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Related Concept Videos

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Related Experiment Video

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Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions
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Effect size, statistical power, and sample size for assessing interactions between categorical and continuous

Gwowen Shieh1

  • 1Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan.

The British Journal of Mathematical and Statistical Psychology
|November 24, 2018
PubMed
Summary

This study introduces effect size measures for interaction analyses, focusing on standardized slopes. It provides methods for accurate estimation and power calculations, improving statistical practices in psychology research.

Keywords:
effect sizesmoderationpower analysissample size

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Area of Science:

  • Psychology
  • Statistics
  • Quantitative Research Methods

Background:

  • Effect size reporting is crucial in psychology but underdeveloped for interaction analyses.
  • Standardized effect sizes are needed for comparing slopes in models with categorical and continuous variables.

Purpose of the Study:

  • To present the variance of standardized slopes as a key effect size measure for interaction terms.
  • To offer a robust estimator for effect size and address power/sample size calculations for slope equality tests.

Main Methods:

  • Developed theoretical foundations for standardized slope variance.
  • Proposed a consistent, nearly unbiased effect size estimator with a refinement for extreme cases.
  • Investigated power and sample size calculations using exact and approximate methods.

Main Results:

  • The variance of standardized slopes effectively quantifies disparity between slope coefficients.
  • The proposed estimator is consistent and nearly unbiased, performing well even in extreme situations.
  • Exact methods for power and sample size calculations demonstrate clear advantages over approximate approaches.

Conclusions:

  • The proposed methods enhance the reporting and interpretation of effect sizes in interaction studies.
  • Provides practical tools (SAS/R code) for researchers to implement advanced statistical analyses.
  • Recommends the exact approach for power and sample size calculations in slope equality tests.