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Related Experiment Videos

A program to compute McGraw and Wong's common language effect size indicator.

W P Dunlap1

  • 1Department of Psychology, Tulane University, New Orleans, LA 70118, USA. dunlap@mailhost.tcs.tulane.edu

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|January 14, 2000
PubMed
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The common language effect size indicator (CL) offers an intuitive measure of effect size, representing the probability of one score exceeding another. This study presents a program to compute CL from various statistical inputs without needing lookup tables.

Area of Science:

  • Psychology
  • Statistics
  • Educational Research

Background:

  • Effect size measures are crucial for interpreting research findings.
  • The common language effect size indicator (CL) provides an intuitive probability-based measure.
  • Existing methods for CL calculation often rely on statistical tables.

Purpose of the Study:

  • To introduce a computational program for calculating the common language effect size indicator (CL).
  • To facilitate CL computation from various statistical parameters, including Cohen's d, t-tests, and point-biserial r.
  • To offer an alternative to traditional CL calculation methods using normal curve probability tables.

Main Methods:

  • Development of a program to compute CL directly from statistical inputs.

Related Experiment Videos

  • Integration of methods to derive CL from Cohen's d, independent samples t-tests, and point-biserial r.
  • Inclusion of equations for converting t and r values to Cohen's d for CL calculation.

    • Main Results:

    • The program computes CL without requiring statistical lookup tables.
    • CL can be accurately calculated from means and standard deviations, Cohen's d, t-tests, or point-biserial r.
    • A table of CL values for different Cohen's d is provided.

    Conclusions:

    • The developed program simplifies the computation of the common language effect size indicator.
    • This tool enhances the accessibility and understanding of effect sizes in research.
    • The program supports diverse statistical inputs, increasing its practical utility for researchers.