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Should providers of treatment be regarded as a random factor? If it ain't broke, don't "fix" it: a comment on Siemer

Ronald C Serlin1, Bruce E Wampold, Joel R Levin

  • 1Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI 53706-1796, USA. rcserlin@wisc.edu

Psychological Methods
|December 11, 2003
PubMed

Insights

Treating service providers as a fixed factor in nested designs can inflate Type I error rates, potentially leading to incorrect conclusions about treatment effectiveness. Researchers must carefully consider model choices for accurate analysis.

Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Siemer and Joormann criticized Wampold and Serlin's analysis of treatment effects in nested designs.
  • They argued for treating service providers as a fixed factor due to non-random selection and assignment.
  • They also noted greater statistical power for detecting treatment effects in fixed versus mixed models.

Purpose of the Study:

  • To address the implications of treating service providers as a fixed factor in nested designs.
  • To demonstrate the consequences of this approach on the generalizability of treatment effect conclusions.

Main Methods:

  • Theoretical analysis of statistical models for nested designs.
  • Examination of the impact of fixed versus mixed effects for providers on error rates.
  • Demonstration of inflated Type I error rates when generalizing beyond study-specific providers.

Main Results:

  • Considering providers as a fixed factor necessitates conditioning conclusions on the specific providers included in the study.
  • Generalizing findings beyond the studied providers under a fixed-effects model leads to inflated Type I error rates.
  • This challenges the approach advocated by Siemer and Joormann regarding provider fixed effects.

Conclusions:

  • The fixed-effects approach for providers in nested designs limits generalizability and increases the risk of false positives.
  • Careful consideration of the statistical model is crucial for accurate interpretation of treatment effects in nested data.
  • Alternative modeling strategies may be necessary to avoid inflated Type I errors when generalizing findings.

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