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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A GENERAL PANEL MODEL WITH RANDOM AND FIXED EFFECTS: A STRUCTURAL EQUATIONS APPROACH.

Kenneth A Bollen1, Jennie E Brand

  • 1H.W. Odum Institute for Research in Social Science, Department of Sociology & Statistics, University of North Carolina, Chapel Hill.

Social Forces; a Scientific Medium of Social Study and Interpretation
|July 20, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible general panel model for longitudinal data, addressing limitations of standard fixed effects (FEM) and random effects (REM) models. It offers researchers an accessible way to analyze complex relationships over time using SEM software.

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Area of Science:

  • Sociology
  • Quantitative Methods

Background:

  • Longitudinal data analysis commonly uses fixed effects (FEM) and random effects (REM) models.
  • Standard FEM and REM models have limitations in flexibility, potentially misrepresenting time-varying effects and latent variable correlations.

Purpose of the Study:

  • To present a general panel model encompassing standard FEM and REM as special cases.
  • To offer a sequence of nested models for easier comparison and implementation.
  • To provide a flexible alternative for analyzing longitudinal data within structural equation modeling (SEM) software.

Main Methods:

  • Development of a general panel model framework.
  • Utilizing likelihood ratio tests and fit statistics for model comparison.
  • Implementation within widely available structural equation modeling (SEM) software.

Main Results:

  • The proposed general panel model includes standard FEM and REM as specific instances.
  • A sequence of nested models allows for richer analysis and easier comparison.
  • The model is implementable in standard SEM software, enhancing accessibility for applied researchers.

Conclusions:

  • The general panel model offers a more flexible and accessible approach to longitudinal data analysis than traditional FEM and REM.
  • Applied researchers can benefit from this unified framework for more nuanced investigations of time-invariant and time-varying effects.
  • This approach facilitates the comparison of various model specifications, improving the robustness of sociological research findings.