A psychology journal (Basic and Applied Social Psychology) has recently caused a bit of stir by banning p-values from their published articles. For what it’s worth, here’s a few views on the journal’s new policy, and on the use of p-values and confidence intervals in empirical research.
Jonathan Bartlett
Interval regression with heteroskedastic errors
Interval regression allows one to fit a linear model of an outcome on covariates when the outcome is subject to censoring. In Stata an interval regression can be fitted using the intreg command. Each outcome value is either observed exactly, is interval censored (we know it lies in a certain range), left censored (we only know the outcome is less than some value), or right censored (we only know the outcome is greater than some value). In Stata’s implementation the robust option is available, which with regular linear regression can be used when the residual variance is not constant. Using robust option doesn’t change the parameter estimates, but the standard errors (SEs) are calculated using the sandwich variance estimator. In this post I’ll briefly look at the rationale for using robust with interval regression, and highlight the fact that if the residual variances are not constant, unlike for regular linear regression, the interval regression estimates are biased.
Why I think Stata’s old xi: prefix is still useful
A few versions ago Stata introduced a new facility for handling factor variables, which in many ways is superior to the older system, which was based on prefixing regression commands with xi:. But I actually think using this older xi: syntax can be useful in some situations, one of which is when trying to understand and learn about regression model specification.