Dara,

Really interesting questions. In my experience SPSS's imputation function is easy to use, both in creating datasets and in analyzing and pooling the resulting imputation datasets. However, its ease of use is its downfall as well. If you look at a similar imputation function in the R statistical software (see for example the 'mice' package), you will see far more options. See Stef van Buurens website for an excellent explanation of multiple imputation in general (with or without using the mice package).

It is very important to note that these additional options are not 'luxury' choices for advanced users only. Some are essential in order to attain proper congeniality, specific models for specific missing variables, specific predictors for specific missing variables,imputation diagnostics, and more, which are not available in the SPSS imputation function. As to your questions:

1. imputation of pre- and post scores and passively replacing the missing differences is appropriate when you want to conserve the relation between the pre- and post scores, and the difference (as answered by jsakaluk). In your case this might be so when you want to build a model with the difference in pre and post score as outcome/dependent variable and the baseline (pre-score) as (one of the) predictors/indepenent variables.
2. Any model used to replace missing values should abide by its assumptions. Meaning that to replace a continuous variable you need to adhere to the assumptions of a linear regression model (in the simplest case). for linear regression, and most other regression model, the predictor variables need not be normally distributed, the model's residuals however, do have to be! Some transformation might therefore be necessary if the latter is the case.
3. See jsakaluk's answer. Do note however that SPSS uses massive imputation, which basically means all entered variables are used to replace variables with missing cases. If you only have one variable with missing this is no problem. If you have multiple however, this means the variables with missingness are also used to complete the other variables with missingness. This might not be a problem, but in some cases this creates feedback loops which bias your final imputation values. It is imperative to check this by looking for trends throughout the iterations of your imputation instead of 'stabilizing' replaced values.
4. I agree with jsakaluk's answer on this one. If you decide to 'distrust' your complete data because you suspect selective missings, and solve or partly remedy this by using multiple imputation techniques (which I think would indeed be the least biased), then your multiple imputation results should be the main results you show. Regrettably, experience has shown reviewers or other interested people sometimes do want to see complete case analyses as well (so keep them at hand).

Kind regards, IWS. PS. @jsakaluk: I have upvoted your answer as well, however I am new to cross-validated and do not have enough reputation to be shown. Props for your answer anyway.

In my experience SPSS's imputation function is easy to use, both in creating datasets and in analyzing and pooling the resulting imputation datasets. However, its ease of use is its downfall as well. If you look at a similar imputation function in the R statistical software (see for example the mice package), you will see far more options. See Stef van Buurens website for an excellent explanation of multiple imputation in general (with or without using the mice package).

It is very important to note that these additional options are not 'luxury' choices for advanced users only. Some are essential in order to attain proper congeniality, specific models for specific missing variables, specific predictors for specific missing variables,imputation diagnostics, and more, which are not available in the SPSS imputation function.

As to your questions:

1. imputation of pre- and post scores and passively replacing the missing differences is appropriate when you want to conserve the relation between the pre- and post scores, and the difference (as answered by jsakaluk). In your case this might be so when you want to build a model with the difference in pre and post score as outcome/dependent variable and the baseline (pre-score) as (one of the) predictors/indepenent variables.
2. Any model used to replace missing values should abide by its assumptions. Meaning that to replace a continuous variable you need to adhere to the assumptions of a linear regression model (in the simplest case). for linear regression, and most other regression model, the predictor variables need not be normally distributed, the model's residuals however, do have to be! Some transformation might therefore be necessary if the latter is the case.
3. See jsakaluk's answer. Do note however that SPSS uses massive imputation, which basically means all entered variables are used to replace variables with missing cases. If you only have one variable with missing this is no problem. If you have multiple however, this means the variables with missingness are also used to complete the other variables with missingness. This might not be a problem, but in some cases this creates feedback loops which bias your final imputation values. It is imperative to check this by looking for trends throughout the iterations of your imputation instead of 'stabilizing' replaced values.
4. I agree with jsakaluk's answer on this one. If you decide to 'distrust' your complete data because you suspect selective missings, and solve or partly remedy this by using multiple imputation techniques (which I think would indeed be the least biased), then your multiple imputation results should be the main results you show. Regrettably, experience has shown reviewers or other interested people sometimes do want to see complete case analyses as well (so keep them at hand).