I am struggling at the moment with how to determine links between different sets of variables. I have data on land use/cover changes in a number of regions in a period of 20 years. They are all expressed in percentages of area surface changed. I have data on so called drivers of change (population change, employment levels, education structure changes, slope, altitude etc) also expressed in percentages or indices. I have a third type of variables - a questionnaire survey.
Basically what I did was multiple regression analysis on each independent variable (land cover change - forest change, grassland expansion or reduction, arable land expansion or reduction etc.) with all of the drivers of change. So I would get R for the connection between e.g. deforestation on one side and population change and altitude on the other (other drivers were eliminated through backward stepwise regression).
So after I would determine that forest cover change is affected by changes in population number or altitude, I would use data from the questionnaire survey to interpret the reasons for such connection.
Is there any better way to model these variables? I am dabbling into canonical correlation - could it be useful? Land cover variables on one side, and the drivers on the other? And could I include the data from the questionnaire survey in the canonical correlation in any way? Thank you very much for your help.