Effect Sizes for Research: Univariate and Multivariate Applications [NOOK Book]

Overview

The goal of this book is to inform a broad readership about a variety of measures and estimators of effect sizes for research, their proper applications and interpretations, and their limitations. Its focus is on analyzing post-research results. The book provides an evenhanded account of controversial issues in the field, such as the role of significance testing. Consistent with the trend toward greater use of robust statistical methods, the book pays much attention to the statistical assumptions of the methods ...

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Effect Sizes for Research: Univariate and Multivariate Applications

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Overview

The goal of this book is to inform a broad readership about a variety of measures and estimators of effect sizes for research, their proper applications and interpretations, and their limitations. Its focus is on analyzing post-research results. The book provides an evenhanded account of controversial issues in the field, such as the role of significance testing. Consistent with the trend toward greater use of robust statistical methods, the book pays much attention to the statistical assumptions of the methods and to robust measures of effect size.


Effect Sizes for Research discusses different effect sizes for a variety of kinds of variables, designs, circumstances, and purposes. It covers standardized differences between means, correlational measures, strength of association, and confidence intervals. The book clearly demonstrates how the choice of an appropriate measure might depend on such factors as whether variables are categorical, ordinal, or continuous; satisfying assumptions; the sampling method; and the source of variability in the population.


It emphasizes a practical approach through:



  • worked examples using real data;

  • formulas and rationales for a variety of variables, designs, and purposes to help readers apply the material to their own data sets;

  • software references for the more tedious calculations; and

  • informative figures and tables, questions, and over 300 references.

Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is an excellent supplement for advanced courses in statistics in disciplines such as psychology, education, the social sciences, business, management, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended.

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Product Details

  • ISBN-13: 9781135611460
  • Publisher: Taylor & Francis
  • Publication date: 4/4/2014
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 272
  • Sales rank: 1,277,957
  • File size: 3 MB

Meet the Author

Robert J. Grissom is a Professor Emeritus and Adjunct Professor of Psychology at San Francisco State University and a Consultant in Statistics. He received his Ph.D. in Psychology from Princeton University. Co-founder of the Graduate Program in Psychological Research at San Francisco State, Dr. Grissom has written numerous chapters and articles on effect size methodology.

John J. Kim is a Professor of Psychology at San Francisco State University. He received his Ph.D. from the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology in 1993. The current Associate Vice President for Academic Resources at San Francisco State, Dr. Kim has written numerous chapters and articles on effect size methodology.

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Table of Contents

1. Introduction. 2. Confidence Intervals for Comparing the Averages of Two Groups. 3. The Standardized Difference Between Means. 4. Correlational Effect Sizes and Related Topics. 5. Parametric and Nonparametric Effect Size Measures that Go Beyond Comparing Two Averages. 6. Effect Sizes for One-Way ANOVA and Nonparametric Approaches. 7. Effect Sizes for Factorial Designs. 8. Effect Sizes for Categorical Variables. 9. Effect Sizes for Ordinal Categorical Dependent Variables (Rating Scales). 10. Effect Sizes for Multiple Regression/Correlation. 11. Effect Sizes for Analysis of Covariance. 12. Effect Sizes for Multivariate Analysis of Variance.

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