bethany, Here is a link to IBM with info on what you can check. I would check the level of measurement for your variables in the Variable View of your dataset first, then go from there. www.ibm.com/support/pages/multiple-imputation-warning-model-contains-more-100-parameters

Hi Qi, I am not sure exactly what you are asking. I know SPSS doesn't give pooled stats for many things, so if you are asking how to pool the standard deviations etc. Then you can start by checking out this link: stats.stackexchange.com/questions/460238/creating-a-pooled-data-set-from-multiple-imputation-output-in-spss

Hi Collins you will need to remove the NAs otherwise the program will think you have string variables, and imputation works on numeric variable. If there is a way you can put your dataset into Excel, then you can do a search for the NAs by clicking on the arrow by "Find and Select" then "Replace" then under the Find What: type NA (Type it exactly like it is in the data) and then in the Replace with: hit the space key. This will erase all of your NA's. Be sure to make a backup of the file before trying this in case things go awry. Hope this helps

@@omegastatistics thanks will do

Please check with IBM regarding obtaining a license for the Missing Values add-on and adding it to what you have.

Hi Grace, Typically you will impute your data before you run your tests. But of course, make sure your data is nice and clean first, i.e no strange numbers that shouldn't be there, like an age of 200 or things like that.

@@omegastatistics Hi Omega, thanks for prompt reply.

Yes, we all have our favorites :) I am glad you still found the information useful. Here is a nice tutorial in R: data.library.virginia.edu/getting-started-with-multiple-imputation-in-r/

Glad it was useful Black Hero!

It depends on how your data is structured. Also, remember that your data must be MCAR or MAR to use Multiple Imputation. If you are MNAR data then MI should not be used (but is often is). Try to run the imputation and see what you get.

Hi Snigdho, You can easily leave entire variables out of the mix, but cell by cell, you may need to use some code with filters. Here is a link to some information on using filters: wlm.userweb.mwn.de/SPSS/wlmssel.htm

@@omegastatistics thank you

You're welcome

Hi The Suck, all of the imputed datasets are used when you run the analysis. You use the multiple sets and that is why it is called multiple imputation. Choosing any of the analyses with the little sea shell looking icon next to them from the analysis menu does this automatically for you.

You're welcome!

Check into Rubins Rule: www.ncbi.nlm.nih.gov/pmc/articles/PMC2727536/

It is easy if you are using R there is a package called MICE for Multiple Imputation and there is a function called Pool for aggregate your results for any number of iteration

I have similar problem. Did you get an answer on how to do this in SPSS?

you can aggregate you data; go to data menu and select aggregate the function is the mean or mode or median depends on your variable types

Mohamed Hussein please let me know how to do that, i mean can you give specific instruction so i can make a single pooled data. moreover, is there any academic source about how to make a single pooled data? thanks in advance.

Hi Bramantio, SPSS will do that for you if you choose regression and run as usual after imputing. Any processes that can make use of the imputed data will have a little sea shell looking icon next to the routine names in the Analyze menu items.

Yes! Anyone who wants the handouts, please email your request to info@omegastatistics.com and let us know the particular presentation you need the slides for. Thanks!

Hello Monalisa, you can email me at info@omegastatistics.com and request a powerpoint for any of my presentations. If I still have it I will email it to you.

Hi Zoya, It is hard for me to know what exactly you are asking and to give an answer without seeing your study/data. I do know that the minimum and maximum options for constraints are only available for when you choose "Linear Regression" as the scale variable model type in the "Method" tab. I hope you were able to figure this out.

@@omegastatistics thank you so much for your reply. I figured it out. my data is large and variables are categorical and ordinal and data is missing not at random as I was experiencing maxmodelparam , after giving the maxmodelparam = desired no an error has occured mentioning Warnings The procedure cannot access a file with the given file specification: imputeddata for keyword IMPUTATIONS of subcommand OUTFILE. The file specification is either syntactically invalid, specifies an invalid drive or directory, specifies a protected directory, specifies a protected file, or specifies a non-sharable file. Execution of this command stops.