Effect Sizes for Research : Univariate and Multivariate Applications, Second Edition

By R. Grissom, John Kim. 2005

579 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/45044f0c62d324f8a22241337908c2973eec7a8b

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Citations in the corpus

A Simple Method for Removing Bias From a Popular Measure of Standardized Effect Size: Adjusted Partial Eta Squared (2019, background): “…2013) and at least one of these alternatives has been strongly recommended in most detailed, statistical discussions (e.g., Carroll & Nordholm, 1975; Grissom & Kim, 2012; Hays, 1963; Keppel, 1991; Maxwell & Delaney, 2004; see also Bakeman, 2005; Olejnik & Algina, 2003), neither of these…”
Assessing statistical aspects of test fairness with structural equation modelling (2013, methodology): “The realization that statistical significance does not directly measure effect size has motivated the development of various methods for estimating effect size apart from statistical significance (e.g., Ellis, 2010; Grissom & Kim, 2011).”
Eta- and partial eta-squared in L2 research: A cautionary review and guide to more appropriate usage (2018, methodology): “With this position in mind, some have gone as far as to argue that failing to appropriately report estimates of effect sizes amounts to ‘a kind of withholding of evidence’ (Grissom and Kim, 2012: 9).”
Statistical reform in information retrieval? (2014, background): “Finally, I pose a speci.c question for the IR community: should IR journal editors and SIGIR PC chairs require (rather than encourage) reporting of e.ect sizes and con.dence intervals?”
Estimating Population Mean Power Under Conditions of Heterogeneity and Selection for Significance (2020, methodology): “This usage is consistent with that of Cohen (1988), who freely uses “effect size" to describe various functions of the model parameters, even for the same statistical test (Grissom & Kim, 2012).”
Correlational effect size benchmarks. (2015, unknown): “…statistical anal- ysis (e.g., meta-analysis; Bayesian techniques; Kruschke, Aguinis, & Joo, 2012), and the assessment of scientific progress (Cohen, 1988; Cumming, 2012; Grissom & Kim, 2012; Ozer, 1985), as well as practical significance (Aguinis et al., 2010; Brooks, Dalal, & Nolan, 2014).”
Many Labs 3: Evaluating participant pool quality across the academic semester via replication (2016, background): “The authors reported that a sign test (Grissom & Kim, 2012) showed a significant bias favoring the first position, PSdep = 0.67, p= .000004, d= .82, 95% CI= [.47, 1.17].”