"This is an excellent treatment of a complex subject. [Warner] has done a great job of making the ideas as clear and accessible as possible."
-- W. James Potter, University of California at Santa Barbara
"I very much like the author's style of writing--she explains complex concepts in simple and accessible language."
--Ruth Childs, University of Toronto , Canada
"The book is easy to read. The author provides excellent practical advice, including the benefits and consequences of different statistical methods, as well as useful APA guidelines for research reports."
--Patrick Leung, University of Houston
Applied Statistics: From Bivariate Through Multivariate Techniques provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. For example, "How do researchers' decisions about treatment dosage levels and sample size tend to influence the magnitude of t and F ratios?" Each chapter presents a complete empirical research example to illustrate the application of a specific method, such as multiple regression. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
The robustancillaries include datasets in SPSS and Excel; answers to all comprehension questions; Microsoft® PowerPoint® slides for each chapter; a listing of useful Web sites; and more. Visit sagepub.com/warnerstudy for more information.
Key Features:
Begins with a clear review and a fresh perspective on concepts including effect size, variance partitioning, and statistical control. Depending on student background and the level of the course, instructors can begin with chapters that review basic material, or they can begin with more advanced topics and use earlier chapters as supplemental review material. Examines three-variable research situations in detail and teaches students how to think about statistical control, which is essential for comprehension of multivariate analyses. Includes a chapter on reliability, validity, and multiple item scales, and draws extensively on path models to illustrate theories about possible causal and noncausal associations among variables, beginning with simple three-variable research situations. Utilizes graphics to explain concepts such as variance partitioning, statistical control, and factor rotation. Contains a glossary and extensive practice exercises to help readers digest the material presented.

Rebecca M. Warner

SAGE Publications, Incorporated

Los Angeles: SAGE Publications

AltaMira Press

Corwin Press

United States, United States of America

Thousand Oaks, Calif, ©2008

Los Angeles, Calif, ©2008

1st Edition, PT, 2007

Includes bibliographical references (p. 1049-1055) and index.

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. For example, "How do researchers' decisions about treatment dosage levels and sample size tend to influence the magnitude of t and F ratios?" Each chapter presents a complete empirical research example to illustrate the application of a specific method, such as multiple regression. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions. The robust Instructor's Resource CD-ROM include datasets in SPSS and Excel; answers to all comprehension questions; Microsoft[R] PowerPoint[R] slides for each chapter; a listing of useful Web sites; and more. The book begins with a clear review and a fresh perspective on concepts including effect size, variance partitioning, and statistical control. Depending on student background and the level of the course, instructors can begin with chapters that review basic material, or they can begin with more advanced topics and use earlier chapters as supplemental review material. The book examines three-variable research situations in detail and teaches students how to think about statistical control, which is essential for comprehension of multivariate analyses. It includes a chapter on reliability, validity, and multiple item scales, and draws extensively on path models to illustrate theories about possible causal and noncausal associations among variables, beginning with simple three-variable research situations. Finally, the book utilizes graphics to explain concepts such as variance partitioning, statistical control, and factor rotation. The 13 chapters comprising this book are: (1) Review of Basic Concepts; (2)Introduction to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals; (3) Statistical Significance Testing; (4) Preliminary Data Screening; (5) Comparing Group Means Using the Independent Samples t Test; (6) One-Way Between-Subjects Analysis of Variance; (7) Bivariate Pearson Correlation; (8) Alternative Correlation Coefficients; (9) Bivariate Regression; (10) Adding a Third Variable: Preliminary Exploratory Analyses; (11) Multiple Regression with Two Predictor Variables; (12) Dummy Predictor Variables and Interaction Terms in Multiple Regression; and (13) Factorial Analysis of Variance

Applied From Bivariate Through Multivariate Techniques provides a clear introduction to widely used topics in bivariate and multivariate statistics including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. Author Rebecca M. Warner presents an applied approach that does not require formal mathematics and equations are accompanied by verbal explanations.

With an approach that does not require formal mathematics training, this textbook provides a clear introduction to widely used topics in multivariate statistics, including Multiple Regression, Discriminant Analysis, MANOVA, Factor Analysis, and Binary Logistic Regression. Each chapter has a glossary and comprehension questions.

Includes bibliographical references (p. 1049-1055) and index

2023-06-28