I have a randomized experimental dataset with six treatments with each approx. N=60. The outcome is a time-series, namely deforestation in a land-use simulation game over 40 rounds.
I managed to show that the impact of the treatments on the state of the land (i.e. number of cummulative cells deforested) is highly significant in a single year, but I have a hard time finding the right method to show that the impact on the ENTIRE TIME SERIES is significant. I'm afraid testing in a single year is overestimating significance, as I can choose any year, adding "researchers degrees of freedom". I could (and successfully did) test every year seperatly, but that seems to be a very unelegant solution.
In more general terms:
I have a single independent variable from one 1 to 6, and my dependent variables is time series for every observation. What I want to do is basically an ANOVA, but feeding it with a whole time series as dependent variable instead of single values for each observation.
If possible, it would be cool if the method also allows for controling for other independent fixed factors, such as player age, occupation etc., and ideally for more than one time series as dependent variable, as I also have data for intensification, savings, cows sold and some other values for every year in the game.
Any expert insights?
My data is a SQL database with a single entry for every round of the time series for every subject, linked to the subject-properties via a unique ID => I can bring it into any shape needed for the analysis. My problem is not to shape it but to find the right test. I'm using mySQL & R.