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.
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. 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?
Thanks in advance,
Jeff
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