Essential Statistics for the Behavioral Sciences - International Student Edition 2/e

Gregory J. Privitera is a professor of psychology at St. Bonaventure University where he is a recipient of its highest teaching honor, The Award for Professional Excellence in Teaching, and its highest honor for scholarship, The Award for Professional Excellence in Research and Publication. Dr. Privitera received his PhD in behavioral neuroscience in the field of psychology at the State University of New York at Buffalo and continued with his postdoctoral research at Arizona State University. He is a nationally award-winning author and research scholar. His textbooks span across diverse topics in psychology and the behavioral sciences, including an introductory psychology text, four statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and analytics. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health and informing policy in health care. His research has earned recognition by the American Psychological Association, and in media to include Oprah's Magazine, Time Magazine, and the Wall Street Journal. He mentors a variety of undergraduate research projects at St. Bonaventure University, where dozens of students, many of whom have gone on to earn graduate and doctoral degrees at various institutions, have coauthored and presented research work. In addition to his teaching, research, and advisement, Dr. Privitera is a veteran of the U.S. Marine Corps, is an identical twin, and is married with two daughters, Grace Ann and Charlotte Jane, and two sons, Aiden Andrew and Luca James.

PART I: Introduction and Descriptive Statistics Chapter 1:Introduction to Statistics 1.1 The Use of Statistics in Science 1.2 Descriptive and Inferential Statistics MAKING SENSE-Populations and Samples 1.3 Research Methods and Statistics MAKING SENSE-Experimental and Control Groups 1.4 Scales of Measurement 1.5 Types of Variables for Which Data Are Measured 1.6 Research in Focus: Evaluating Data and Scales of Measurement 1.7 SPSS in Focus: Entering and Defining Variables Chapter 2: Summarizing Data: Frequency Distributions in Tables and Graphs 2.1 Why Summarize Data? 2.2 Frequency Distributions for Grouped Data 2.3 Identifying Percentile Points and Percentile Ranks 2.4 SPSS in Focus: Frequency Distributions for Quantitative Data 2.5 Frequency Distributions for Ungrouped Data 2.6 Research in Focus: Summarizing Demographic Information 2.7 SPSS in Focus: Frequency Distributions for Categorical Data 2.8 Graphing Distributions: Continuous Data 2.9 Graphing Distributions: Discrete and Categorical Data MAKING SENSE- Deception Due to the Distortion of Data 2.10 Research in Focus: Frequencies and Percents 2.11 SPSS in Focus: Histograms, Bar Charts, and Pie Charts Chapter 3: Summarizing Data: Central Tendency 3.1 Introduction to Central Tendency 3.2 Measures of Central Tendency MAKING SENSE-Making the Grade 3.3 Characteristics of the Mean 3.4 Choosing an Appropriate Measure of Central Tendency 3.5 Research in Focus: Describing Central Tendency 3.6 SPSS in Focus: Mean, Median, and Mode Chapter 4: Summarizing Data: Variability 4.1 Measuring Variability 4.2 The Range and Interquartile Range 4.3 Research in Focus: Reporting the Range 4.4 The Variance 4.5 Explaining Variance for Populations and Samples 4.6 The Computational Formula for Variance 4.7 The Standard Deviation 4.8 What Does the Standard Deviation Tell Us? MAKING SENSE-Standard Deviation and Nonnormal Distributions 4.9 Characteristics of the Standard Deviation 4.10 SPSS in Focus: Range, Variance, and Standard Deviation PART II: Probability and the Foundations of Inferential Statistics Chapter 5: Probability, Normal Distributions, and z Scores 5.1 Introduction to Probability 5.2 Calculating Probability 5.3 Probability and the Normal Distribution 5.4 Characteristics of the Normal Distribution 5.5 Research in Focus: The Statistical Norm 5.6 The Standard Normal Distribution and z Scores 5.7 A Brief Introduction to the Unit Normal Table 5.8 Locating Proportions 5.9 Locating Scores MAKING SENSE-Standard Deviation and the Normal Distribution 5.10 SPSS in Focus: Converting Raw Scores to Standard z Scores Chapter 6: Characteristics of the Sample Mean 6.1 Selecting Samples From Populations 6.2 Selecting a Sample: Who's In and Who's Out? 6.3 Sampling Distributions: The Mean 6.4 The Standard Error of the Mean 6.5 Factors That Decrease Standard Error 6.6 SPSS in Focus: Estimating the Standard Error of the Mean 6.7 APA in Focus: Reporting the Standard Error 6.8 Standard Normal Transformations With Sampling Distributions Chapter 7: Hypothesis Testing: Significance, Effect Size, and Power 7.1 Inferential Statistics and Hypothesis Testing 7.2 Four Steps to Hypothesis Testing MAKING SENSE-Testing the Null Hypothesis 7.3 Hypothesis Testing and Sampling Distributions 7.4 Making a Decision: Types of Error 7.5 Testing for Significance: Examples Using the z Test 7.6 Research