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PREFACE

Technology is not progress. Empathy is. The dogs are watching us.

Now in its second edition, this book provides a general introduction and overview of univariate through to multivariate statistical modeling techniques typically used in the social, behavioral, and related sciences. Students reading this book will come from a variety of fields, including psychology, sociology, education, political science, biology, medicine, economics, business, forestry, nursing, chemistry, law, among others. The book should be of interest to anyone who desires a relatively compact and succinct survey and overview of statistical techniques useful for analyzing data in these fields, while also wanting to understand and appreciate some of the theory behind these tools. Spanning several statistical methods, the focus of the book is naturally one of breadth than of depth into any one particular technique, focusing on the unifying principles as well as what substantively (scientifically) can or cannot be concluded from a method when applied to real data. These are topics usually encountered by upper division undergraduate or beginning graduate students in the aforementioned fields.

The first edition has also been used widely as a reference resource for both students and researchers working on dissertations, manuscripts, and other publications. It is hoped to provide the student with a “big picture” overview of how applied statistical modeling works, while at the same time providing him or her the opportunity in many places to implement, to some extent at least, many of these models using SPSS and/or R software. References and recommendations for further reading are provided throughout the text for readers who wish to pursue these topics further. Each topic and software demonstration can literally be “unpacked” into a deeper discussion, and so long as the reader is aware of this, they will appreciate this book for what it is—a bird's eye view of applied statistics, and not the “one and only” source they should refer to when conducting analyses. The book does not pretend to be a complete compendium of each statistical method it discusses, but rather is a survey of each method in hopes of conveying how these methods generally “work,” what technical elements unites virtually all of them, and the benefits and limitations of how they may be used in addressing scientific questions.

This second edition has been revised to make the book clearer and more accessible compared to the first edition. The book also contains a gentle introduction (“foot in the door”) to a variety of new topics that did not appear in the first edition. All chapters have been edited to varying degrees to improve clarity of prose and in places provide more information or clarification of the concept under discussion. The following is a summary of updates and revisions in the second edition:

 Significant revision and corrections of errata appearing in the first edition. The second edition is a stronger and better book because it has been thoroughly re‐read and edited in places where rewording was required. In this sense, the second edition has undergone very much “vetting” since the first edition. At the same time, some sections have been entirely deleted from the first edition due to their explanations being too brief to make them worthwhile. These are sections that did not seem to “work” in the first edition, so they were omitted in the second. This hopefully 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 daniel.denis@umontana.edu or email@datapsyc.com. Data sets and errata are available at www.datapsyc.com.

Daniel J. Denis