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Related Experiment Videos

Comparing personal trajectories and drawing causal inferences from longitudinal data.

S W Raudenbush1

  • 1School of Education and Institute for Social Research, University of Michigan, 610 East University Avenue, Ann Arbor, Michigan 48109, USA. rauden@umich.edu

Annual Review of Psychology
|January 10, 2001
PubMed
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This review covers statistical analysis for repeated measures data, focusing on modeling individual change trajectories and population averages. It explores hierarchical, multilevel, and latent growth models for diverse developmental studies.

Area of Science:

  • Statistics
  • Developmental Psychology
  • Biostatistics

Background:

  • Studies with repeated measures are crucial for understanding population changes and individual differences.
  • Analyzing longitudinal data requires robust statistical frameworks to capture complex developmental patterns.

Purpose of the Study:

  • To review statistical analysis methods for repeated measures data.
  • To highlight the utility of person-specific and between-person models in longitudinal research.
  • To discuss causal inference from repeated measures data.

Main Methods:

  • Focuses on a two-stage modeling framework: person-specific trajectory models and between-person models.
  • Discusses various analytic approaches including hierarchical, multilevel, latent growth, and random coefficient models.

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  • Reviews published examples across diverse developmental domains.

    • Main Results:

    • The two-stage modeling framework provides a flexible approach to analyzing repeated measures data.
    • This framework is applicable to diverse developmental trajectories, such as vocabulary growth, academic learning, and antisocial propensity.
    • The review addresses the challenges and methods for drawing causal inferences.

    Conclusions:

    • A unified modeling framework effectively analyzes individual and population-level changes from repeated measures.
    • The methods discussed are adaptable to various fields of developmental research.
    • Further consideration is given to establishing causality in longitudinal studies.