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

A jargon-free introduction for students and researchers looking to interpret the practical significance of their results.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, it explains the reporting and interpretation of effect sizes, the analysis of statistical power, and the meta-analytic pooling of effect size estimates.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, it explains the reporting and interpretation of effect sizes, the analysis of statistical power, and the meta-analytic pooling of effect size estimates.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.List of figures; List of tables; List of boxes; Introduction; Part I. Effect Sizes and the Interpretation of Results: 1. Introduction to effect sizes; 2. Interpreting effects; Part II. The Analysis of Statistical Power: 3. Power analysis and the detection of effects; 4. The painful lessons of power research; Part III. Meta-Analysis: 5. Drawing conclusions using meta-analysis; 6. Minimizing bias in meta-analysis; Last word: thirty recommendations for researchers; Applƒ-