Hi everyone, I need help for what analysis is better for this longitudinal study. In my study, I measured the attachment security of children at 5 time points. My dubts are that: 1) spacing between time points is not equal (T1 = beginning; T2 = after 1 month; T3 = after 2 months; T4 = after 6 months; T5 = after 15 months); 2) the total sample is 148 children but not all of them have the 5 observations/scores (T1 = 148; T2 = 140; T3 = 112; T4 = 20; T5 = 50) so there are many missing, especially at T4.
Aim: I would like to examine if attachment scores change significantly over time and if these are affected by other variables such as gender, age, etc.
My questions are:
- focusing on the first period of time, as preliminary analysis for T1-2-3 I used the Repeated Measures ANOVA, because the spacing between time points are equal (however, there are some missing and I lose some information). Then, I analyzed means with the Repeated Measures ANOVA analysis and Post-hoc tests (Bonferroni) with e.g. "gender" as between-subjects factor. Does that work?
- then the study has continued at T4 and T5. Which analysis can I use now? Does it make sense to quit T4 with so few subjects?
- what analysis considering the role played by other variables? Growth Curve Model?
Thanks so much
Hello Alessia, I would use mixed models with "subjects" as random intercept and "gender" as a fixed effect: mixed models allow you to use eventual alternative distributions in case of your dependent variable is not normally distributed and they are less sensitive to missing data.
Anyway, the different time points are so unbalanced, in my opinion, and I would suggest considering just T1-2-3.
You can repeat the same analysis including T4 and T5 only on participants included at T4, so with a reduced sample.
This seems to be the only reliable solution, in my opinion, but I'm curious to know whether someone else has some best suggestions.
Good work and good analyses!
You can use either the mixed-effect model or generalized estimating equation (GEE). Your sample size is small. So try the GEE with different correlation structures.
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