Behavioral Research Data Analysis with R - Jonathan Baron

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Résumé :
This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research. The authors emphasize practical data analytic skills so that they can be quickly incorporated into readers? own research.

Biographie:
Yuelin Li is a research psychologist and a behavioral statistician.? His appointment at Memorial Sloan-Kettering?Cancer Center allows him to apply a range of statistical techniques in understanding complex human behaviors---social network influence of young adult smoking, genetic-environment interaction in cognitive impairment, health behavior change, psychosocial and quality of life outcomes in? cancer treatment, survivorship, and end of life care. Jonathan Baron is Professor of Psychology at the University of Pennsylvania, where he teaches Judgments and Decision and does research people's judgments and decisions about public policies.? He has been fascinated by the promise of computers since about 1960 and has come of age with them and used them in his research.? In 2000, he began the Web site (http://finzi.psych.upenn.edu) and document that led to this book, which was then mostly about data layout, until Yuelin Li (who shared the same PhD advisor, David Krantz) volunteered to help with the more substantive parts.? Baron is founding and current editor of the journal Judgment and Decision Making....

Sommaire:
Introduction.- Reading and Transformting Data Format.- Statistics for Comparing Means and Proportions.- R Graphics and Trellis Plots.- Analysis of Variance.- Linear and Logistic Regression.- Statistical Power and Sample Size Considerations.- Item Response Theory.- Imputation of Missing Data.- Linear Mixed Effects Models in Analyzing Repeated Measures Data.- Linear Mixed Effects Models in Cluster Randomized Studies.