I have a good knowledge on imputation. If you want I can guide toward the best methods. Not all imputations are equivalent. Some are adequate only in some type of data. What's the data you use ? Is it the file named "dataset.csv" that you uploaded here ? Because I'm not sure i can recommend the use of imputation. I have explained that here. Your variables must be highly correlated (approaching at least 0.40 or so). The ratio of subjects/variables (put in the imputation procedure) should not fall below 10/1. Alternatively, Hardt et al. (2012) recommend a maximum ratio of 1:3 for variables (with or without auxiliaries) against complete cases, that is, for 60 people having complete data, up to 20 variables could be used. The auxiliary variables are those that can be substitute because of their high correlation, such as, identical variables measured at different points in time (i.e., repeated measures). If the % of missing cases is too high, such as in your variables ViolentCrimeNorway, LarcenyNorway, ViolentCrimeFinland, and LarcenyFinland, I can tell you'll have big troubles.

Besides, the superiority of imputation over complete case is not restricted to FA, but extend to all kind of analyses. For example, in that case, multiple regression is inefficient, and then nearly 100% of this kind of analyses published in various other journals should be wrong, because they almost never apply imputation. Either because they don't know a thing about it or because it's time consuming. For example, 5 imputation minimum is recommended, but can be higher given the features of your data. But with 5 imputation, you will need to run the analysis five times with each of the imputed data set, and then, average the results, and as is recommended, you should also provide the standard error or CI, or standard deviation, to let the readers know about how much the estimates vary over the imputation. If your estimates vary too much, that may be a problem, a signals that your estimate is not stable, and that maybe, you'll need more imputation, 10, 20, 30, etc. But repeating the analysis 30 times with your 30 dataset is something researchers don't want to do. And I understand that...

Personally, I prefer maximum likelihood (ML) estimation, because multiple imputation (MI) gives me some headache about choosing which kind of MI is good depending on the data you have. If you have AMOS, or R, you can easily use ML.

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In your last sentence of your paper, "All datasets and source code is available in the supplementary materials.", it's optional but I recommend you to add "on OpenPsych Forum".

Besides, the superiority of imputation over complete case is not restricted to FA, but extend to all kind of analyses. For example, in that case, multiple regression is inefficient, and then nearly 100% of this kind of analyses published in various other journals should be wrong, because they almost never apply imputation. Either because they don't know a thing about it or because it's time consuming. For example, 5 imputation minimum is recommended, but can be higher given the features of your data. But with 5 imputation, you will need to run the analysis five times with each of the imputed data set, and then, average the results, and as is recommended, you should also provide the standard error or CI, or standard deviation, to let the readers know about how much the estimates vary over the imputation. If your estimates vary too much, that may be a problem, a signals that your estimate is not stable, and that maybe, you'll need more imputation, 10, 20, 30, etc. But repeating the analysis 30 times with your 30 dataset is something researchers don't want to do. And I understand that...

Personally, I prefer maximum likelihood (ML) estimation, because multiple imputation (MI) gives me some headache about choosing which kind of MI is good depending on the data you have. If you have AMOS, or R, you can easily use ML.

*****

In your last sentence of your paper, "All datasets and source code is available in the supplementary materials.", it's optional but I recommend you to add "on OpenPsych Forum".