Correlational Effect Size Benchmarks

• 成果类型: Article
• 署名作者: Bosco, Frank A.; Aguinis, Herman; Singh, Kulraj; Field, James G.; Pierce, Charles A.
• 署名单位: Virginia Commonwealth University; Indiana University System; Indiana University Bloomington; IU Kelley School of Business; South Dakota State University; University of Memphis
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/a0038047
• 发表日期: 2015
• 页码: 431-449
• 关键词: effect size; statistical analysis; null hypothesis testing; big data

摘要

Effect size information is essential for the scientific enterprise and plays an increasingly central role in the scientific process. We extracted 147,328 correlations and developed a hierarchical taxonomy of variables reported in Journal of Applied Psychology and Personnel Psychology from 1980 to 2010 to produce empirical effect size benchmarks at the omnibus level, for 20 common research domains, and for an even finer grained level of generality. Results indicate that the usual interpretation and classification of effect sizes as small, medium, and large bear almost no resemblance to findings in the field, because distributions of effect sizes exhibit tertile partitions at values approximately one-half to one-third those intuited by Cohen (1988). Our results offer information that can be used for research planning and design purposes, such as producing better informed non-nil hypotheses and estimating statistical power and planning sample size accordingly. We also offer information useful for understanding the relative importance of the effect sizes found in a particular study in relationship to others and which research domains have advanced more or less, given that larger effect sizes indicate a better understanding of a phenomenon. Also, our study offers information about research domains for which the investigation of moderating effects may be more fruitful and provide information that is likely to facilitate the implementation of Bayesian analysis. Finally, our study offers information that practitioners can use to evaluate the relative effectiveness of various types of interventions.