I have to test for significant differences between scenarios. Data consist of the length of a segment divided by the total length of the network. They are distributed between 0 (never equal to 0) and 1, with the length of the network changing. My data contains also many 1s. An example is:
|basin||scenario 1||scenario 2||scenario 3||Network length|
I tried a GLM with quasi-binomial family in R with model 'data ~ scenario' The summary is :
|Estimate Std.||Error t||value||Pr(>t)|
------- cut ----------
(Dispersion parameter for quasibinomial family taken to be 0.594791)
Null deviance: 3166.8 on 10539 degrees of freedom.
Residual deviance: 3165.0 on 10530 degrees of freedom
The test is never significant neither for scenarios nor for pairwise comparison (with means). Am I using the correct distribution for these data? Is it fully correct to use a GLM to test for significant differences among groups of data in a case like this?