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.
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1 Review of Essential Statistical Principles
1.1 Variables and Types of Data
1.2 Significance Tests and Hypothesis Testing
1.3 Significance Levels and Type I and Type II Errors
1.4 Sample Size and Power
1.5 Model Assumptions
2 Introduction to SPSS
2.1 How to Communicate with SPSS
2.2 Data View vs. Variable View
2.3 Missing Data in SPSS: Think Twice Before Replacing Data!
3 Exploratory Data Analysis, Basic Statistics, and Visual Displays
3.1 Frequencies and Descriptives
3.2 The Explore Function
3.3 What Should I Do with Outliers? Delete or Keep Them?
3.4 Data Transformations
4 Data Management in SPSS
4.1 Computing a New Variable
4.2 Selecting Cases
4.3 Recoding Variables into Same or Different Variables
4.4 Sort Cases
4.5 Transposing Data
5 Inferential Tests on Correlations, Counts, and Means
‐Scores in SPSS
5.2 Correlation Coefficients
5.3 A Measure of Reliability: Cohen’s Kappa
5.4 Binomial Tests
5.5 Chi‐square Goodness‐of‐fit Test
‐Test for a Mean
‐Test for Means
6 Power Analysis and Estimating Sample Size
6.1 Example Using G*Power: Estimating Required Sample Size for Detecting Population Correlation
6.2 Power for Chi‐square Goodness of Fit
6.3 Power for Independent‐samples
6.4 Power for Paired‐samples
7 Analysis of Variance
7.1 Performing the ANOVA in SPSS
‐Test for ANOVA
7.3 Effect Size
7.4 Contrasts and Post Hoc Tests on Teacher
7.5 Alternative Post Hoc Tests and Comparisons
7.6 Random Effects ANOVA
7.7 Fixed Effects Factorial ANOVA and Interactions
7.8 What Would the Absence of an Interaction Look Like?
7.9 Simple Main Effects
7.10 Analysis of Covariance (ANCOVA)
7.11 Power for Analysis of Variance
8 Repeated Measures ANOVA
8.1 One‐way Repeated Measures
8.2 Two‐way Repeated Measures: One Between and One Within Factor
9 Simple and Multiple Linear Regression
9.1 Example of Simple Linear Regression
9.2 Interpreting a Simple Linear Regression: Overview of Output
9.3 Multiple Regression Analysis
9.4 Scatterplot Matrix
9.5 Running the Multiple Regression
9.6 Approaches to Model Building in Regression
9.7 Forward, Backward, and Stepwise Regression
9.8 Interactions in Multiple Regression
9.9 Residuals and Residual Plots: Evaluating Assumptions
9.10 Homoscedasticity Assumption and Patterns of Residuals
9.11 Detecting Multivariate Outliers and Influential Observations
9.12 Mediation Analysis
9.13 Power for Regression
10 Logistic Regression
10.1 Example of Logistic Regression
10.2 Multiple Logistic Regression
10.3 Power for Logistic Regression
11 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
11.1 Example of MANOVA
11.2 Effect Sizes
11.3 Box’s M Test
11.4 Discriminant Function Analysis
11.5 Equality of Covariance Matrices Assumption
11.6 MANOVA and Discriminant Analysis on Three Populations
11.7 Classification Statistics
11.8 Visualizing Results
11.9 Power Analysis for MANOVA
12 Principal Components Analysis
12.1 Example of PCA
12.2 Pearson’s 1901 Data
12.3 Component Scores
12.4 Visualizing Principal Components
12.5 PCA of Correlation Matrix
13 Exploratory Factor Analysis
13.1 The Common Factor Analysis Model
13.2 The Problem with Exploratory Factor Analysis
13.3 Factor Analysis of the PCA Data
13.4 What Do We Conclude from the Factor Analysis?
13.5 Scree Plot
13.6 Rotating the Factor Solution
13.7 Is There Sufficient Correlation to Do the Factor Analysis?
13.8 Reproducing the Correlation Matrix
13.9 Cluster Analysis
13.10 How to Validate Clusters?
13.11 Hierarchical Cluster Analysis
14 Nonparametric Tests
14.1 Independent‐samples: Mann–Whitney U
14.2 Multiple Independent‐samples: Kruskal–Wallis Test
14.3 Repeated Measures Data: The Wilcoxon Signed‐rank Test and Friedman Test
14.4 The Sign Test
Closing Remarks and Next Steps
End User License Agreement
Figure 2.1 SPSS Data View (left) vs. Variable View (right).
