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Sample size planning for statistical power and accuracy in parameter estimation.

Scott E Maxwell1, Ken Kelley, Joseph R Rausch

  • 1Department of Psychology, University of Notre Dame, Notre Dame, Indiana 46556, USA. smaxwell@nd.edu

Annual Review of Psychology
|October 17, 2007
PubMed
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This review explores advanced sample size planning for researchers, focusing on accurate parameter estimation (AIPE) alongside traditional power analysis to build reliable cumulative knowledge in psychology.

Area of Science:

  • Psychology
  • Research Methodology

Background:

  • Traditional sample size planning in psychology relies heavily on power analysis.
  • Recent research emphasizes effect size estimation and confidence intervals, necessitating a shift in planning approaches.

Purpose of the Study:

  • To review advances in sample size planning beyond traditional power analysis.
  • To highlight the importance of accurate parameter estimation (AIPE) for cumulative knowledge.
  • To summarize methods for implementing AIPE in common psychological research designs.

Main Methods:

  • Review of recent literature on sample size planning.
  • Focus on accuracy in parameter estimation (AIPE) principles.
  • Synthesis of AIPE implementation strategies for psychological research.

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Main Results:

  • Advances in power analysis are discussed.
  • The significance of AIPE as a complementary or alternative goal to power is established.
  • Practical approaches for sample size planning for AIPE are presented.

Conclusions:

  • Accurate parameter estimation (AIPE) is crucial for robust psychological research and cumulative science.
  • Sample size planning should increasingly incorporate AIPE alongside power analysis.
  • The review provides a foundation for researchers to adopt AIPE in their studies.