Dear Jeff,
See below.
From: [hidden email] <[hidden email]>
Sent: Wednesday, April 7, 2021 10:20 AM
To: Ginkel, J.R. van <[hidden email]>; [hidden email]
Subject: RE: Multiple imputation question
Ironically,
It was one of your papers from which I got the term, “stochastic regression imputation,” (van Ginkel et al, 2020 in J. Personality Assessment). I hadn’t heard of that term before I just read that paper.
I think you’re mixing up two things: the stochastic regression imputation I talked about in my 2020 paper was a method for single imputation, which you could say was the predecessor of fully conditional
specification using regression. The regression method that I’m talking about is the one described on p. 4 of that paper in the Multiple Imputation Explained section.
I caught most of what you said and understand that R is much more sophisticated than most other statistical packages (I think it would take me a bit of time to fully digest your response), but am still curious if SPSS
can be set to produce the type of singly-imputed dataset as you described above in that 2020 paper via its MI procedure even if this is not ideal in practice. I can imagine that it wouldn’t be too difficult to create a macro to so such a thing, but I’m wondering
whether it’s built-in?
It is possible to produce a single imputed dataset with the MI procedure in SPSS by setting the number of imputations to 1, but that is not equivalent to stochastic regression imputation (the latter
doesn’t use fully conditional specification as an estimation method).
Regardless of the paper, I can easily see that as being helpful in certain situations where you want to explore a number of different model specifications before settling on one that you’ll use in a final model.
Thanks in advance and for your former response.
Jeff
From: Ginkel, J.R. van <[hidden email]>
Sent: Wednesday, April 7, 2021 5:05 PM
To: '[hidden email]' <[hidden email]>;
[hidden email]
Subject: RE: Multiple imputation question
Dear Jeff,
See my answers below.
From: SPSSX(r) Discussion <[hidden email]>
On Behalf Of Jeff A
Sent: Wednesday, April 7, 2021 5:38 AM
To: [hidden email]
Subject: Multiple imputation question
I’m currently reviewing an article for a pretty decent journal in the psychological literature where the authors have said that they’ve used spss and also have used multiple imputation.
They are clearly either making a mistake (at worst) or just not describing things well (at best).
They say, “In order to analyze a complete data set multiple imputation (MI) was used to input the missing values.”
They state no other real details of the purported MI procedure they went through and are mixing up the definitions of MCAR, MAR, and NMAR (but only slightly – I’ve seen much worse).
I’m trying to understand what they may have done since otherwise, this is a very good paper, but I haven’t used spss’s implementation of multiple imputation (but have seen and assisted a colleague who was confused) so
it’s difficult for me to figure out where they may have made an error.
SPSS performs multiple imputation, creates a new data file in which all imputed versions of the incomplete dataset are appended after another with the original dataset on top, all indicated by an
indicator variable “Imputation_”.
I’ve only used older and less-user-friendly MI software (e.g., Norm) in the past (before MI became available in spss) and not seen in detail what spss can currently do with MI.
This may sound a bit offending to the makers of SPSS, but after doing multiple imputation in SPSS a few times I stopped using it and switched to the mice procedure in R. The basic procedure in SPSS
is fully conditional specification, just like in R, but it lacks all flexibility that R has. In R you can specify a separate imputation model for each variable, which doesn’t necessarily need to include all variables entered, whereas in SPSS each variable
is predicted by all other variables entered in the MI procedure. When you have entered many variables this will inevitably lead to overfitted imputation models, causing the imputed values to become near random (I have seen it happen in scatter plots). Additionally,
R can use predictive mean matching (PMM) for some numerical variables and linear regression imputation for other numerical variables. In SPSS you can either use PMM for all numerical variables or regression for al numerical variables, but not one method for
one set of variables and the other method for the other set of variables. These are just a few examples, but SPSS lacks many more (to my opinion, essential) features that mice has. To make matters worse, SPSS hardly has any diagnostic tools to determine whether
the imputation process went right while mice has several features for that. In short, as an expert on multiple imputation I wouldn’t recommend the MI procedure in SPSS, unless the dataset has relatively few variables and the missing-data problem is relatively
simple. Since you cannot tell this from the information that the authors gave in their paper, my comment as a reviewer would be that the authors should switch to mice in R.
Is there someway that SPSS will kick out a single set of regression coefficients that a user might interpret in the wrong way as coming from a single dataset? Can spss produce a single data set that might be described
as what some call, “stochastic regression imputation,” which is where missing values are predicted by non-missing values within the data, but an random error term is then subsequently applied? In other words, can the MI procedure in SPSS be set to produce
only a single dataset?
SPSS does have a possibility of combining the results of several imputed datasets into one result using Rubin’s combination rules. When SPSS recognizes a dataset as a multiple-imputation dataset,
it gives the user a warning that the split file option must be switched on first, before carrying out any analysis (with Imputation_ as a split variable). Next, SPSS automatically does the combining, which is a really nice feature because you don’t need to
do the combining yourself. What I usually do is impute the data in R first, save the result to a dataset in SPSS format, and next do the analyses in SPSS. However, SPSS does not pool the results of all statistical analyses. For example, it doesn’t pool the
F-tests of ANOVA, R^2 in regression, or the results of PCA. Usually I use my own SPSS macros for that, which are freely available on my personal page.
In some of my papers (Van Ginkel & Kroonenberg, 2014; Van Ginkel, 2019; Van Wingerde & Van Ginkel, 2021) I also refer to these macros.
Most of these pooling procedures can also be done in R with the relevant packages by the way.
Long story short: I wouldn’t worry about the authors reporting a set of regression coefficients as coming from a single dataset. What I would worry about more, is the whole imputation process that
preceded the analysis.
Best regards,
Joost van Ginkel
Thanks in advance,
Jeff
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