I have a dataset in psychology with 3 measurement points (longitudinal data), 4 questionnaires, and about 120 participants.
There is a lot of missing data in this dataset. If I delete all the participants, less than half of the sample is left.
Furthermore, I need to work with the same sample for all questionnaires (I think), but the data is diffuse: For example: a participant has completed all questionnaires (A, B, C), except for questionnaire D, where he skipped observations 2 and 3. In this case, they can be included in the sample for questionnaires A, B, C, but what to do with the fact that he didn’t participate in observations 2 and 3 for questionnaire D? Should the person be deleted completely or only for the analysis of questionnaire D, won’t this ruin the sample?