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Related Experiment Videos

Strategies for analyzing ecological momentary assessment data

J E Schwartz1, A A Stone

  • 1Department of Psychiatry and Behavioral Science, State University of New York at Stony Brook 11794-8790, USA.

Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association
|February 12, 1998
PubMed
Summary

Analyzing momentary data in behavioral and medical science requires advanced statistical methods. Common approaches often lead to suboptimal results and incorrect inferences, necessitating the use of multilevel models for accurate analysis.

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Area of Science:

  • Behavioral Science
  • Medical Science
  • Psychology
  • Psychiatry
  • Health Sciences

Background:

  • Repeated observations of momentary phenomena are increasingly used in behavioral and medical research.
  • Many researchers lack familiarity with appropriate statistical techniques for analyzing such data.

Purpose of the Study:

  • To review common suboptimal methods for analyzing momentary data.
  • To describe advanced multilevel statistical models suitable for this data type.
  • To highlight key analytic considerations for accurate inference.

Main Methods:

  • Review of aggregation strategies, repeated measures ANOVA, pooled within-person regression, and two-stage multilevel models.
  • Description of multilevel statistical models for momentary data.

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  • Discussion of analytic issues: variable scaling, serial autocorrelation, and random vs. fixed effects.

    • Main Results:

    • Common methods like aggregation and repeated measures ANOVA are often suboptimal for momentary data.
    • These suboptimal methods can lead to incorrect scientific inferences.
    • Multilevel models offer a more appropriate framework for analyzing momentary data.

    Conclusions:

    • Standard statistical approaches are frequently inadequate for analyzing repeated momentary observations.
    • Multilevel models provide a robust framework for addressing complex research questions with momentary data.
    • Careful consideration of scaling, autocorrelation, and effect variability is crucial for valid analysis.