Fundamental Statistics for the Social and Behavioral Sciences

Howard Tokunaga is Professor of Psychology at San Jose State University, where he serves as Coordinator of the MS Program in Industrial/Organizational (I/O) Psychology and teaches undergraduate and graduate courses in statistics, research methods, and I/O psychology.  He received his bachelor’s degree in psychology at UC Santa Cruz and his Ph.D. in psychology at UC Berkeley. In addition to his teaching, he has consulted with a number of public sector and private sector organizations on a wide variety of management and human resource issues.  He is author of Moving from IBM SPSS to R and RStudio:  A Statistics Companion, and co-author (with G. Keppel) of Introduction to Design and Analysis:  A Student’s Handbook.  In his spare time, he enjoys the outdoors, museums, live theater, and being physically, socially, and politically active as possible.

• PrefaceAcknowledgementsAbout The AuthorsChapter 1: Introduction to Statistics1.1 What Is Statistics?1.2 Why Learn Statistics?1.3 Introduction to the Stages of the Research Process1.4 Plan of the Book1.5 Looking Ahead1.6 Summary1.7 Important Terms1.8 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 2: Examining Data: Tables and Figures2.1 An Example From the Research: Winning the Lottery2.2 Why Examine Data?2.3 Examining Data Using Tables2.4 Grouped Frequency Distribution Tables2.5 Examining Data Using Figures2.6 Examining Data: Describing Distributions2.7 Looking Ahead2.8 Summary2.9 Important Terms2.10 Formula Introduced in This Chapter2.11 Using IBM SPSS Software2.12 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 3: Measures of Central Tendency3.1 An Example From the Research: The 10% Myth3.2 Understanding Central Tendency3.3 The Mode3.4 The Median3.5 The Mean3.6 Comparison of the Mode, Median, and Mean3.7 Measures of Central Tendency: Drawing Conclusions3.8 Looking Ahead3.9 Summary3.10 Important Terms3.11 Formulas Introduced in This Chapter3.12 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 4: Measures of Variability4.1 An Example From the Research: How Many “Sometimes” in an “Always”?4.2 Understanding Variability4.3 The Range4.4 The Interquartile Range4.5 The Variance (s2)4.6 The Standard Deviation (s)4.7 Measures of Variability for Populations4.8 Measures of Variability for Nominal Variables4.9 Measures of Variability: Drawing Conclusions4.10 Looking Ahead4.11 Summary4.12 Important Terms4.13 Formulas Introduced in This Chapter4.14 Using SPSS4.15 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 5: Normal Distributions5.1 Example: SAT Scores5.2 Normal Distributions5.3 The Standard Normal Distribution5.4 Applying z-Scores to Normal Distributions5.5 Standardizing Frequency Distributions5.6 Looking Ahead5.7 Summary5.8 Important Terms5.9 Formulas Introduced in This Chapter5.10 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 6: Probability and Introduction to Hypothesis Testing6.1 A Brief Introduction to Probability6.2 Example: Making Heads or Tails of the Super Bowl6.3 Introduction to Hypothesis Testing6.4 Issues Related to Hypothesis Testing: An Introduction6.5 Looking Ahead6.6 Summary6.7 Important Terms6.8 Formula Introduced in This Chapter6.9 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 7: Testing One Sample Mean7.1 An Example From the Research: Do You Read Me?7.2 The Sampling Distribution of the Mean7.3 Inferential Statistics: Testing One Sample Mean (s Known)7.4 A Second Example From the Research: Unique Invulnerability7.5 Introduction to the t-Distribution7.6 Inferential Statistics: Testing One Sample Mean (s Not Known)7.7 Factors Affecting the Decision About the Null Hypothesis7.8 Looking Ahead7.9 Summary7.10 Important Terms7.11 Formulas Introduced in This Chapter7.12 Using SPSS7.13 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 8: Estimating the Mean of a Population8.1 An Example From the Research: Salary Survey8.2 Introduction to the Confidence Interval for the Mean8.3 The Confidence Interval for the Mean (s Not Known)8.4 The Confidence Interval for the Mean (s Known)8.5 Factors Affecting the Width of the Confidence Interval for the Mean8.6 Interval Estimation