Table of Contents
Daniel J. Denis
This edition first published 2019© 2019 John Wiley & Sons, Inc.
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Library of Congress Cataloging‐in‐Publication Data
Names: Denis, Daniel J., 1974– author.Title: SPSS data analysis for univariate, bivariate, and multivariate statistics / Daniel J. Denis.Description: Hoboken, NJ : Wiley, 2019. | Includes bibliographical references and index. |Identifiers: LCCN 2018025509 (print) | LCCN 2018029180 (ebook) | ISBN 9781119465805 (Adobe PDF) | ISBN 9781119465782 (ePub) | ISBN 9781119465812 (hardcover)Subjects: LCSH: Analysis of variance–Data processing. | Multivariate analysis–Data processing. | Mathematical statistics–Data processing. | SPSS (Computer file)Classification: LCC QA279 (ebook) | LCC QA279 .D45775 2019 (print) | DDC 519.5/3–dc23LC record available at https://lccn.loc.gov/2018025509
Cover Design: WileyCover Images: © GarryKillian/Shutterstock
The goals of this book are to present a very concise, easy‐to‐use introductory primer of a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences, and to get you started doing data analysis fast. The emphasis on the book is data analysis and drawing conclusions from empirical observations. The emphasis of the book is not on theory. Formulas are given where needed in many places, but the focus of the book is on concepts rather than on mathematical abstraction. We emphasize computational tools used in the discovery of empirical patterns and feature a variety of popular statistical analyses and data management tasks that you can immediately apply as needed to your own research. The book features analyses and demonstrations using SPSS. Most of the data sets analyzed are very small and convenient, so entering them into SPSS should be easy. If desired, however, one can also download them from www.datapsyc.com. Many of the data sets were also first used in a more theoretical text written by the same author (see Denis, 2016), which should be consulted for a more in‐depth treatment of the topics presented in this book. Additional references for readings are also given throughout the book.
This is a “how‐to” book and will be of use to undergraduate and graduate students along with researchers and professionals who require a quick go‐to source, to help them perform essential statistical analyses and data management tasks. The book only assumes minimal prior knowledge of statistics, providing you with the tools you need right now to help you understand and interpret your data analyses. A prior introductory course in statistics at the undergraduate level would be helpful, but is not required for this book. Instructors may choose to use the book either as a primary text for an undergraduate or graduate course or as a supplement to a more technical text, referring to this book primarily for the “how to’s” of data analysis in SPSS. The book can also be used for self‐study. It is suitable for use as a general reference in all social and natural science fields and may also be of interest to those in business who use SPSS for decision‐making. References to further reading are provided where appropriate should the reader wish to follow up on these topics or expand one’s knowledge base as it pertains to theory and further applications. An early chapter reviews essential statistical and research principles usually covered in an introductory statistics course, which should be sufficient for understanding the rest of the book and interpreting analyses. Mini brief sample write‐ups are also provided for select analyses in places to give the reader a starting point to writing up his/her own results for his/her thesis, dissertation, or publication. The book is meant to be an easy, user‐friendly introduction to a wealth of statistical methods while simultaneously demonstrating their implementation in SPSS. Please contact me at [email protected] or [email protected] with any comments or corrections.
When you see this symbol, it means a brief sample write‐up has been provided for the accompanying output. These brief write‐ups can be used as starting points to writing up your own results for your thesis/dissertation or even publication.
When you see this symbol, it means a special note, hint, or reminder has been provided or signifies extra insight into something not thoroughly discussed in the text.
When you see this symbol, it means a special WARNING has been issued that if not followed may result in a serious error.
Thanks go out to Wiley for publishing this book, especially to Jon Gurstelle for presenting the idea to Wiley and securing the contract for the book and to Mindy Okura‐Marszycki for taking over the project after Jon left. Thank you Kathleen Pagliaro for keeping in touch about this project and the former book. Thanks goes out to everyone (far too many to mention) who have influenced me in one way or another in my views and philosophy about statistics and science, including undergraduate and graduate students whom I have had the pleasure of teaching (and learning from) in my courses taught at the University of Montana.
This book is dedicated to all military veterans of the United States of America, past, present, and future, who teach us that all problems are relative.
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