TN - Sampling Issues - Sampling Issues In this module we discuss how to fix sampling issues The key methods you will learn are 1 Weighted least squares

TN - Sampling Issues - Sampling Issues In this module we...

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Sampling Issues In this module, we discuss how to fix sampling issues. The key methods you will learn are: 1) Weighted least squares 2) Discrete Dependent variables (logit/probit models) 3) Selection models (Tobit models) Weighted Least Squares Suppose you were doing political polling where you knew that the true population split between the Democratic Party and Republican Party was 40% to 60%. And further suppose that when you did your polling, your sample was reversed, i.e., 60% democratic and 40% republican; and that each party received 70% of the vote for their own candidate. If you just report the sample mean (60%*70% + 40%*30%=54% for Democrat, 46% for Republican), you know that your estimate is biased. That is, if the true population weights were used, then the Republican would receive 54% and the Democrat would receive 46%.
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