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 pra
Behavioral Research Data Analysis with R || Linear Mixed-Effects Models in Analyzing Repeated-Measures Data
β Scribed by Li, Yuelin; Baron, Jonathan
- Book ID
- 120154606
- Publisher
- Springer New York
- Year
- 2011
- Tongue
- English
- Weight
- 369 KB
- Edition
- 2012
- Category
- Article
- ISBN
- 1461412382
No coin nor oath required. For personal study only.
β¦ Synopsis
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 readersβ own research.
π SIMILAR VOLUMES
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 pra
Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. A simple and convenient approach to analysis is to develop summary measures for each individual and then regress the summary measures on between-subject covariates