Controlling for individual heterogeneity in longitudinal models , with applications to student achievement ∗

@inproceedings{Lockwood2008ControllingFI,
  title={Controlling for individual heterogeneity in longitudinal models , with applications to student achievement ∗},
  author={J. R. Lockwood and Daniel F. McCaffrey},
  year={2008}
}
Longitudinal data tracking repeated measurements on individuals are highly valued for research because they offer controls for unmeasured individual heterogeneity that might otherwise bias results. Random effects or mixed models approaches, which treat individual heterogeneity as part of the model error term and use generalized least squares to estimate model parameters, are often criticized because correlation between unobserved individual effects and other model variables can lead to biased… CONTINUE READING
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