Luke Keele
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Luke Keele has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Boris Shor
University of Chicago
1 shared publication - David Park1 shared publication
- Dustin H. Tingley
Princeton University
1 shared publication - Joseph Bafumi
Dartmouth College
1 shared publication - Kosuke Imai
Harvard University
1 shared publication - Teppei Yamamoto
Princeton University
1 shared publication
Latest Publications
- 1 January 2007
- Published by Cambridge University Press (CUP) in Political Analysis
- Vol. 15 (2) , 165-181
A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data
The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.
- 1 January 2007
- Published by Cambridge University Press (CUP) in Political Analysis
- Vol. 15 (2) , 165-181
A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data
The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.