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Statistical power in partially nested designs probing multilevel mediation.

Ben Kelcey1, Fangxing Bai1, Yanli Xie1

  • 1College of Education, Criminal Justice, Human Services and Information Technology, University of Cincinnati, Cincinnati, OH, USA.

Psychotherapy Research : Journal of the Society for Psychotherapy Research
|February 11, 2020
PubMed
Summary

This study provides statistical power formulas for psychotherapy research, aiding in the design of studies examining treatment mechanisms within partially nested data structures. The findings emphasize the need for adequate sample sizes or effect sizes for well-powered mediation analyses.

Keywords:
mediationpartially nestedprocess researchstatistical methodology

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Area of Science:

  • Psychotherapy research
  • Statistical methodology
  • Clinical psychology

Background:

  • Understanding intermediate mechanisms is crucial for psychotherapy development.
  • Partially nested designs are common in psychotherapy research.
  • Limited guidance exists for designing studies to detect mediation in these complex structures.

Purpose of the Study:

  • To develop statistical power formulas for mediation analysis in two- and three-level partially nested designs.
  • To guide the planning and sample size determination for psychotherapy studies investigating treatment mechanisms.

Main Methods:

  • Investigation of multilevel mediation models within partially nested structures.
  • Development of statistical power formulas tailored for two- and three-level partially nested designs.

Main Results:

  • Well-powered studies require moderate to large sample sizes or moderate to large effect sizes.
  • The developed formulas assist in determining necessary sample sizes for detecting mediated effects.

Conclusions:

  • The study provides practical tools (R package PowerUpR and Shiny web application) for researchers.
  • These tools facilitate the efficient design of studies examining mediation in partially nested psychotherapy data.