How to Estimate and Interpret Various Effect Sizes

By T. Vacha-haase, B. Thompson. 2004

561 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/29933ceba49f67cdaaac84cfb27b963b7073f54f

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Citations in the corpus

Guidelines for Meta-Analyses of Counseling Psychology Research (2006, background): “This is considered vote counting (Cohen, 1994; Hedges & Olkin, 1980; Lipsey & Wilson, 1993; Schmidt & Hunter, 1996; Vacha-Haase & Thompson, 2004) in which the reviewer counts the number of studies demonstrating significance versus the number failing to demonstrate significance in the predicted…”
A systematic review of effect size in software engineering experiments (2007, background): “His definitions are also used to interpret observed effect sizes, but this is also only advisable when no other sources for effect size estimation are available [43].”
Effect size measures for mediation models: quantitative strategies for communicating indirect effects. (2011, unknown): “We reiterate the “Three Reporting Rules” suggested by Vacha-Haase and Thompson (2004) for reporting effect size estimates; these rules are just as applicable in the mediation context as in many other contexts: 1.”
A Comprehensive Review of Effect Size Reporting and Interpreting Practices in Academic Journals in Education and Psychology. (2010, methodology): “…2006; Ander- son et al., 2000; APA, 2001, 2010; Cumming & Finch, 2002; Fan, 2001; Kirk, 1996, 2001; McLean & Ernest, 1998; Snyder & Lawson, 1993; Thompson, 1996, 1997, 2002b; Vacha-Haase & Thompson, 2004; Vaske, Gliner, &Morgan, 2002; Wilkinson &APA Task Force on Statistical Inference, 1999).”
Interactions between Type of Instruction and Type of Language Feature: A Meta-Analysis. (2010, methodology): “…2010, pp. 263–308 306 Spada and Tomita Type of Instruction and Language Feature d was calculated as in (a), where M stands for the mean, SQRT refers to the squared root, and SD is the standard deviation (adapted from Vacha-Haase & Thompson, 2004, p. 474): d = {M1 − M2}/{SQRT[(SD1SD1 + SD2SD2)/2]}.”
Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education (2016, background): “…refer to (a) effect sizes of similar interventions targeting similar outcomes, (b) the gain in the target outcomes that one would see among target learners without an intervention, and (c) practical significance (Durlak, 2009; Hill, Bloom, Black, & Lipsey, 2008; Vacha-Haase & Thompson, 2004).”
Student evaluations of teaching: combining the meta-analyses and demonstrating further evidence for effective use (2012, background): “Converting d effect sizes to r effect sizes assumes that mean differences are correlational in nature, which results in a variance accounted for effect size (Roberts and Henson 2002 ) and allows researchers to clearly re‐express all effect sizes (Vacha‐Haase and Thompson 2004 ).”
Effect-Size Measures and Meta-Analytic Thinking in Counseling Psychology Research (2006, methodology): “Some other disciplines, such as medicine, do not suffer from this ambiguity to the same degree, where some things (e.g., blood pressure, cholesterol levels) are measured on commonly accepted metrics ( Vacha-Haase & Thompson, 2004 ).”