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Dear all, I am a MSc OR student and need some advise on how to handling sampling biases. My data set, which would be used to develop the model, is not a good representation of the population. There are huge differences between the population and the sample in terms of the explanatory variables (categorical). For example, the data contains 30% of one of the levels of a variable, whereas, it is known that the true proportion is about 10%. Due to time constraint and limited resource, it is impossible to re-collect the data according to the population portion. I have to use the data to build the model. Since the sample is not very representative, My questions are: 1)Is there any method to adjust the data to make it looks similar to the population? 2) Is the adjustment necessary ? Do I have to deal with the bias ? Due to the data is highly skewed, I am going to use logistic regression to build my model. To what degree, the bias would distort my final finding ? Thanks for your help Regards, __________________________________________________ Do You Yahoo!? Make international calls for as low as $.04/minute with Yahoo! Messenger http://phonecard.yahoo.com/