Analytic and Bootstrap Confidence Intervals for the Common-Language Effect Size Estimate
Evaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
PsychOpen
2021-03-01
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Series: | Methodology |
Subjects: | |
Online Access: | https://meth.psychopen.eu/index.php/meth/article/view/4495 |
Summary: | Evaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been limited progress in further refining CLES. This study: a) extends Blomqvist’s work by providing a mathematical foundation for Bp (a non-parametric version of CLES) and an analytic approach for estimating its standard error; and b) evaluates the performance of the analytic and bootstrap confidence intervals (CIs) for Bp. The simulation shows that the bootstrap bias-corrected-and-accelerated interval (BCaI) has the best protected Type 1 error rate with a slight compromise in Power, whereas the analytic-t CI has the highest overall Power but with a Type 1 error slightly larger than the nominal value. This study also uses a real-world data-set to demonstrate the applicability of the CLES in measuring the relationship between age and sexual compulsivity. |
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ISSN: | 1614-2241 |