Effect size estimates: current use, calculations, and interpretation.

By C. O. Fritz, Peter E. Morris, Jennifer J. Richler. Journal of experimental psychology. General, 2012

4,913 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/63ea7f6c0ded688aa717e1a6d35496569e1f8cb5

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Citations in the corpus

Effect Size, Confidence Intervals and Statistical Power in Psychological Research (2015, background): “Fritz, Morris, and Reichler (2012) analyzed studies that were published in the Journal of Experimental Psychology: General, during the period from 2009 to 2010 and found that less than half of the studies reported the effect size, and none of them reported the effect size with its respective…”
A Systematic Review of Teacher-Delivered Behavior-Specific Praise on K–12 Student Performance (2019, positive): “BC-SMD effect sizes can be interpreted following other standardized mean difference effect size interpretation (e.g., Fritz, Morris, & Richler, 2012): small (0.20–0.50), medium (0.50–0.80), large ( (cid:116) 0.80).”
A Meta‐Analysis on the Effects of Text Structure Instruction on Reading Comprehension in the Upper Elementary Grades (2020, methodology): “Because the sample sizes of these six studies were quite large (n > 42), the calculated value of Cohen’s d was practically identical to the Hedges’ g value (Borenstein et al., 2009; Fritz et al., 2012).”
The need to report effect size estimates revisited. An overview of some recommended measures of effect size (2014, methodology): “A way out of this problem is to calculate the partial eta-squared statistic ( ), ηp 2 where a given factor is seen as playing a role in explaining the portion of variance in the dependent variable provided that other effects (factors) present in the analysis have been excluded [6].”
Modeling Clustered Data with Very Few Clusters (2016, methodology): “…(the area of application motiving the study) seen in practice (Hedges & Hedberg, 2007).11 Population values for regression coefficients were based on eta squared and then converted to Cohen’s d as outlined in Fritz, Morris, and Richler (2012) and are presented in Table 3 for ease of interpretation.”
Covariational reasoning among U.S. and South Korean secondary mathematics teachers (2017, background): “PS is the long-run relative frequency that a randomly selected member of the “superior” group will have a higher score than a randomly selected member of the “subordinate” group (Fritz, Morris, & Richler, 2012).”