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Measuring effect sizes using manifest versus latent variables: consequences and implications for research.

Rainer Leonhart1, Markus Wirtz, Jürgen Bengel

  • 1Department of Social Psychology and Methodology, Institute of Psychology, Albert-Ludwigs-University of Freiburg, Freiburg, Germany. leonhart@psychologie.uni-freiburg.de

International Journal of Rehabilitation Research. Internationale Zeitschrift Fur Rehabilitationsforschung. Revue Internationale De Recherches De Readaptation
|August 19, 2008
PubMed
Summary

Calculating effect sizes using latent variables in structural equation models can yield higher estimates than manifest variables. This method, particularly valuable in rehabilitation research, offers more robust effect size reporting.

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Area of Science:

  • Psychology
  • Rehabilitation Medicine
  • Statistical Modeling

Background:

  • Effect sizes quantify mean differences independent of sample size, crucial for research interpretation.
  • Current research typically calculates effect sizes at the manifest level.
  • Latent variable effect size estimation within structural equation models offers potential for more valid and distinct results.

Purpose of the Study:

  • To compare manifest and latent estimation of effect sizes.
  • To investigate the impact of different estimation methods on subgroups (indication, sex, age).
  • To assess the generalizability of latent effect size calculations in rehabilitation research.

Main Methods:

  • Utilized data from a large meta-analysis (N=5809) of subjective health status in orthopedic, cardiological, and psychosomatic rehabilitation.
  • Calculated standardized effect sizes and standardized response means for both manifest and latent variables.
  • Analyzed subgroup differences based on indication, sex, and age.

Main Results:

  • Manifest effect sizes ranged from 0.03 to 1.44 (standardized effect sizes) and 0.05 to 1.31 (standardized response means).
  • Latent effect sizes ranged from 0.04 to 1.58 (standardized effect sizes) and 0.07 to 1.45 (standardized response means).
  • Latent effect sizes were generally higher than manifest effect sizes, though no universal conversion factor exists.

Conclusions:

  • Latent effect size estimation in structural equation models provides valuable, potentially higher, estimates compared to manifest calculations.
  • While not always yielding higher values, latent effect size computation should be standard practice.
  • This approach enhances the validity and precision of effect size reporting in rehabilitation research.