R Data Analysis without Programming

David W. Gerbing (Portland State University, USA)

ISBN 10: 103224402X / ISBN 13: 9781032244020

Published by Taylor & Francis, 2023

About this Item

David Gerbing is Professor in the Applied Data Science program in the School of Business, Portland State University. He has published extensively in a wide range of social, behavioral, and methodological journals, and contributed some ba.

Seller Inventory # 703646885

Bibliographic Details

• Title: R Data Analysis without Programming
• Publisher: Taylor & Francis
• Publication Date: 2023
• Language: English
• ISBN 10: 103224402X
• ISBN 13: 9781032244020
• Binding: Gebunden
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition’s introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the lessR enhancements added to the standard R environment. Using lessR, data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user.

Highlights along with content new to this edition include:

• Explanation and Interpretation of all data analysis techniques; much more than a computer manual, this book shows the reader how to explain and interpret the results.
• Introduces the concepts and commands reviewed in each chapter.
• Clear, relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing.
• Extensive margin notes highlight, define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number for the initial introduction.
• Scenarios that highlight the use of a specific analysis followed by the corresponding R/lessR input, output, and an interpretation of the results.

Numerous examples of output from psychology, business, education, and other social sciences, that demonstrate the analysis and how to interpret results.

• Two data sets are analyzed multiple times in the book, provide continuity throughout.
• Comprehensive: A wide range of data analysis techniques are presented throughout the book.
• Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective.
• End of chapter problems help readers test their understanding of the concepts.
• A website at www.lessRstats.com that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, R/lessR videos to help readers better understand the program, and more.

This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course, research methods, and/or any data analysis course, taught in departments of psychology, business, education, and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge, though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed.

About the Author

David Gerbing is Professor in the Applied Data Science program in the School of Business, Portland State University. He has published extensively in a wide range of social, behavioral, and methodological journals, and contributed some basic methodological concepts to develop the two-step method for the analysis called structural equation modeling.