Measures of Effect Size for Comparative Studies: Applications, Interpretations, and Limitations (original) (raw)
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The Effect Size Statistic: Overview of Various Choices
2000
Over the years, methodologists have been recommending that researchers use magnitude of effect estimates in result interpretation to highlight the distinction between statistical and practical significance (cf. R. Kirk, 1996). A magnitude of effect statistic (i.e., effect size) tells to what degree the dependent variable can be controlled, predicted, or explained by the independent variable (P. Snyder and S. Lawson, 1993). There are a number of ways one can compute an effect size statistic as part of data analysis. There is no concept of "one size fits all" (B. Thompson, 1999), so it is up to the smart researcher to choose the index best suited for a particular research endeavor. It has now become necessary that such a statistic always be included to enable other researchers to carry out meta-analyses and to inform judgment regarding the practical significance of results. This paper provides a tutorial summary of some of the effect size choices so that researchers will be able to follow the recommendations of the American Psychological Association (APA) publication manual, those of the APA Task Force on Statistical Inference, and the publication requirements of some journals. (Contains 3 tables and 11 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
A Primer on Basic Effect Size Concepts
2001
The increased interest in reporting effect sizes means that it is necessary to consider what should be included in a primer on effect sizes. A review of papers on effect sizes and commonly repeated statistical analyses suggests that it is important to discuss effect sizes relative to bivariate correlation, t-tests, analysis of variance/covariance, and multiple regression/correlation. An agreed upon nomenclature regarding effect sizes should be established. R. Rosenthal (1994) has classified effect sizes into the "r" family (the Pearson product moment correlation coefficient and the various squared indices of "r" and "r"-type quantities) and the "d" family (mean difference and standardized mean difference indices). Other measures of effect size have been suggested, and some suggestions are given for further reading on these measures. Parsimony and replication should be joined by meaning as principles to consider in reporting research results. To enhance meaning and interpretability of research findings, it is essential that various psychometric variables and test scores be studied and reported for specific samples under varied conditions. (Contains 29 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
Effect size estimates: Current use, calculations, and interpretation
2012
The Publication Manual of the American Psychological Association (American Psychological Association, 2001, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial 2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.
An effect size primer: A guide for clinicians and researchers
Professional Psychology: Research and …, 2009
Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data. Nonetheless, the use of effect size reporting remains inconsistent, and interpretation of effect size estimates continues to be confused. Researchers are presented with numerous effect sizes estimate options, not all of which are appropriate for every research question. Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice. The current article provides a primer of effect size estimates for the social sciences. Common effect sizes estimates, their use, and interpretations are presented as a guide for researchers.
Recent years have witnessed a growing number of published reports that point out the need for reporting various effect size estimates in the context of null hypothesis testing (H0) as a response to a tendency for reporting tests of statistical significance only, with less attention on other important aspects of statistical analysis. In the face of considerable changes over the past several years, neglect to report effect size estimates may be noted in such fields as medical science, psychology, applied linguistics, or pedagogy. Nor have sport sciences managed to totally escape the grips of this suboptimal practice: here statistical analyses in even some of the current research reports do not go much further than computing p-values. The p-value, however, is not meant to provide information on the actual strength of the relationship between variables, and does not allow the researcher to determine the effect of one variable on another. Effect size measures serve this purpose well. While the number of reports containing statistical estimates of effect sizes calculated after applying parametric tests is steadily increasing, reporting effect sizes with non-parametric tests is still very rare. Hence, the main objectives of this contribution are to promote various effect size measures in sport sciences through, once again, bringing to the readers’ attention the benefits of reporting them, and to present examples of such estimates with a greater focus on those that can be calculated for non-parametric tests.
How to Select, Calculate, and Interpret Effect Sizes
Journal of Pediatric Psychology, 2009
The objective of this article is to offer guidelines regarding the selection, calculation, and interpretation of effect sizes (ESs). To accomplish this goal, ESs are first defined and their important contribution to research is emphasized. Then different types of ESs commonly used in group and correlational studies are discussed. Several useful resources are provided for distinguishing among different types of effects and what modifications might be required in their calculation depending on a study's purpose and methods. This article should assist producers and consumers of research in understanding the role, importance, and meaning of ESs in research reports.
The purpose of this study was to determine: (1) the extent to which effect sizes vary by chance; (2) the proportion of standardized effect sizes that achieve or exceed commonly used criteria for small, medium, and large effect sizes; (3) whether standardized effect sizes are random or systematic across numbers of groups and sample z:izes; and (4) whether it is possible to predict standardized effect sizes using degrees of freedom, number of groups, and sample sizes. Monte Carlo procedures were used to generate standardized effect sizes in a one-way analysis of variance situation with 2 through 10 groups with samples sizes from 5 to 100 in steps of 5. Within each of the 180 configurations, 5,000 replications were done. It was found that standardized effect size variation was systematic rather than random. Numbers of groups and sample sizes were highly predictive of standardized effect size, but error degrees of freedom was not predictive. Equations were developed that could be used t...
Empirically Based Criteria for Determining Meaningful Effect Size
1999
The purpose of this study was to determine: (1) the extent to which effect sizes vary by chance; (2) the proportion of standardized effect sizes that achieve or exceed commonly used criteria for small, medium, and large effect sizes; (3) whether standardized effect sizes are random or systematic across numbers of groups and sample z:izes; and (4) whether it is possible to predict standardized effect sizes using degrees of freedom, number of groups, and sample sizes. Monte Carlo procedures were used to generate standardized effect sizes in a one-way analysis of variance situation with 2 through 10 groups with samples sizes from 5 to 100 in steps of 5. Within each of the 180 configurations, 5,000 replications were done. It was found that standardized effect size variation was systematic rather than random. Numbers of groups and sample sizes were highly predictive of standardized effect size, but error degrees of freedom was not predictive. Equations were developed that could be used to predict standardized effect sizes that could be expected by chance, using number of groups and sample size as the predictor variables. The prediction equations were extremely accurate. This research provides a better alternative for the evaluation of empirical standardized effect sizes than the somewhat arbitrary and fixed criteria often used to classify standardized effect sizes as small, medium, or large. (Contains 3 tables, 10 figures, and 34 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
How to calculate effect sizes from published research: A simplified methodology
This article provides a simplified methodology for calculating Cohen's d effect sizes from published experiments that use t-tests and F-tests. Accompanying this article is a Microsoft Excel Spreadsheet to speed your calculations. Both the spreadsheet and this article are available as free downloads at www.work-learning.com/effect\_sizes.htm.