Title:      Beyond "Fixed Versus Random Effects": A Framework for
Improving Substantive and Statistical Analysis of Panel, TSCS,
and Multilevel Data

Authors:    Brandon Bartels

Entrydate:  2008-09-30 11:25:39

Keywords:   random effects, fixed effects, pooling, time-series
cross-sectional data, panel data, multilevel modeling

Abstract:   Researchers analyzing panel, time-series
cross-sectional, and multilevel data often choose between a
random effects, fixed effects, or complete pooling modeling
approach. While pros and cons exist for each approach, I contend
that some core issues concerning clustered data continue to be
ignored. I present a unified and simple modeling framework for
analyzing clustered data that solves many of the substantive and
statistical problems inherent in extant approaches. The approach:
(1) solves the substantive interpretation problems associated
with cluster confounding, which occurs when one assumes that
within- and between-cluster effects are equal; (2) accounts for
cluster-level unobserved heterogeneity via a random intercept
model; (3) satisfies the controversial statistical assumption
that level-1 variables be uncorrelated with the random effects
term; (4) allows for the inclusion of level-2 variables; and (5)
allows for statistical tests of cluster confounding. I illustrate
this approach using three substantive examples: global human
rights abuse, oil production for OPEC countries, and Senate
voting on Supreme Court nominations. Reexaminations of these
data produce refined interpretations of some of the core
substantive conclusions.

http://polmeth.wustl.edu/retrieve.php?id=838

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