# Aligned rank transform on time series data

I have some time-series data that I wish to test to see if a certain factor has an effect. There are few samples per time step (10 on average), but the whole time-series has 306 samples. When divided into groups based upon a factor the result is non-normal and non homogeneous. I wish to test the effect of the factor, and if possible, determine a linear trend over the time-series accounting for this effect.

To test the effect, I performed an aligned rank transform on the data, then used an ANOVA. However this does not take into account time. To do this I tried a General Linear Model on the transformed data using the factor as a fixed effect and time as a covariate. Does this sound correct?

What is the correct way to include time information in a factorial analysis when the number of samples per unit time is quite low?

Time Series

Factor

Note: software used is SPSS, stats experience is beginner.

I'm not familiar with the aligned rank transform, but it seems utilizing multi level models (i.e. MIXED in SPSS command lingo) may be a fruitful approach to be able to incorporate any temporal trends (perhaps in the original units instead of the ranked units). –  Andy W Apr 22 '12 at 12:23