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Kim, Yukyoum; Lee, J. Lucy – Measurement in Physical Education and Exercise Science, 2019
The purposes of this manuscript are to identify common statistical mistakes in sport management, and to provide scholars with suggestions on how to develop and improve the quality of quantitative research. We have reviewed articles published from 2001 to 2017 in the "Journal of Sport Management," "Sport Management Review,"…
Descriptors: Athletics, Research, Research Problems, Statistical Analysis
Gorard, Stephen; Gorard, Jonathan – International Journal of Social Research Methodology, 2016
This brief paper introduces a new approach to assessing the trustworthiness of research comparisons when expressed numerically. The 'number needed to disturb' a research finding would be the number of counterfactual values that can be added to the smallest arm of any comparison before the difference or 'effect' size disappears, minus the number of…
Descriptors: Statistical Significance, Testing, Sampling, Attrition (Research Studies)
Ruscio, John; Gera, Benjamin Lee – Multivariate Behavioral Research, 2013
Researchers are strongly encouraged to accompany the results of statistical tests with appropriate estimates of effect size. For 2-group comparisons, a probability-based effect size estimator ("A") has many appealing properties (e.g., it is easy to understand, robust to violations of parametric assumptions, insensitive to outliers). We review…
Descriptors: Psychological Studies, Gender Differences, Researchers, Test Results
Citkowicz, Martyna; Hedges, Larry V. – Society for Research on Educational Effectiveness, 2013
In some instances, intentionally or not, study designs are such that there is clustering in one group but not in the other. This paper describes methods for computing effect size estimates and their variances when there is clustering in only one group and the analysis has not taken that clustering into account. The authors provide the effect size…
Descriptors: Multivariate Analysis, Effect Size, Sampling, Sample Size
Manolov, Rumen; Solanas, Antonio – Psychological Methods, 2012
There is currently a considerable diversity of quantitative measures available for summarizing the results in single-case studies. Given that the interpretation of some of them is difficult due to the lack of established benchmarks, the current article proposes an approach for obtaining further numerical evidence on the importance of the results,…
Descriptors: Sampling, Probability, Statistical Significance, Case Studies
Miller, Andrew – Journal of Teaching in Physical Education, 2015
The purpose of this systematic review was to investigate the weight of scientific evidence regarding student outcomes (physical, cognitive and affective) of a Game Centered Approach (GCA) when the quality of a study was taken into account in the interpretation of collective findings. A systematic search of five electronic databases (Sports…
Descriptors: Teaching Methods, Literature Reviews, Educational Games, Children
Gottfried, Michael A. – Elementary School Journal, 2012
This study contributes a novel perspective on grade retention by empirically examining how classroom composition relates to the standardized-testing performance of grade-retained students in their post-retained years. This evaluation employed a sample of entire cohorts of urban elementary school children in the Philadelphia School District over 6…
Descriptors: Grade Repetition, School Holding Power, Evidence, Testing
Zou, Guang Yong – Psychological Methods, 2007
Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate…
Descriptors: Intervals, Effect Size, Research Methodology, Correlation
Peer reviewedMorse, David T. – Educational and Psychological Measurement, 1998
Describes MINSIZE, an MS-DOS computer program that permits the user to determine the minimum sample size needed for the results of a given analysis to be statistically significant. Program applications for statistical significance tests are presented and illustrated. (SLD)
Descriptors: Computer Software, Effect Size, Sample Size, Sampling
Fan, Xitao – 1999
This paper suggests that statistical significance testing and effect size are two sides of the same coin; they complement each other, but do not substitute for one another. Good research practice requires that both should be taken into consideration to make sound quantitative decisions. A Monte Carlo simulation experiment was conducted, and a…
Descriptors: Decision Making, Effect Size, Monte Carlo Methods, Research Methodology
Peer reviewedThompson, Bruce – Educational and Psychological Measurement, 1995
Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)
Descriptors: Analysis of Covariance, Correlation, Effect Size, Evaluation Methods
Peer reviewedHedges, Larry V. – Journal of Educational Statistics, 1984
If the quantitative result of a study is observed only when the mean difference is statistically significant, the observed mean difference, variance, and effect size are biased estimators of corresponding population parameters. The exact distribution of sample effect size and the maximum likelihood estimator of effect size are derived. (Author/BW)
Descriptors: Effect Size, Estimation (Mathematics), Maximum Likelihood Statistics, Meta Analysis
Peer reviewedCarver, Ronald P. – Journal of Experimental Education, 1993
Four things are recommended to minimize the influence or importance of statistical significance testing. Researchers must not neglect to add "statistical" to significant and could interpret results before giving p-values. Effect sizes should be reported with measures of sampling error, and replication can be built into the design. (SLD)
Descriptors: Educational Researchers, Effect Size, Error of Measurement, Research Methodology
Peer reviewedKirk, Roger E. – Educational and Psychological Measurement, 2001
Makes the case that science is best served when researchers focus on the size of effects and their practical significance. Advocates the use of confidence intervals for deciding whether chance or sampling variability is an unlikely explanation for an observed effect. Calls for more emphasis on effect sizes in the next edition of the American…
Descriptors: Effect Size, Hypothesis Testing, Psychology, Research Reports
Rosenthal, Robert – 1989
An overview of the state of the art in psychological research is presented, with an emphasis on the attention given to effect sizes. The acceptance of small effect sizes for biomedical research is contrasted with the rejection of similar effect sizes for psychological research. The Binomial Effect Size Display is used to depict the practical…
Descriptors: Effect Size, Mathematical Models, Meta Analysis, Psychological Studies
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