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Cover of Understanding Behavioral Statistics v1.0
November 2023
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ISBN (Digital): 

Understanding Behavioral Statistics

Version 1.0
By Robert F. Lorch Jr.

Key Features

  • Over 54 embedded hyperlinks to streaming videos enrich hybrid and online courses, engage students, and reinforce or augment many of the presented topics
  • Structured in two overall parts: Part One: Univariate Procedures (Chapters 1–13) and Part Two: Bivariate procedures (Chapters 14 and 15). The advantages of treating bivariate descriptive statistics later in the book are: (1) the distinction between univariate and bivariate statistics is made explicit and clear; and (2) descriptive and inferential procedures in bivariate statistics are treated consecutively so there is no need to refresh student’s memory for the descriptive statistics before introducing the inferential tests
  • Organized around the key theme that all inferential procedures depend on the ability to compute probabilities of events. The centrality of probability is introduced in Sections 1 and 2 of Chapter 4 on probability and the role of probability in hypothesis-testing and power computations is elaborated when these topics are introduced in Chapter 6. The general idea that the ability to make inferences depends on separating sampling error (“noise”) from true effects (“signal”) is referred to throughout the book
  • The core inferential procedures of hypothesis testing, confidence intervals, effect size estimation and power are returned to repeatedly across different domains (i.e., binomial distribution, Normal distribution, t-distribution, Chi-square, correlation and regression) to emphasize the common logic of each procedure across the domains
  • Chi-Square is presented early in the text instead of at the end of the book. Chi-Square (Chapter 7) immediately follows procedures based on the Binomial (Chapter 6) because both distributions are used to analyze frequency data.  This organization also places the Chi-square chapter close to the development of probability theory (i.e., Multiplicative Law) that is central to logic of the Test of Association.
  • Unique treatment of contrast procedures in which the computation of the t-test is identical across all contexts and only the critical value of t is adjusted to control familywise error rate (Chapters 12 and 13). This approach is easier for students to understand because it separates computation of the test statistic from the conceptual need to control Type 1 error rate across multiple tests
  • Expanded coverage of bivariate inferential procedures reflects the extensive use of regression-based statistics in the behavioral sciences and the need for students to develop a clear understanding of correlation and regression statistics
  • Supplementary Appendix on using SPSS (Appendix A) includes an overview of how to use the popular software. Relevant chapters in the book include illustrations of the applications of specific SPSS programs to datasets, along with exercises for students.  
  • Five optional supplementary Appendices to enrich advanced or master’s level courses. Appendix B establishes the place and role of statistical analysis in the broader research context. The remaining four Appendices describe authentic, large-scale studies requiring multiple analyses. The approach to determining which analyses are relevant is described and the analyses are carried out and interpreted.
  • Supportive learning structure includes:
    • “Learning Questions” at the start of each main section help to orient students to their goals in studying the section
    • “Key Terms” and marginal glossary help students understand and become comfortable with important vocabulary and phrases
    • “Key Takeaways” at the end of each main section reflect the corresponding Learning Objectives and summarize key ideas in bullet-point fashion. “Key Takeaways” enable the learner to pause and consolidate the information just read into a ‘chunk.’ This process enables the reader to better understand and retain the section’s content and its key concepts
  • End-of-Chapter Material
    • “Test Your Understanding” questions recap the Learning Objectives for the chapter. Stated as open-ended questions rather than multiple-choice questions, they require deeper understanding of chapter concepts and procedures. Answer guidelines are included in the Instructor’s Manual
    • Homework Exercises provide practice with procedures, extend students’ understanding to novel situations, and get students to think more about important concepts. Elaborated answers to the chapter exercises are compiled in the Instructor’s Manual
    • “Using SPSS” illustrates how to execute the relevant SPSS program using datasets and practice problems
    • Answer guidelines to all chapter-end material are included in the Instructor’s Manual
  • All supplements are written by the author to ensure an excellent match with the book’s content. The Instructor’s Manual includes suggested answer guidelines for all discussion questions and exercises.


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Understanding Behavioral Statistics is appropriate for the first course in basic statistical methods applied to selected behavioral sciences at the undergraduate and master’s levels in both two- and four-year colleges and universities. Courses may be called Introduction to Behavioral Statistics, Behavioral Statistics, Psychological Statistics, Educational Statistics or similar titles, and are typically offered in departments of psychology, education, and educational psychology. 

Understanding Behavioral Statistics helps students attain statistical literacy by achieving three broad goals: (1) Understand basic descriptive statistics, including how to present and use tables and graphs and the common measures of properties of distributions, (2) Fully appreciate how the process of inferring population characteristics from sample characteristics is based on the ability to compute probabilities of study outcomes, and (3) Understand the logic and interpretation of the four major inferential procedures in conventional statistical applications—hypothesis testing, confidence intervals, effect size estimation, and power. This book encourages learners to attain statistical literacy by treating all statistical concepts and procedures as logical and meaningful, particularly for students who are uncomfortable with or anxious about mathematics.

Understanding Behavioral Statistics is distinguished from its closely related variation called Making Sense of Behavioral Statistics by how statistical inference is first introduced. In Understanding Behavioral Statistics, probability laws presented in Chapter 4 are used to develop the binomial distribution (Chapter 5) which, in turn, is used to introduce hypothesis testing and power. Therefore, Understanding Behavioral Statistics is most appropriate for faculty who want to emphasize the central role of probability in statistical inference and prefer to use the binomial distribution to explicitly connect the probability theory of Chapter 4 to the logic of inference as developed in Chapter 6.

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Instructor’s Manual

Instructor’s Manual

The Instructor’s Manual guides you through the main concepts of each chapter and important elements such as learning objectives, key terms, and key takeaways. Can include answers to chapter exercises, group activity suggestions, and discussion questions.

Instructor’s Manual

PowerPoint Lecture Notes

PowerPoint Lecture Notes

A PowerPoint presentation highlighting key learning objectives and the main concepts for each chapter are available for you to use in your classroom. You can either cut and paste sections or use the presentation as a whole.

PowerPoint Lecture Notes

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Test Bank Files for Import to Learning Management Systems

Test Bank Files for Import to Learning Management Systems

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Test Item File

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Sample Syllabi

Sample Syllabi

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Other Supplements

Other Supplements

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Robert F. Lorch Jr. University of Kentucky

Robert F. Lorch, Jr. (PhD University of Massachusetts) is Professor of Psychology at the University of Kentucky. Bob currently teaches courses in cognitive science and statistics. He is the recipient of a Fulbright Research Fellowship and past chair of the University of Kentucky’s psychology department.

Bob’s research interests focus on text processing, particularly processing of expository material (i.e., textbook material), the underlying comprehension of text as a person reads, and the processes by which a reader retrieves text information after reading.  More recently, Bob has been conducting research into elementary schools on science instruction.  That research aims to design instructional interventions that are effective at teaching the logic and process of experimentation.  It involves both lab-based and classroom-based research.

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