Practices of data analysis in psychology and related disciplines are changing. This is evident in the longstanding controversy about statistical tests in the behavioral sciences and the increasing number of journals requiring effect size information. This book offers integrative and clear presentations about the limitations of statistical tests and reviews alternative methods of data analysis, such as effect size estimation (at both the group and case levels) and interval estimation (i.e., confidence intervals). Written in a clear and accessible style, it is intended for applied researchers and students who may not have strong quantitative backgrounds. Readers will learn how to measure effect size on continuous or dichotomous outcomes in comparative studies with independent or dependent samples. They will also learn how to calculate and correctly interpret confidence intervals for effect sizes. Numerous research examples from a wide range of areas illustrate the application of these principles and how to estimate substantive significance instead of just statistical significance. Additional alternatives to statistical tests are also described, including meta-analysis, resampling techniques like bootstrapping, and Bayesian estimation.
Stored only when both papers are in the corpus — 1,033 citations exist upstream.