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A jargon-free introduction for students and researchers looking to interpret the practical significance of their results.
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.
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. Effect Sizes for Research 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.
Want to do research that matters? Want results that make sense? This jargon-free introduction to effect sizes will show you how to the answer the dreaded "So what?" question. It will teach you how to interpret the substantive as opposed to the statistical significance of your results. Find out why your estimate of the effect size is the primary output of your study Learn how to calculate effect sizes in a variety of metrics Boost your publication prospects by interpreting the real-world significance of your results Discover the difference between an effect size and a p-value Understand the critical role of effect sizes in power- and meta-analyses And that's just for starters! Give journal editors what they are asking for - results that are engaging and relevant to stakeholders. Using a class-tested approach, Effect Size Matters will provide you with the tools to meaningfully interpret the results of your research. Book 1 of 3 in the MadMethods series. (Get all three books for less than the price of two with MadMethods 1-3!) Get your copy now!
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.
Three books in one! Get the entire MadMethods series in this special edition for one low price! 1. Effect Size Matters: Learn how to do research that matters and get results that make sense. This plain-English guide will show you how to the answer the dreaded "So what?" question. It will teach you how to interpret the substantive or practical significance of your results and convey them in ways that are meaningful to stakeholders. 2. Statistical Power Trip: Many studies lack the statistical power to find effects. This jargon-free guide will show you how to boost the power of underpowered research and design studies that succeed. Learn how to calculate sample sizes and boost your funding prospects with power analyses that dazzle. 3. Meta-Analysis Made Easy: Will your research make a unique contribution? Or are you reinventing the wheel? Using examples proven in the field and tested in the classroom, this quick guide to meta-analysis will show you how to draw conclusions even from inconclusive studies. It will help you discover patterns of meaning in disparate research and uncover gold mines for further research. What readers are saying: ★★★★★ "Who knew statistics was this interesting? I learned a lot!" ★★★★★ "I was impressed by the clarity (and very amusing examples) in this text." ★★★★★ "Some people write to impress and others to instruct. This book is for instruction." ★★★★★ "Exceptionally lucid overview of power analysis, effect sizes, and sample size calculations." ★★★★★ "The clearest presentation of three complex and important subjects." ★★★★★ "An easy read on a complex topic. Great resource!" ★★★★★ "Clearly explains the concepts behind statistical mechanics." ★★★★★ "Easy to read, understand, and follow." Get your copy now.
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.
