Effect sizes for research: A broad practical approach.

By R. Grissom, John Kim. 2005

723 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9aee2b77f631192d5f6d34f6901dc9d0aa623f75

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Citations in the corpus

Effect Size in Single-Case Research: A Review of Nine Nonoverlap Techniques (2011, background): “Key publications exploring dominance statistics are Cliff (1993); Grissom and Kim (2005); Huberty and Lowman (2000); Acion, Peterson, Temple, and Arndt (2006); D’Agostino, Campbell, and Greenhouse (2006); and Delaney and Vargha (2002).”
Performance Trajectories and Performance Gaps as Achievement Effect-Size Benchmarks for Educational Interventions (2008, background): “Glass, McGaw, and Smith (1981); Grissom and Kim (2005); Hedges and Olkin (1985); Lipsey and Wilson (2001); Rosenthal (1991, 1994); and Rosenthal, Rosnow, and Rubin”
Neglected biological patterns in the residuals (2011, background): “Both Grissom and Kim (2005) and Erceg-Hurn and Mirosevich (2008) provide a more detailed discussion about why heteroscedasticity occurs in real data sets.”
The Relationship Between “Job Satisfaction” and “Job Performance”: A Meta-analysis (2021, background): “The obtained index summarizes effect sizes of findings in meta-analysis (Grissom & Kim, 2005).”
An improved effect size for single-case research: nonoverlap of all pairs. (2009, background): “For ill-shaped distributions, the mean or median may poorly represent most data points, so an index like AUC, which emphasizes all data values equally, is preferred (Delaney & Vargha, 2002; Grissom & Kim, 2005).”
Contrasting the Effectiveness of Fluency Interventions for Students with or at Risk for Learning Disabilities: A Multilevel Random Coefficient Modeling Meta–Analysis (2006, background): “Otherlimitationsof traditionaleffectsizeindicesinvolvedifﬁcultiesincalcu-lating conﬁdence intervals as they are seriously affected by (a) homoscedasticity and nonnormality (Grissom & Kim, 2005), (b) unequal population sizes, or even (c) cultural factors (Matsumoto, Grissom, & Dinnel, 2001).”
A systematic review of effect size in software engineering experiments (2007, background): “When both variables are dichotomous, the population point-biserial correlation is called Φ and is expressed in terms of the proportions in a 2*2 table, [14].”
A Comprehensive Review of Effect Size Reporting and Interpreting Practices in Academic Journals in Education and Psychology. (2010, methodology): “…secondary analysis or meta-analysis have to use an approximate conversion formula to estimate the effect size because of no access to the raw data (Grissom & Kim, 2005); therefore, researchers working with primary data should report exact effect size to improve the accuracy of estimation for…”
Does Working Memory Training Transfer? A Meta-Analysis Including Training Conditions as Moderators (2015, methodology): “The reason is that in the calculation of g, variances are pooled by using n–1, whereas in the calculation of Cohen’s d, variances are pooled by using n (Grissom & Kim, 2005).”
Professional Development That Supports the Teaching of Cognitive Reading Strategy Instruction (2010, methodology): “The CIOPS is a combination of observational note-taking and a quantitative coding process following the work of Herbert and Attridge (1975). Based on recommendations by DeVellis (2003) and Nunnally and Bernstein (1978), the instrument underwent several rounds of development, redevelopment, and field”
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013, background): “…though not often used, interpretation of differences between groups can be provided by the common language effect size (McGraw and Wong, 1992), also known as the probability of superiority (Grissom and Kim, 2005), which is a more intuitively understandable statistic than Cohen’s d or r .”