Exploring Missingness and its Implications in Traffic Stop Data

Presented by:
Amber Lee (Pomona College)

As traffic stop data has become increasingly available, so has scholarship for analyzing the data for evidence of discriminatory policing. However, relatively few studies address missingness (NA values) in the data despite all the data being conditional on recording. This project develops a framework for studying missingness through the stop missingness rate (SMR) and presents exploratory data analysis of SMR on data from the Stanford Open Policing Project. Using the SMR, we observe trends in the SMR across variables date and day/night; such trends provide descriptive evidence of missingness as a confounding variable. We run several logistic regressions for data grouped by distinct missingness patterns and observe changes in the significance and magnitude of some race and sex variables. The possibility of missingness as a confounding variable calls for further research in its trends and impacts.