Key Features

• Over 45 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 1–12) and Part Two: Bivariate Procedures (Chapters 13 and 14). 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.
• The core inferential procedures of hypothesis testing, confidence intervals, effect size estimation and power are returned to repeatedly across different domains (e.g., normal distribution, t-distribution, Chi-square, correlation and regression) to emphasize the common logic of each procedure across the domains
• Chi-Square is presented in Chapter 12 instead of at the end of the book to keep it grouped with univariate procedures.
• 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 10 and 11). 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 chapter 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 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 Questions 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 Questions 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.