Disclaimer: I'm no expert is the mixed procedure and I simply happened to have similar questions analysing my own data.
According to SPSS 25 manual p. 22 the repeated covariance type is the covariance structure for the residuals. Among others, SPSS provides following structures:
- Compound Symmetry
The only reason I picked those 4 (out of 22) is that I found those more relevant to my own research
On p.80 of the same manual we can find a brief explanations:
- AR(1). This is a first-order autoregressive structure with homogenous
variances. The correlation between any two elements is equal to rho
for adjacent elements, rho2 for elements that are separated by a
third, and so on. is constrained so that –1<<1.
- Compound Symmetry. This structure has constant variance and constant
- Diagonal. This covariance structure has heterogeneous variances and
zero correlation between elements.
- Unstructured. This is a completely general covariance matrix.
I would not go as far as recommending any structure for your data (this question is 6 years old, so you probably sorted it out) but:
for a repeated measure design the default in SPSS is Diagonal.
A. Field (in Discovering Statistics Using SPSS) p. 738 suggests testing different structures on a final model (estimated with ML, not the default REML) and comparing their goodness-of-fit indices (AIC, AICC)
Also this answer might be helpful as well: https://stats.stackexchange.com/a/49786/133561
If anyone can provide a plain English explanation I'd love to hear it and understand some more.