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The role of between-case effect size in conducting, interpreting, and summarizing single-case research
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Year: 2015 Publisher: [Washington, D.C.] : U.S. Department of Education, Institute of Education Sciences,

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Effect sizes for research : univariate and multivariate applications
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ISBN: 9780415877688 0415877687 9780415877695 0415877695 9780203803233 9781136632303 9781136632341 9781136632358 Year: 2012 Publisher: New York, NY [etc.] Routledge

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"Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. A variety of measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, And S-Plus are provided for the more tedious calculations. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new edition include: Three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance. More learning tools in each chapter including introductions, summaries, "Tips and Pitfalls" and more conceptual and computational questions. More coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research. More software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus. The data used in the book is now provided on the web along with suggested calculations for computational practice. Effect Sizes for Research, 2nd Edition covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. The book clearly demonstrates how the choice of an appropriate measure depends on such factors as whether variables are categorical, ordinal, or continuous; satisfying assumptions; sampling; and the source of variability in the population. Background information on multivariate statistics is provided for those who need it. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended"--


Book
The essential guide to effect sizes : statistical power, meta-analysis, and the interpretation of research results.
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ISBN: 9780521142465 9780521194235 0521142466 0521194237 9780511761676 9780511929793 051192979X 9780511932472 0511932472 0511924755 9780511924750 0511761678 1107204666 0511852320 0511927290 Year: 2013 Publisher: Cambridge Cambridge university press

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"This succinct and jargon-free introduction to effect sizes gives students and researchers the tools they need to interpret the practical significance of their results. Using a class-tested approach that includes numerous examples and step-by-step exercises, it introduces and explains three of the most important issues relating to the practical significance of research results: the reporting and interpretation of effect sizes (Part I), the analysis of statistical power (Part II), and the meta-analytic pooling of effect size estimates drawn from different studies (Part III). The book concludes with a handy list of recommendations for those actively engaged in or currently preparing research projects"--


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Handbook of Meta-analysis in Ecology and Evolution

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Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets

Keywords

Meta-analysis. --- Evolution --- Ecology --- Philosophy --- Creation --- Emergence (Philosophy) --- Teleology --- Medicine --- Psychometrics --- Social sciences --- Mathematical models. --- Statistical methods. --- Research --- Evaluation --- Statistical methods --- Bayesian analysis. --- Bayesian approach. --- Lepidoptera mating. --- allometric scaling. --- average trends. --- biodiversity. --- collaborative research. --- computer software. --- conceptual tool. --- conservation. --- conventional wisdom. --- data analysis. --- data appraisal. --- data collection. --- data extraction. --- data gathering. --- data quality. --- ecology. --- effect size. --- effect sizes. --- evolution. --- evolutionary biology. --- exemplar studies. --- forest plots. --- imputation methods. --- insufficient data. --- interaction effects. --- invasive plants. --- knowledge gaps. --- large-scale monitoring. --- least-squares method. --- literature search. --- management intervention. --- maximum likelihood estimation. --- medicine. --- meta-analysis database. --- meta-analysis. --- meta-analytic process. --- meta-regression plots. --- missing data. --- moment-based approach. --- non-independence. --- parameter estimation. --- partial information. --- phylogenetic nonindependence. --- phylogenetic relationships. --- plant abundance. --- primary data. --- problem formulation. --- publication bias. --- published studies. --- quantitative research synthesis. --- research evidence. --- research pooling. --- research practice. --- research synthesis. --- sample error. --- sampling. --- scatter plots. --- scientific literature. --- scientific publications. --- scoping search. --- sex ratio theory. --- sexual selection. --- small-scale surveys. --- social sciences. --- statistical analysis. --- statistical inference. --- statistical methodology. --- statistical models. --- statistical power. --- statistical software. --- statistical tool. --- study quality. --- study replication. --- study selection. --- subjectivity. --- systematic research synthesis. --- systematic review. --- systematic reviews. --- systematic search. --- temporal change. --- temporal trends. --- visualization.

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