and Hypothesis Testing8.7 Looking Ahead8.8 Summary8.9 Important Terms8.10 Formulas Introduced in This Chapter8.11 Using SPSS8.12 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 9: Testing the Difference Between Two Means9.1 An Example From the Research: You Can Just Wait9.2 The Sampling Distribution of the Difference9.3 Inferential Statistics: Testing the Difference Between Two Sample Means9.4 Inferential Statistics: Testing the Difference Between Two Sample Means (Unequal Sample Sizes)9.5 Inferential Statistics: Testing the Difference Between Paired Means9.6 Looking Ahead9.7 Summary9.8 Important Terms9.9 Formulas Introduced in This Chapter9.10 Using SPSS9.11 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 10: Errors in Hypothesis Testing, Statistical Power, and Effect Size10.1 Hypothesis Testing vs. Criminal Trials10.2 An Example From the Research: Truth or Consequences10.3 Two Errors in Hypothesis Testing: Type I and Type II Error10.4 Controlling Type I and Type II Error10.5 Measures of Effect Size10.6 Looking Ahead10.7 Summary10.8 Important Terms10.9 Formulas Introduced in This Chapter10.10 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 11: One-Way Analysis of Variance (ANOVA)11.1 An Example From the Research: It’s Your Move11.2 Introduction to Analysis of Variance (ANOVA)11.3 Inferential Statistics: One-Way Analysis of Variance (ANOVA)11.4 A Second Example: The Parking Lot Study Revisited11.5 Analytical Comparisons Within the One-Way ANOVA11.6 Looking Ahead11.7 Summary11.8 Important Terms11.9 Formulas Introduced in This Chapter11.10 Using SPSS11.11 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 12: Two-Way Analysis of Variance (ANOVA)12.1 An Example From the Research: Vote—or Else!12.2 Introduction to Factorial Research Designs12.3 The Two-Factor (A × B) Research Design12.4 Introduction to Analysis of Variance (ANOVA) for the Two-Factor Research Design12.5 Inferential Statistics: Two-Way Analysis of Variance (ANOVA)12.6 Investigating a Significant A × B Interaction Effect: Analysis of Simple Effects12.7 Looking Ahead12.8 Summary12.9 Important Terms12.10 Formulas Introduced in This Chapter12.11 Using SPSS12.12 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 13: Correlation13.1 An Example From the Research: It’s Good for You!13.2 Introduction to the Concept of Correlation13.3 Inferential Statistics: Pearson Correlation Coefficient13.4 Correlating Two Sets of Ranks: The Spearman Rank-Order Correlation13.5 Correlational Statistics vs. Correlational Research13.6 Looking Ahead13.7 Summary13.8 Important Terms13.9 Formulas Introduced in This Chapter13.10 Using SPSS13.11 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 14: Linear Regression and Multiple Correlation14.1 Predicting One Variable From Another: Linear Regression14.2 Correlation With Two or More Predictors: Introduction to Multiple Correlation and Regression14.3 Looking Ahead14.4 Summary14.5 Important Terms14.6 Formulas Introduced in This Chapter14.7 Using SPSS14.8 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesChapter 15: Chi-Square15.1 An Example From the Research (One Categorical Variable): Are You My Type?15.2 Introduction to the Chi-Square Statistic15.3 Inferential Statistic: Chi-Square Goodness-of-Fit Test15.4 An Example From the Research (Two Categorical Variables): Seeing Red15.5 Inferential Statistic: Chi-Square Test of Independence15.6 Parametric and Nonparametric Statistical Tests15.7 Looking Ahead15.8 Summary15.9 Important Terms15.10 Formulas Introduced in This Chapter15.11 Using SPSS15.12 ExercisesAnswers to Learning ChecksAnswers to Odd-Numbered ExercisesTablesAppendixGlossaryReferences

Fundamental Statistics for the Social and Behavioral Sciences is an excellent choice for an undergraduate-level first statistics course. It provides fairly large breadth while also providing just enough depth for students to understand the purpose of statistical methods in different types of research scenarios. It does a particularly good job of including only the most necessary computational details and avoids getting bogged down by technical jargon and equations that may turn social science students away from methodology.