Hello everyone,
I need help interpreting the results of a Mixed Linear Model (Type III) – General Linear Model with Repeated Measures in SPSS.
I have collected monthly COVID-19 media coverage data for three media sectors over four years. Each sector consists of multiple media outlets, and I am analyzing how coverage varies over time. The graphical representation suggests differences between the three curves (mean ± S.E.M.).
Since some values are missing, I used a Mixed Effects Model (GLM with Repeated Measures) instead of a two-way repeated-measures ANOVA. My model includes:
- Fixed effects: Time, Media Sector, and the Time × Media Sector interaction.
- The results show that both main effects and the interaction are statistically significant (p < 0.05), indicating that at least one sector behaves differently over time.
- Post-hoc tests for individual months reveal multiple significant differences between sectors at specific time points.
My Question:
How can I determine which of the three media sector curves is significantly different from the others over the entire four-year period?
I tried using the "Contrast" option in SPSS, but I’m unsure if it provides the correct answer, as there are multiple contrast types.
- Should I use polynomial contrasts, repeated contrasts, or another method?
- Is there a better way to compare the overall trajectories of the three curves rather than just month-to-month differences?
Any advice, resources, or tutorials would be greatly appreciated.
Thanks in advance for your help!