Stata for the Behavioral Sciences, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to he reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.
Although this book is not designed as an introduction to Stata, it is appealing even to Stata novices. Throughout the text, Mitchell thoughtfully addresses any features of Stata that are important to understand for the analysis at hand. He also is careful to point out additional resources such as related videos from Stata's YouTube channel.
This book is an easy-to-follow guide to analyzing data using Stata for researchers in the behavioral sciences and a valuable addition to the bookshelf of anyone interested in applying ANOVA methods to a variety of experimental designs.
…for those who know STATA and are interested in ANOVA and ANCOVA, this is a highly useful book, for which there do not seem to be many alternatives. It is also easy to imagine that the book would be useful for experienced users of ANOVA and ANCOVA who are contemplating stepping over from SPSS to STATA.
—Gijs Dekkers, Federal Planning Bureau, Belgium
Warming up. Introduction. Descriptive statistics. Basic inferential statistics. Between-subjects ANOVA models. One-way between-subjects ANOVA. Contrasts for a one-way ANOVA. Analysis of covariance. Two-way factorial between-subjects ANOVA. Analysis of covariance with interactions. Three-way between-subjects analysis of variance. Supercharge your analysis of variance (via regression). Power analysis for analysis of variance and covariance. Repeated measures and longitudinal designs. Repeated measures designs. Longitudinal designs. Regression models. Simple and multiple regression. More details about the regress command. Presenting regression results. Tools for model building. Regression diagnostics. Power analysis for regression. Stata overview. Common features of estimation commands. Postestimation commands. Stata data management commands. Stata equivalents of common IBM SPSS Commands.