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In the next screen, click “match cases on key variables in sorted files,” and “Both files provide cases.” Place “ID” (or whatever your participant ID number variable is) in the box “key variables.” Then click okay. Your new data set should be listed under “open datasets.” Click on it and press “continue”ġ6. In the master file (not the smaller, newly-created file), Click on Data –> Merge Files –> Add Variablesġ5. Double check to make sure you have done this.ġ4. Make sure both files are sorted by ascending ID number, as described in step 2. Make sure both the master data file and the new data file created with the above syntax are open at the same time. If you sorted correctly, you should be able to copy and paste it from the master file.ġ3. You’ll need this later to merge the files. Next, add an ID number variable (representing the participant ID number) that will be identical to whatever is in your master file (including variable name!). You are doing this because you do not want to overwrite the raw data with missing values included in the master data file.ġ2. Make it simple, something like the following syntax: In the data file created with the above syntax, rename every variable. Merging the master file and the file created with EM aboveġ1. If you want to have these variables in your master data file, you will have to merge the files together. However, the new datafile will ONLY contain the variables listed in the syntax above. If everything went well, this new data file will have no missing data! (You can verify this for yourself by running analyzeàFrequencies on all your variables). The syntax you ran also saved a brand new datafile in a location you specified above. If that doesn’t work, try reducing the number of variables in your analysis.ĩ. To fix it, increase the number of iterations specified in the syntax (e.g. If this message DOES pop up, it means that the data imputation will be inaccurate. If you don’t find it at all in the output, it’s because everything is working properly. It will only pop up if there is a problem. (a) If Little’s MCAR test is nonsignificant, this is a good thing! It means that your variables are missing Completely at Random (see #4 in FAQ).
![spss modeler 18 imputation spss modeler 18 imputation](https://bookdown.org/mwheymans/bookmi/images/fig4.13.png)
Both can be found in the spot indicated in the picture below: This will produce a rather large output file, but only a few things within are necessary for our purposes: (a) Little’s MCAR Test and (b) whether or not the analysis converged. Highlight all the text in the syntax file, and click on the “run” button on the toolbar:Ħ. sav is the file extension for an SPSS file, so make sure it ends in that. Copy and paste the following syntax into the Syntax Editor, adding in your own variables after MVA VARIABLES, and specifying a location on your computer after OUTFILE.Also, note that. To do this, right click on the ID column, and click “sort ascending”Ĥ. This is critical if you do not do this, everything you do subsequently could be inaccurate. Sort the data file by ascending ID or Participant number. Open the data-file you want to work with.Ģ.
#Spss modeler 18 imputation how to#
I start with a step-by-step tutorial on how to do this in SPSS, and finish with a discussion of some of the finer points of doing this analysis.ġ. In this post, I outline when and how to use single imputation using an expectation-maximization algorithm in SPSS to deal with missing data.