On effect size.

@article{Kelley2012OnES,
  title={On effect size.},
  author={Ken Kelley and Kristopher J Preacher},
  journal={Psychological methods},
  year={2012},
  volume={17 2},
  pages={
          137-52
        }
}
The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect… 
The Measurement and Communication of Effect Sizes in Management Research
The measurement and communication of the effect size of an independent variable on a dependent variable is critical to effective statistical analysis in the Social Sciences. We develop ideas about
Researchers’ choice of the number and range of levels in experiments affects the resultant variance-accounted-for effect size
TLDR
It is found that researchers may affect the resultant effect size to be either double or half simply by suitably choosing the number of levels and their ranges, and it is confirmed that this relation also applies to sample effect size indices in much the same way.
The Meaningfulness of Effect Sizes in Psychological Research: Differences Between Sub-Disciplines and the Impact of Potential Biases
TLDR
Certain biases have caused a dramatic inflation in published effects, making it difficult to compare an actual effect with the real population effects (as these are unknown), and there were very large differences in the mean effects between psychological sub-disciplines and between different study designs,Making it impossible to apply any global benchmarks.
Recalibrating expectations about effect size: A multi-method survey of effect sizes in the ABCD study
TLDR
It is found that empirically determined effect sizes from a notably large dataset are smaller than would be expected based on existing heuristics.
Planning sample sizes when effect sizes are uncertain: The power-calibrated effect size approach.
TLDR
A power-calibrated effect size (PCES) approach to sample size planning is proposed that accounts for the uncertainty associated with an effect size estimate in a properly calibrated manner: sample sizes determined on the basis of the PCES are neither too small nor too large and thus provide the desired level of power.
How Big Is “Big”? Interpreting Effect Sizes in L2 Research
The calculation and use of effect sizes—such as d for mean differences and r for correlations—has increased dramatically in second language (L2) research in the last decade. Interpretations of these
Improving IS Practical Significance through Effect Size Measures
ABSTRACT Evidence-based practice in management assigns a high value to research results as a guide to practices that have been rigorously shown to be effective. To emphasize the practical relevance
Effect sizes for contrasts of estimated marginal effects
  • B. Shaw
  • Psychology
    The Stata Journal: Promoting communications on statistics and Stata
  • 2022
The statistical literature is replete with calls to report standardized measures of effect size alongside traditional p-values and null hypothesis tests. While effect-size measures such as Cohen’s d
Reporting Effect Sizes in Original Psychological Research: A Discussion and Tutorial
TLDR
Recommendations for reporting and interpreting effect sizes and their confidence intervals should directly answer their motivating research questions, be comprehensible to the average reader, and be based on meaningful metrics of their constituent variables.
New Recommendations on the Use of R-Squared Differences in Multilevel Model Comparisons
TLDR
A more general set of total, within-clusters, and between-cluster R-squared difference measures than previously considered in MLM comparisons are defined and given to give researchers concrete step-by-step procedures for identifying which measure is relevant to which model comparison.
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The present article provides a primer on (a) effect sizes, (b) confidence intervals, and (c) confidence intervals for effect sizes. Additionally, various admonitions for reformed statistical practice
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Factors such as reliability, range restriction and differences in design that distort standardized effect size unless suitable corrections are employed are explored.
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TLDR
The authors demonstrate the issue by focusing on two popular effect-size measures, the correlation coefficient and the standardized mean difference, both of which can be used when one variable is dichotomous and the other is quantitative.
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The present article presents a tutorial on how to estimate and interpret various effect sizes. The 5th edition of the Publication Manual of the American Psychological Association (2001) described the
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TLDR
The first new effect size index is described is a residual-based index that quantifies the amount of variance explained in both the mediator and the outcome and the second new effectsize index quantifying the indirect effect as the proportion of the maximum possible indirect effect that could have been obtained, given the scales of the variables involved.
It's Not Effect Sizes So Much as Comments About Their Magnitude That Mislead Readers
Abstract The authors investigated the influence of effect size and comment inclusion on readers' perceptions of research results. In three experiments, undergraduates, graduates, and faculty read a
Effect size, confidence interval and statistical significance: a practical guide for biologists
TLDR
This article extensively discusses two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta‐analysis.
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TLDR
Several measures of effect size that might be used in group comparison studies involving univariate and/or multivariate models are discussed.
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Reform of statistical practice in the social and behavioral sciences requires wider use of confidence intervals (CIs), effect size measures, and meta-analysis. The authors discuss four reasons for
A Simple, General Purpose Display of Magnitude of Experimental Effect
We introduce the binomial effect size display (BESD), which is useful because it is (a) easily understood by researchers, students, and lay persons; (b) widely applicable; and (c) conveniently
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