Statistical Research Methods

This expert guide to the entire process of data analysis offers non-specialists a user-friendly introduction using real-life examples. It takes readers from hypothesis generation to the techniques involved in writing up their results in manuscript form.

This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.

Geared to describe the entire data analysis process, from hypothesis generation to writing the results in a manuscript Designed to communicate with non-statisticians in a non-statistical language Complete with real-world examples and sample write-ups for Methods and Results sections of manuscripts Includes supplementary material: sn.pub/extras

Autorentext

Roy Sabo, Department of Biostatistics, Virginia Commonwealth University

Edward Boone, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University

Inhalt
Introduction.- One-Sample Proportions.- Two-Sample Proportions.- Multi-Category Data.- Summarizing Continuous Data.- One-Sample Means.- Two-Sample Means.- Analysis of Variance.- Power and Sample-Size.- Association and Regression.