SPSS Linear Mixed Model repeated covariance type

Hi everyone,

I need your help with regards to specifying repeated covariance type in SPSS.

I am trying to build a Linear Mixed Model in SPSS with the subject being 'borough' and repeated variable being YEAR . My dependent variable is the number of people claiming housing benefit, per year (for 7 years overall), for each borough. All my covariates (fixed effects) represent a value for a tax year. Examples of my covariates are: population, unemployment rate, mean rent, median house price to median earnings ratio. I've tried setting different covariance types and the results do differ significantly, so I guess it is important to set the correct one. I've tried researching, how to determine the repeated covariance type but I've been unsuccessful. This is the only guidance I've found https://www.ibm.com/support/knowled...ariance_structures.html#covariance_structures however, as I am not a statistician (but a sociologist) these descriptions mean nothing to me and sound like a foreign language.

The covariance types options are:
  • Ante-Dependence:First Order
  • AR1
  • AR1 Heterogenous
  • ARMA
  • Compound Symmetry
  • Compound Symmetry: Correlation Metric
  • Compound Symmetry: Heterogenous
  • Diagonal
  • Factor Analytic: First Order
  • Factor Analytic: First Order, Heterogeneous
  • Huynh-Feldt
  • Scaled Identity
  • Toeplitz
  • Toeplitz: Heterogenous
  • Unstructured
  • Unstructured:Correlations

I would be grateful for your help!


Less is more. Stay pure. Stay poor.
The citations above seem reasonable enough. I would base a decision on the following:

-Primarily your familiarity with the study context. What type of structure would existing knowledge support.

-Secondarily, if you are contemplating a couple of structures, you can default to unstructured or see if the information criteria may better support a particular structure. However, I see that one of the above citations mentions IC may not always be right, and I wouldn't refute that claim.