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I plan to use SPSS for an actor-partner interdependence model (APIM). My sample will have a significant amount (possibly >20%) missing data on one of the predictor variables (edit: I'm emphasizing predictor because I just saw something about ML only being useful when missing data are on the outcome variable; not sure if this is true). I'm somewhat familiar with issues related to missing data mechanisms (I believe my data are MAR, though I haven't systematically examined it yet) and the choice of different strategies, e.g., it seems there is a consensus that both multiple imputation (MI) and maximum likelihood (ML) are best practices. My questions are about implementation.

  1. I've been told that SPSS does ML, but can anyone confirm this is possible when using APIM, and if so, is it the default?

  2. I've also been told that SPSS can do MI with a particular add-on. But how does pooling work? Can an APIM be estimated following MI? It seems that ML is the more convenient option when using SPSS, but I'm curious if there is an automated process for MI like there is in the R mice package (though I'm also not sure with mice whether it's possible to do multilevel models, etc.)

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SPSS does not have a procedure specifically designed for an APIM model, but if you're intending to use a linear mixed models approach, then ML estimation using the MIXED procedure is available (the default is restricted maximum likelihood or REML).

There is a multiple imputation procedure to create imputed data, and MIXED does produce pooled estimates for model parameters. Pooling is done based on Rubin's rules, as is standard in analyses of multiply-imputed data.

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