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Calculate Effect Size Metrics

Brett's Pick this week is the Measures of Effect Size Toolbox, by Harald Hentschke.

"In statistics, an effect size is a measure of the strength of the relationship between two variables in a statistical population, or a sample-based estimate of that quantity." (Or so I gather from the Wikipedia article on the topic, anyway. I'm not a statistician. :) )

For those users who need to measure effect sizes, Harald's Toolbox provides a dazzling array of functionality that extends the capabilities of the Statistics Toolbox, including:

  • Hedges' g
  • Glass' delta
  • requivalent (point-biserial correlation)
  • common language effect size
  • Cohen's U1
  • Cohen's U3
  • receiver-operating characteristic
  • right/left tail ratio
  • rank-biserial correlation
  • standardized mean differences for contrasts
  • eta squared
  • partial eta squared
  • omega squared
  • partial omega squared
  • risk difference
  • risk ratio
  • odds ratio
  • phi
  • sensitivity
  • specificity
  • positive predictive value
  • negative predictive value
  • binomial effect size display
  • Cramer's V

User Daniel Polders summed it up in his nomination/comment for the tools: "Wow! Very impressed with the well written code and manual. Due to it’s extensive documentation its very usuable by non-statisticians. Thanks a bunch!"

Thanks, Harald. And thanks, Daniel, for the nomination. Swag is on the way to both of you! (Thanks, too, to Maik Stüttgen, who collaborated with Harald on the tools.)

As always, comments to this blog post are welcome. Or leave a comment for Harald here.

Published with MATLAB® 7.14

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