Comparative research often uses multilevel models to study how country-level factors affect individual behaviors. However, these traditional estimation methods face challenges when data includes only a few countries.
➡️ How Few Countries Work:
A large-scale Monte Carlo experiment reveals that maximum likelihood estimates can be severely biased with limited country samples, particularly in models featuring cross-level interactions.
➡️ Bayesian Solution:
Bayesian approaches demonstrate much greater robustness and provide more conservative test results even when the number of countries is small.
➡️ The Takeaway:
Researchers should consider using Bayesian methods for multilevel analyses involving few countries to improve reliability.
| How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches. was authored by Daniel Stegmueller. It was published by Wiley in AJPS in 2013. |
