I am interested in developing a regression model. The data (about 1000 observations) that I have are not really random (i.e., spatially, the closest ones are correlated to some degree), so I drew a stratified random sample from my dataset at a 50% sampling rate and I ran a regression model using the random sample. I tried two additional sampling rates of 60% and 75% and fitted regression models with the same predictors as earlier. I see that some predictors are now significant with a larger sample. My problem is which sampling rate to select? I do not want to bias the analysis by selecting a sampling rate that gives me significance. Can members suggest any appropriate approach to my problem? Would it be better to run the analysis with the three sampling rates and then use the models developed to validate another set of independent data, and select the model that gives the best prediction.