Name: Using R With Multivariate Statistics By Randall E. Schumacker
SKU: 9781483377964
Price: 33.39 USD
Availability: InStock

Using R With Multivariate Statistics Summary

Using R With Multivariate Statistics by Randall E. Schumacker

This book helps students and researchers in the social and behavioural sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.

Using R With Multivariate Statistics Reviews

This book is not only an excellent introductory resource of multivariate statistics using R, but also provides a complete coverage of multivariate statistics. I really love this book and look forward to using it for my stats courses.

-- Jianmin Guan, University of Texas at San Antonio

The use of the programming language R in a meaningful way is a great strength of this book, as is the associated emphasis on matrix algebra. Also, the addition of brief biographies of key statisticians makes this book more interesting. Finally, the range and scope of techniques that are presented is impressive.

-- David E. Drew, Claremont Graduate University

The text is down-to-earth and practical, with a straightforward approach to communicating a set of procedures for analyzing data.

-- Darrell Rudmann, Shawnee State University
[...]I found the directions very clear and was able to run the syntax and get the output very easily. -- Camille L. Bryant, Columbus State University

About Randall E. Schumacker

Dr. Randall E. Schumacker is professor of educational research at the University of Alabama. He has written and co-edited several books, including A Beginner's Guide to Structural Equation Modeling, Third Edition, Advanced Structural Equation Modeling: Issues and Techniques, Interaction and Non-Linear Effects in Structural Equation Modeling, New Developments and Techniques in Structural Equation Modeling, Understanding Statistical Concepts Using S-PLUS, Understanding Statistics Using R, and Learning Statistics Using R. Dr. Schumacker was the founder and is now emeritus editor of Structural Equation Modeling: A Multidisciplinary Journal, and has established the Structural Equation Modeling Special Interest Group within the American Educational Research Association (AERA). He is also the emeritus editor of Multiple Linear Regression Viewpoints, the oldest journal sponsored by AERA (Multiple Linear Regression: General Linear Model Special Interest Group). Dr. Schumacker has conducted international and national workshops, has served on the editorial board of several journals, and currently pursues his research interests in statistics and structural equation modeling. He was the 1996 recipient of the Outstanding Scholar Award and the 1998 recipient of the Charn Oswachoke International Award. In 2010, he launched the DecisionKit App for the iPhone, iPad, and iTouch, which can assist researchers in making decisions about which measurement, research design, or statistic to use in their research projects. In 2011, he received the Apple iPad Award. In, 2012, he received the CIT Faculty Technology Award. In 2013, he received the McCrory Faculty Excellence in Research Award from the College of Education at the University of Alabama. In 2014, Dr. Schumacker was the recipient of the Structural Equation Modeling Service Award at AERA.

Preface Acknowledgments About the Author 1. Introduction and Overview Background Persons of Interest Factors Affecting Statistics R Software Web Resources References 2. Multivariate Statistics: Issues and Assumptions Issues Assumptions SPSS Check Summary Web Resources References 3. Hotelling's T2 : A Two-Group Multivariate Analysis Overview Assumptions Univariate Versus Multivariate Hypothesis Practical Examples Using R Power and Effect Size Reporting and Interpreting Summary Exercises Web Resources References 4. Multivariate Analysis of Variance MANOVA Assumptions MANOVA Example: One-Way Design MANOVA Example: Factorial Design Effect Size Reporting and Interpreting Summary Exercises Web Resources References 5. Multivariate Analysis of Covariance Assumptions Multivariate Analysis of Covariance Reporting and Interpreting Propensity Score Matching Summary Web Resources References 6. Multivariate Repeated Measures Assumptions Advantages of Repeated Measure Design Multivariate Repeated Measure Examples Reporting and Interpreting Results Summary Exercises Web Resources References 7. Discriminant Analysis Overview Assumptions Dichotomous Dependent Variable Polytomous Dependent Variable Effect Size Reporting and Interpreting Summary Exercises Web Resources References 8. Canonical Correlation Overview Assumptions R Packages Canonical Correlation Example Effect Size Reporting and Interpreting Summary Exercises Web Resources References 9. Exploratory Factor Analysis Overview Types of Factor Analysis Assumptions Factor Analysis Versus Principal Components Analysis EFA Example Reporting and Interpreting Summary Exercises Web Resources References Appendix: Attitudes Toward Educational Research Scale 10. Principal Components Analysis Overview Assumptions Basics of Principal Components Analysis Principal Component Example Reporting and Interpreting Summary Exercises Web Resources References 11. Multidimensional Scaling Overview Assumptions R Packages Goodness-of-Fit Index MDS Metric Example MDS Nonmetric Example Reporting and Interpreting Results Summary Exercises Web Resources References 12. Structural Equation Modeling Overview Assumptions Equal Variance-Covariance Matrices Correlation Versus Covariance Matrix R Packages CFA Models Structural Equation Models Reporting and Interpreting Results Summary Exercises Web Resources References Statistical Tables Table 1: Areas Under the Normal Curve (z Scores) Table 2: Distribution of t for Given Probability Levels Table 3: Distribution of r for Given Probability Levels Table 4: Distribution of Chi-Square for Given Probability Levels Table 5: The F Distribution for Given Probability Levels (.05 Level) Table 6: The Distribution of F for Given Probability Levels (.01 Level) Table 7: Distribution of Hartley F for Given Probability Levels Chapter Answers R Installation and Usage R Packages, Functions, Data Sets, and Script Files Index

Additional information

Sku

CIN1483377962G

ISBN 13

9781483377964

ISBN 10

1483377962

Title

Using R With Multivariate Statistics by Randall E. Schumacker

Author

Randall E. Schumacker

Condition

Used - Good

Binding type

Paperback

Publisher

SAGE Publications Inc

Year published

20150908

Number of pages

408

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in good condition, but if you are not entirely satisfied please get in touch with us

Using R With Multivariate Statistics Randall E. Schumacker

Using R With Multivariate Statistics by Randall E. Schumacker

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Using R With Multivariate Statistics by Randall E. Schumacker

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