Calculate Effect Size Metrics

저자 Brett Shoelson, August 31, 2012

7 회 조회 (최근 30일) | 0 좋아요 | 0 댓글

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