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.
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