in Focus: Directional Versus Nondirectional Tests 7.7 Measuring the Size of an Effect: Cohen's d 7.8 Effect Size, Power, and Sample Size 7.9 Additional Factors That Increase Power 7.10 SPSS in Focus: A Preview for Chapters 8 to 14 7.11 APA in Focus: Reporting the Test Statistic and Effect Size PART III: Making Inferences About One or Two Means Chapter 8: Testing Means: One-Sample t Test With Confidence Intervals 8.1 Going From z to t 8.2 The Degrees of Freedom 8.3 Reading the t Table 8.4 Computing the One-Sample t Test 8.5 Effect Size for the One- Sample t Test 8.6 Confidence Intervals for the One-Sample t Test 8.7 Inferring Significance and Effect Size From a Confidence Interval 8.8 SPSS in Focus: One-Sample t Test and Confidence Intervals 8.9 APA in Focus: Reporting the t Statistic and Confidence Intervals Chapter 9: Testing Means: Two-Independent-Sample t Test With Confidence Intervals 9.1 Introduction to the Between- Subjects Design 9.2 Selecting Samples for Comparing Two Groups 9.3 Variability and Comparing Differences Between Two Groups 9.4 Computing the Two-Independent-Sample t Test MAKING SENSE-The Pooled Sample Variance 9.5 Effect Size for the Two-Independent-Sample t Test 9.6 Confidence Intervals for the Two-Independent-Sample t Test 9.7 Inferring Significance and Effect Size From a Confidence Interval 9.8 SPSS in Focus: Two-Independent- Sample t Test and Confidence Intervals 9.9 APA in Focus: Reporting the t Statistic and Confidence Intervals Chapter 10: Testing Means: Related-Samples t Test With Confidence Intervals 10.1 Related Samples Designs 10.2 Introduction to the Related-Samples t Test 10.3 Computing the Related-Samples t Test MAKING SENSE-Increasing Power by Reducing Error 10.4 Measuring Effect Size for the Related-Samples t Test 10.5 Confidence Intervals for the Related-Samples t Test 10.6 Inferring Significance and Effect Size From a Confidence Interval 10.7 SPSS in Focus: Related-Samples t Test and Confidence Intervals 10.8 APA in Focus: Reporting the t Statistic and Confidence Intervals PART IV: Making Inferences About The Variability of Two or More Means Chapter 11: One-Way Analysis of Variance: Between-Subjects and Within-Subjects (Repeated-Measures) Designs 11.1 An Introduction to Analysis of Variance 11.2 The Between-Subjects Design for Analysis of Variance 11.3 Computing the One-Way Between-Subjects ANOVA MAKING SENSE-Mean Squares and Variance 11.4 Post Hoc Tests: An Example Using Tukey's HSD 11.5 SPSS in Focus: The One-Way Between-Subjects ANOVA 11.6 The Within-Subjects Design for Analysis of Variance 11.7 Computing the One-Way Within-Subjects ANOVA 11.8 Post Hoc Tests for the Within-Subjects Design 11.9 SPSS in Focus: The One-Way Within-Subjects ANOVA 11.10 A Comparison of Within-Subjects and Between-Subjects Designs for ANOVA: Implications for Power 11.11 APA in Focus: Reporting the Results of the One-Way ANOVAs 327 Chapter Summary Organized by Learning Objective Chapter 12: Two-Way Analysis of Variance: Between- Subjects Factorial Design 12.1 Introduction to Factorial Designs 12.2 Structure and Notation for the Two-Way ANOVA 12.3 Describing Variability: Main Effects and Interactions MAKING SENSE-Graphing Interactions 12.4 Computing the Two-Way Between-Subjects ANOVA 12.5 Analyzing Main Effects and Interactions 12.6 Measuring Effect Size for Main Effects and the Interaction 12.7 SPSS in Focus: The Two-Way Between-Subjects ANOVA 12.8 APA in Focus: Reporting the Results of the Two-Way ANOVAs PART V: Making Inferences About Patterns, Prediction, and Nonparametric Tests Chapter 13: Correlation and Linear Regression 13.1 The Structure of Data Used for Identifying Patterns and Making Predictions 13.2 Fundamentals of the Correlation 13.3 The Pearson Correlation Coefficient MAKING SENSE-Understanding Covariance 13.4 SPSS in Focus: Pearson Correlation Coefficient 13.5 Assumptions and Limitations for Linear Correlations 13.6 Alternatives to Pearson: Spearman, Point-Biserial, and Phi 13.7 SPSS in Focus: Computing the Alternatives to Pearson 13.8 Fundamentals of Linear Regression 13.9 Using the Method of Least Squares to Find the Regression Line MAKING SENSE-SP, SS, and the Slope of a Regression Line 13.10 Using Analysis of Regression to Determine Significance 13.11 SPSS in Focus: Analysis of Regression 13.12 A Look Ahead to Multiple Regression 13.13 APA in Focus: Reporting Correlations and Linear Regression Chapter 14: Chi-Square Tests: Goodness-of-Fit and the Test for Independence 14.1 Distinguishing Parametric and Nonparametric Tests 14.2 The Chi-Square Goodness-of-Fit Test MAKING SENSE-The Relative Size of a Discrepancy 14.3 SPSS in Focus: The Chi-Square Goodness-of-Fit Test 14.4 Interpreting the Chi-Square Goodness-of-Fit Test 14.5 The Chi-Square Test for Independence 14.6 Measures of Effect Size for the Chi-Square Test for Independence 14.7 SPSS in Focus: The Chi-Square Test for Independence 14.8 APA in Focus: Reporting the Chi-Square Tests