Scientific Measurement in Redistricting Research (Princeton University)
Gary King. 2021.
"Scientific Measurement in Redistricting Research (Princeton University)."
Abstract
We discuss the essential requirements for the measurement of any quantity of interest as applied to redistricting research. Most importantly, a quantity of interest must be defined separately from its measure, without which empirical estimates cannot evaluated or improved. Only with such a standard can we learn about an electoral system or understand fundamental concepts in the field such as legislative compactness, partisan bias, electoral responsiveness, among others (with or without differentially private noise applied to census data), all of which we will illustrate.
See Also
- [Presentation] Correcting Measurement Error Bias in Conjoint Survey Experiments (University of Central Florida) (2025)
- [Presentation] Correcting Measurement Error Bias in Conjoint Survey Experiments (Stanford University) (2023)
- [Presentation] Statistically Valid Inferences from Privacy Protected Data (Princeton University) (2022)
- [Book] Designing Social Inquiry: Scientific Inference in Qualitative Research, New Edition (2021)
- [Paper] How Human Subjects Research Rules Mislead You and Your University, and What to Do About It (2016)
- [Paper] Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research (2004)
- [Book] Designing Social Inquiry: Scientific Inference in Qualitative Research (1994)
- [Paper] Good Research and Bad Research: Extending Zimile's Criticism (1993)