Hello all,
I am pretty new to SEM and I am currently using AMOS to analyse longitudinal data. I have data from the same participants and the same measurement instrument (EPDS for assessing postnatal depression) for 4 measurement occasions. I do have quite a lot of missing data (60% on one occasion as an axample) Some of the participants have not completed the EPDS on any of the 4 measurement occasions, and some have only completed it only 1 out of 4 times. I have spoken to two persons, and they have given me different advise. The first person who is familiar with SEM said to include all participants who have completed the EPDS, even if it was only on one occasion, and let AMOS handle the missing data with ML. The other person (a statistician) said that I should at least exclude the participants who had missing data on three out of four occasions, even if I am using ML estimation.
I am a little confused on what advice to follow, even if I delete participants, I still have more than enough, and it makes sense to exclude participants who only have answered on one occasion as that is not enough data to see how depressive symptoms change over time for that participant, but I have not seen that it is common to use listwise deletion by removing some participants, and then using ML for the rest of the missing data. I do not wish to create a bias by deleting certain participants.
What is your view on this? Would it be very wrong to delete the participants with the most missing data or is it the best solution?
I will be grateful for any answer.
Thank you..