The other day in class, while talking about instances (e.g., analyzing clustered data or heteroskedastic residuals) where adjustments are required to the standard errors of a regression model, a student asked: how do we know what the ‘true’ standard error should be in the first place– which is necessary to know if it is too high or too low.
Apr 14, 2019
Illustration showing different flavors of robust standard errors. Load in library, dataset, and recode. Do not really need to dummy code but may make making the X matrix easier. Using the High School & Beyond (hsb) dataset.
Nov 4, 2018
In our module on regression diagnostics, I mentioned 1) that at times (with clustered data) standard errors may be misestimated and may be too low, resulting in a greater chance of making a Type I error (i.e., claiming statistically significant results when they should not be). In our ANCOVA session, I also indicated that 2) covariates are helpful because they help to lower the (standard) error in the model and increase power. So, it sounds like we would like to have models with lower standard errors. However, there are cases when the standard error is estimated lower than it should be (i.e., the standard error is biased).
Jun 28, 2018