SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

Enables readers to start doing actual data analysis fast for a truly hands–on learning experience

This concise and very easy–to–use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction.

SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non–Parametric Tests. This helpful resource allows readers to:

Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real–world application to apply concepts from the book to actual research

Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent how–to book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go–to source for performing essential statistical analyses and data management tasks.

Preface

Acknowledgements

Chapter 1 Review of Essential Statistical Principles Big Picture on Statistical Modeling and Inference

Chapter 2 Introduction to SPSS

Chapter 3 Exploratory Data Analysis, Basic Statistics, and Visual Displays

Chapter 4 Data Management in SPSS

Chapter 5 Inferential Tests on Correlations, Counts, and Means

Chapter 6 Power Analysis and Estimating Sample Size

Chapter 7 Analysis of Variance Fixed and Random Effects

Chapter 8 Repeated Measures ANOVA

Chapter 9 Simple and Multiple Linear Regression

Chapter 10 Logistic Regression

Chapter 11 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis

Chapter 12 Principal Components Analysis

Chapter 13 Exploratory Factor Analysis

Chapter 14 Non–Parametric Tests

Closing Remarks & Next Steps

Index

Daniel J. Denis, PhD, is Professor of Quantitative Psychology in the Department of Psychology at the University of Montana where he teaches courses in applied univariate and multivariate statistics. He has published several articles in peer–reviewed journals and regularly serves as consultant to researchers and practitioners in a variety of fields.