Applied Univariate, Bivariate, and Multivariate Statistics. Daniel J. Denis
will help improve the “flow” of the book without the reader stumbling across sections that are insufficiently explained.
Bolded text is used quite liberally to indicate emphasis and signal areas that are key for a good understanding of applied statistics. “Accentuate” bold text when reading the book. They are the key words and themes around which the book was built.
The images in many chapters have been reproduced to make them clearer and more detailed than in the first edition. This is thanks to Wiley's team who has reconstructed many of the figures and diagrams.
Chapter 2 now includes a brief survey of psychometric validity and reliability, along with a simple demonstration of computing Cronbach's alpha in SPSS.
Chapter 3 features a bit more detail and better introduction on the nature of nonparametric statistics in the context of the analysis of variance.
Chapters 7 and 8 on regression have been revised and edited in places to include expanded or new discussion, including a demonstration of power analysis using G*Power in addition to R. Chapter 8 now includes a more thorough and deeper discussion of model selection, and also features a new section that briefly introduces ridge and lasso regression, both penalized regression methods.
Chapter 9 on interactions in regression now contains a brief software demonstration of the analysis of covariance (ANCOVA), conceptualized as a special case of the wider regression model. Some of the theory of the first edition has been removed as it did not seem to serve its intended goal. For readers who would like to delve into the subject of interactions in regression more deeply, additional sources and recommendations are provided.
Chapter 11 now includes R and SPSS code for obtaining Hotelling's T2. While readers can simply use a MANOVA program to evaluate mean vector differences on two groups, the inclusion of the relevant software code for Hotelling's T2 is useful to make the MANOVA chapter a bit more complete.
Chapter 14 on exploratory factor analysis now concludes with a brief introduction and overview of the technique of multidimensional scaling should readers wish to pursue this topic further. By relating the technique somewhat to previously learned techniques, the reader is encouraged to see the learning of new techniques as extending their current knowledge base. This is due to the book emphasizing foundations and fundamental principles of applied statistics, rather than a series of topics seemingly unrelated.
Chapter 15 has been expanded slightly to include a basic demonstration of data analysis using AMOS software. Many users who perform SEM models use AMOS instead of R, and so it seemed appropriate to include a small sample of AMOS output in the context of building a simple path model. Additional references for learning and using AMOS are also provided for those who wish to venture further into structural equation models.
The inclusion in select places brief discussions of, and references to, “Big Data,” as well as data science and machine learning, and why understanding fundamentals and classical statistics is even more important today than ever before in light of these advancements. These fields are heavily computational, but for the most part, have technical origins in fundamental statistics and mathematics. We try our best to key the reader to where these topics “fit” in the wider data analytic landscape, so if they choose to embark on these topics in future study, or further their study of computer science, for example, they have a sense of how many of these techniques build on foundational elements.
Select chapter exercises have been edited as to clarify what they are asking, while a few others have been deleted since they did not seem to work well in the first edition of the book. The majority of the exercises remain conceptually‐based as to encourage a deep and far‐reaching understanding of the material. Select data‐analytic exercises have been either edited or substituted for better ones.
Additional references and citations have been added to supplement the book which already features many classic references to pioneers in applied statistics.
An on‐line Appendix featuring a review of essential mathematics is available at www.datapsyc.com.
ACKNOWLEDGMENTS
I am indebted to all at Wiley who helped in the production of the book, both directly and indirectly. A sincere thank you to Mindy Okura‐Marszycki, Editor at Wiley, who supported the writing of this second edition (the first edition was edited by Steve Quigley and Jon Gurstelle). Thank you as well to all other associates, both professional and unprofessional, who in one way or another influenced my own learning as it concerns statistics and research. Comments, criticism, corrections, and questions about the book are most welcome. Please e‐mail your feedback to [email protected] or [email protected]. Data sets and errata are available at www.datapsyc.com.
Daniel J. Denis
ABOUT THE COMPANION WEBSITE
This book is accompanied by a companion website:
www.wiley.com/go/denis/appliedstatistics2e
The website contains appendix and preface of the first edition.
1 PRELIMINARY CONSIDERATIONS
Still, social science is possible, and needs a strong empirical component. Even statistical technique may prove useful – from time to time.
(Freedman, 1987, As Others See Us: A Case in Path Analysis, p. 125)
Before we delve into the complexities and details that is the field of applied statistics, we first lightly survey some germane philosophical issues that lay at the heart of where statistics fit in the bigger picture of science. Though this book is primarily about applied statistical modeling, the end‐goal is to use statistical modeling in the context of scientific exploration and discovery. To have an appreciation for how statistics are used in science, one must first have a sense of some essential foundations so that one can situate where statistics finds itself within the larger frame of scientific investigation.
1.1 THE PHILOSOPHICAL BASES OF KNOWLEDGE: RATIONALISTIC VERSUS EMPIRICIST PURSUITS
All knowledge can be said to be based on fundamental philosophical assumptions, and hence empirical knowledge derived from the sciences is no different. There have, historically, been two means by which knowledge is thought to be attained. The rationalist derives knowledge primarily from mental, cognitive pursuits. In this sense, “real objects” are those originating from the mind via reasoning and the like, rather than obtained empirically. The empiricist, on the other hand, derives knowledge from experience, that is, one might crudely say, “objective” reality. To the empiricist, knowledge is in the