This book is organized into 3 general categories: the research process and how data that has been collected can be organized, presented and summarized (chapters 1 - 4); the process of conducting statistical analyses to test research questions, and hypotheses and issues and controversies regarding the process (chapters 5 - 10) and the last section which examines statistical procedures used in research situations that vary in the number of independent variables in the study, as well as how the independent and dependent variables have been measured (chapters 11 - 14). Every chapter contains learning checks which include review questions, and exercises are included at the end of every chapter to help assess what was learned.
Howard T. 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 PhD 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 coauthor (with G. Keppel) of Introduction to Design and Analysis: A Student's Handbook.
Chapter 1: Introduction to Statistics 1.1 What Is Statistics? 1.2 Why Learn Statistics? 1.3 Introduction to the Stages of the Research Process 1.4 Plan of the Book Chapter 2: Examining Data: Tables and Figures 2.1 An Example From the Research: Winning the Lottery 2.2 Why Examine Data? 2.3 Examining Data Using Tables 2.4 Grouped Frequency Distribution Tables 2.5 Examining Data Using Figures 2.6 Examining Data: Describing Distributions Chapter 3: Measures of Central Tendency 3.1 An Example From the Research: The 10% Myth 3.2 Understanding Central Tendency 3.3 The Mode 3.4 The Median 3.5 The Mean 3.6 Comparison of the Mode, Median, and Mean 3.7 Measures of Central Tendency: Drawing Conclusions Chapter 4: Measures of Variability 4.1 An Example From the Research: How Many "Sometimes" in an "Always"? 4.2 Understanding Variability 4.3 The Range 4.4 The Interquartile Range 4.5 The Variance (s2) 4.6 The Standard Deviation (s) 4.7 Measures of Variability for Populations 4.8 Measures of Variability: Drawing Conclusions Chapter 5: Normal Distributions 5.1 Example: SAT Scores 5.2 Normal Distributions 5.3 The Standard Normal Distribution 5.4 Applying z-Scores to Normal Distributions 5.5 Standardizing Frequency Distributions Chapter 6: Probability and Introduction to Hypothesis Testing 6.1 A Brief Introduction to Probability 6.2 Example: Making Heads or Tails of the Super Bowl 6.3 Introduction to Hypothesis Testing 6.4 Issues Related to Hypothesis Testing: An Introduction Chapter 7: Testing One Sample Mean 7.1 An Example From the Research: Do You Read Me? 7.2 The Sampling Distribution of the Mean 7.3 Inferential Statistics: Testing One Sample Mean (s Known) 7.4 A Second Example From the Research: Unique Invulnerability 7.5 Introduction to the t-Distribution 7.6 Inferential Statistics: Testing One Sample Mean (s Not Known) 7.7 Factors Affecting the Decision About the Null Hypothesis Chapter 8: Estimating the Mean of a Population 8.1 An Example From the Research: Salary Survey 8.2 Introduction to the Confidence Interval for the Mean 8.3 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 Mean 8.6 Interval Estimation and Hypothesis Testing Chapter 9: Testing the Difference Between Two Means 9.1 An Example From the Research: You Can Just Wait 9.2 The Sampling Distribution of the Difference 9.3 Inferential Statistics: Testing the Difference Between Two Sample Means 9.4 Inferential Statistics: Testing the Difference Between Two Sample Means (Unequal Sample Sizes) 9.5 Inferential Statistics: Testing the Difference Between Paired Means Chapter 10: Errors in Hypothesis Testing, Statistical Power, and Effect Size 10.1 Hypothesis Testing vs. Criminal Trials 10.2 An Example From the Research: Truth or Consequences 10.3 Two Errors in Hypothesis Testing: Type I and Type II Error 10.4 Controlling Type I and Type II Error 10.5 Measures of Effect Size Chapter 11: One-Way Analysis of Variance (ANOVA) 11.1 An Example From the Research: It's Your Move 11.2 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 Revisited 11.5 Analytical Comparisons Within the One-Way ANOVA Chapter 12: Two-Way Analysis of Variance (ANOVA) 12.1 An Example From the Research: Vote-or Else! 12.2 Introduction to Factorial Research Designs 12.3 The Two-Factor (A x B) Research Design 12.4 Introduction to Analysis of Variance (ANOVA) for the Two-Factor Research Design 12.5 Inferential Statistics: Two-Way Analysis of Variance (ANOVA) 12.6 Investigating a Significant A x B Interaction Effect: Analysis of Simple Effects ********************************************************************************************************* Chapter 13: Correlation and Linear Regression 13.1 An Example From the Research: Snap Judgment 13.2 Introduction to the Concept of Correlation 13.3 Inferential Statistics: Pearson Correlation Coefficient 13.4 Predicting One Variable From Another: Linear Regression 13.5 Correlating Two Sets of Ranks: The Spearman Rank-Order Correlation 13.6 Correlational Statistics vs. Correlational Research Chapter 14: Chi-Square 14.1 An Example From the Research (One Categorical Variable): Are You My Type? 14.2 Introduction to the Chi-Square Statistic 14.3 Inferential Statistic: Chi-Square Goodness-of-Fit Test 14.4 An Example From the Research (Two Categorical Variables): Seeing Red 14.5 Inferential Statistic: Chi-Square Test of Independence 14.6 Parametric and Nonparametric Statistical Tests