Bit green when it comes to some of these methods, so please be gentle... but I'll try to provide as much info and concisely as possible. Any help sincerely appreciated.

I have a non-normal dataset with missing at random data and am trying to determine which imputation method to use. I am using SPSS, so please if possible keep this in mind when offering practical suggestions.

Two things: 1. I don't feel missing value deletion is a good option as cases with missing values are still viable and valid records with complete data in important variables. 2. I cannot use multiple imputation as I need to import the imputed dataset into another dataset of different dimensionality for final analysis.

*Questions*So I am looking at imputing using either Expectation Maximisation or Regression (as these are inbuilt in SPSS).

Does one of these approaches fit my data better than the other?

Or is there a more appropriate approach I could try?

**About my data***Missingness model*: Okay... so I am confident that my missing data is MAR. I have come up with a missingness model that I'm pretty satisfied with – showing a very strong relationship between the variable in which missing data occurs and a specific auxiliary variable I brought in, both in statistical and semantic terms. I won't say anything more than that as I don't think it necessary here.

*Some stats for the variable with the missing data*: 14% missing values (139 cases out of 987) Skewness 2.984 (SE .084) Kurtosis 7.427 (SE .168) Using Q-Q plots, the distribution of the variable with missing data seems to be closest to a Gamma distribution. Gamma dist. Shape: 0.2 Gamma dist. Scale: 0.000003

...

Thanks in advance for answers/suggestions.