ISBN-10:
0521142466
ISBN-13:
9780521142465
Pub. Date:
08/16/2010
Publisher:
Cambridge University Press
The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results

The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results

by Paul D. Ellis

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Overview

The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results

The succinct and jargon-free introduction to effect sizes gives students and researchers the tools they need to interpret the practical significance of their research 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 related to the assessment of practical significance: 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.

Product Details

ISBN-13: 9780521142465
Publisher: Cambridge University Press
Publication date: 08/16/2010
Pages: 194
Sales rank: 613,503
Product dimensions: 6.80(w) x 9.60(h) x 0.40(d)

About the Author

Paul D. Ellis is Professor in the Department of Management and Marketing at Hong Kong Polytechnic University, where he has taught research methods for nearly fifteen years. His research interests include trade and investment issues, marketing and economic development, international entrepreneurship, and economic geography. Professor Ellis has been ranked as one of the world's most prolific scholars in the field of international business.

Table of Contents

List of figures ix

List of tables x

List of boxes xi

Introduction xiii

Part I Effect sizes and the interpretation of results 1

1 Introduction to effect sizes 3

The dreaded question 3

Two families of effects 6

Reporting effect size indexes-three lessons 16

Summary 24

2 Interpreting effects 31

An age-old debate - rugby versus soccer 31

The Problem of interpretation 32

The importance of context 35

The contribution of knowledge 38

Cohen's controversial criteria 40

Summary 42

Part II The analysis of statistical power 45

3 Power analysis and the detection of effects 47

The foolish astronomer 47

The analysis of statistical power 56

Using power analysis to select sample size 61

Summary 66

4 The painful lessons of power research 73

The low power of published research 73

How to boost statistical power 81

Summary 82

Part III Meta-analysis 87

5 Drawing conclusions using meta-analysis 89

The problem of discordant results 89

Reviewing past research - two approaches 90

Meta-analysis in six (relatively) easy steps 97

Meta-analysis as a guide for further research 109

Summary 112

6 Minimizing bias in meta-analysis 116

Four ways to ruin a perfectly good meta-analysis 116

1 Exclude relevant research 117

2 Include bad results 122

3 Use inappropriate statistical models 127

4 Run analyses with insufficient statistical power 130

Summary 131

Last word: thirty recommendations for researchers 134

Appendices

1 Minimum sample sizes 138

2 Alternative methods for meta-analysis 141

Bibliography 153

Index 170

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The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results 5 out of 5 based on 0 ratings. 2 reviews.
Anonymous More than 1 year ago
A-Schwab More than 1 year ago
This practical and very accessible book addresses three key issues for the interpretation of quantitative empirical research in the social sciences: 1. How to determine if the effects observed are of substantive importance? 2. How to use power evaluations for research design and the interpretation of observed effects? 3. How to use meta analyses to draw conclusions from multiple past studies that have reported disparate effects? In my opinion, the main advantage of this book is that it outlines answers to these questions without relying primarily on mathematical equations. Instead, the fundamental logic of the introduced logics is explained in the text and using simple examples. The focus is on introducing methodologies that have been well-established in the research methods literature to a boarder audience of social science researchers. Given the increasing recognition of the severe limitations of standard null-hypothesis significance tests, I find all three topics covered in the book important as they indicate ways how we can substantially improve the interpretation of our empirical research results. I would recommend this book to scholars, doctoral students, and practitioners interested in the advanced interpretation of empirical research findings.