Daniel J. Denis 
Applied Univariate, Bivariate, and Multivariate Statistics 
Understanding Statistics for Social and Natural Scientists, With Applications in SPSS and R

AN UPDATED GUIDE TO STATISTICAL MODELING TECHNIQUES USED IN THE SOCIAL AND NATURAL SCIENCES

This revised and updated second edition of Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R contains an accessible introduction to statistical modeling techniques commonly used in the social and natural sciences. The text offers a blend of statistical theory and methodology and reviews both the technical and theoretical aspects of good data analysis.

Featuring applied resources at various levels, the book includes statistical techniques using software packages such as R and SPSS¯®. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. The second edition has been designed to be more approachable by minimizing theoretical or technical jargon and maximizing conceptual understanding with easy-to-apply software examples. This important text:

* Offers demonstrations of statistical techniques using software packages such as R and SPSS¯®

* Contains examples of hypothetical and real data with statistical analyses

* Provides historical and philosophical insights into many of the techniques used in modern science

* Includes a companion website that features further instructional details, additional data sets, and solutions to selected exercises



Written for students of social and applied sciences, Applied Univariate, Bivariate, and Multivariate Statistics, Second Edition offers a thorough introduction to the world of statistical modeling techniques in the sciences.

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Preface xviii

About the Companion Website xxi

1 Preliminary Considerations 1

2 Introductory Statistics 16

3 Analysis of Variance: Fixed Effects Models 97

4 Factorial Analysis of Variance: Modeling Interactions 146

5 Introduction to Random Effects and Mixed Models 175

6 Randomized Blocks and Repeated Measures 204

7 Linear Regression 232

8 Multiple Linear Regression 286

9 Interactions in Multiple Linear Regression 316

10 Logistic Regression and the Generalized Linear Model 333

11 Multivariate Analysis of Variance 361

12 Discriminant Analysis 394

13 Principal Components Analysis 423

14 Factor Analysis 449

15 Path Analysis and Structural Equation Modeling 497

References 534

Index 548

เกี่ยวกับผู้แต่ง

DANIEL J. DENIS, Ph D, is Professor of Quantitative Psychology at the University of Montana where he teaches courses in univariate and multivariate statistics. He has published a number of articles in peer-reviewed journals and has served as consultant to researchers and practitioners in a variety of fields.