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Dedication Preface Acknowledgements About the companion Website INTRODUCTION: BASIC CONCEPTS IN RESEARCH
Chapter 1: Basic Concepts in Research 1.1 The Scientific Method
1.2 The Goals of the Researcher
1.3 Types of Variables
1.4 Controlling Extraneous Variables BOX
1.1: Is the Scientific Method Broken? The Wallpaper Effect
1.5 Validity Issues BOX
1.2: Feeling Good and Helping Others: A Study With a Confound
1.6 Causality and Correlation
1.7 The Role of Statistics and the Organization of the Textbook BOX
1.3: A Strategy for Studying Statistics: Distributed Over Mass Practice Summary Key Terms for
Chapter 1 Questions and Exercises for
Chapter 1
PART 1: DESCRIPTIVE STATISTICS
Chapter 2: Scales of Measurement and Data Display
2.1 Scales of Measurement SPOTLIGHT
2.1 Rensis Likert
2.2 Discrete Variables, Continuous Variables, and the Real Limits of Numbers
2.3 Using Tables to Organize Data BOX
2.1 Some Notes on the History of Statistics
2.4 Using Graphs to Display Data BOX
2.2 Using a Graph to Provide a Visual Display of Data BOX
2.3 Is the Scientific Method Broken? The Misrepresentation of Data/Findings
2.5 The Shape of Things to Come Summary Introduction to Microsoft® Excel and SPSS® Key Terms for
Chapter 2 Question and Exercises for
Chapter 2
Chapter 3: Measures of Central Tendency
3.1 Describing a Distribution of Scores
3.2 Parameters and Statistics
3.3 The Rounding Rule
3.4 The Mean
3.5 The Median BOX
3.1: The Central Tendency of Likert Scales: The Great Debate
3.6 The Mode
3.7 How the Shape of Distributions Affects Measures of Central Tendency
3.8 When to Use the Mean, Median, and Mode
3.9 Experimental Research and the Mean: A Glimpse of Things to Come BOX
3.2 Learning to Control Our Heart Rate Summary Using Microsoft® Excel and SPSS® to find measures of centrality Key Formulas for
Chapter 3 Key Terms for
Chapter 3 Questions and Exercises for
Chapter 3
Chapter 4: Measures of Variability
4.1 The Importance of Measures of Variability
4.2 Range
4.3 Mean Deviation
4.4 The Variance BOX
4.1 The Substantive Importance of the Variance
4.5 The Standard Deviation BOX
4.2 The Origins of the Standard Deviation
4.6 Simple Transformations and Their Effect on the Mean and Variance
4.7 Deciding Which Measure of Variability to Use BOX
4.3 Is the Scientific Method Broken? Demand Characteristics and Shrinking Variation Summary Using Microsoft® Excel and SPSS® to Find Measures of Variability Key Formulas for
Chapter 4 Key Terms for
Chapter 4 Questions and Exercises for
Chapter 4
Chapter 5: The Normal Curve and Transformations: Percentiles, z Scores and T Scores
5.1 Percentile Rank
5.2 The Normal Distributions SPOTLIGHT
5.1 Abraham De Moivre
5.3 Standardized Scores (z Scores) BOX
5.1 With z Scores We Can Compare Apples and Oranges Summary Using Microsoft® Excel and SPSS® to Find z Scores Key Formulas for
Chapter 5 Key Terms for
Chapter 5 Questions and Exercises for
Chapter 5
PART 2: Inferential Statistics: Theoretical Basis
Chapter 6: Basic Concepts of Probability
6.1 Theoretical Support for Inferential Statistics
6.2 The Taming of Chance
6.3 What is Probability? BOX
6.1 Is the Scientific Method Broken? Uncertainty, Likelihood, and Clarity
6.4 Sampling with and without Replacement
6.5 A Priori and A Posteriori Approaches to Probability
6.6 The Addition Rule
6.7 The Multiplication Rule
6.8 Conditional Probabilities
6.9 Bayes Theorem SPOTLIGHT
6.1 Thomas Bayes and Bayesianism Summary Key Formulas for
Chapter 6 Key Terms for
Chapter 6 Questions and Exercises for
Chapter 6
Chapter 7: Hypothesis Testing and Sampling Distributions
7.1 Inferential Statistics
7.2 Hypothesis Testing
7.3 Sampling Distributions BOX
7.1 Playing with the Numbers: Create Our Own Sampling Distribution
7.4 Estimating the Features of Sampling Distributions BOX
7.2 Is the Scientific Method Broken? The Value of Replication Summary Key Formulas for
Chapter 7 Key Terms for
Chapter 7 Questions and Exercises for
Chapter 7
PART 3: Inferential Statistics: z Test, t Tests, and Power Analysis
Chapter 8: Testing a Single Mean: The Single-Sample z and t Tests
8.1 The Research Context
8.2 Using the Sampling Distribution of Means for the Single-Sample z Test
8.3 Type I and Type II Errors BOX
8.1 Is the Scientific Method Broken? Type I Errors and the Ioannidis Critique
8.4 Is a Significant Finding "Significant"?
8.5 The Statistical Test for the Mean of a Population When Sigma is unknown: The t Distributions BOX
8.2 Visual Illusions and Immaculate Perception
8.6 Assumptions of the Single-Sample z and t Test
8.7 Interval Estimation of the Population Mean
8.8 How to Present Formally the Conclusions for a Single-Sample t Test Summary Using Microsoft® Excel and SPSS® to Run Single-Sample t Tests Key Formulas for
Chapter 8 Key Terms for
Chapter 8 Questions and Exercises for
Chapter 8
Chapter 9: Testing the Difference between Two Means: The Independent-Samples t Test
9.1 The Research Context SPOTLIGHT
9.1 William Gosset
9.2 The Independent-Sample t Test BOX
9.1 Can Epileptic Seizures Be Controlled By Relaxation Training?
9.3 The Appropriateness of Unidirectional Tests
9.4 Assumptions of the Independent-Samples t Test
9.5 Interval Estimation of the Population Mean Difference
9.6 How to Present Formally the Conclusions for an Independent-Samples t Test Summary Using Microsoft® Excel and SPSS® to run an Independent-Samples t Test Key Formulas for
Chapter 9 Key Terms for
Chapter 9 Questions and Exercises for
Chapter 9
Chapter 10: Testing the Difference Between Two Means: The Dependent-samples t Test
10.1 The Research Context
10.2 The Sampling Distribution for the Dependent-Samples t Test
10.3 The t Distribution for Dependent Samples
10.4 Comparing the Independent- and Dependent-Samples t Tests
10.5 The One-Tailed t Test Revisited BOX
10.1 Is the Scientific Method Broken? The Questionable Use of One-Tailed t Tests
10.6 Assumptions of the Dependent-Samples t Test BOX
10.2 The First Application of the t Test
10.7 Interval Estimation of the Population Mean Difference
10.8 How to Present Formally the Conclusions for a Dependent-Samples t Test Summary Using Microsoft® Excel and SPSS® to Run a Dependent-Samples t Test Key Formulas for
Chapter 10 Key Terms for
Chapter 10 Questions and Exercises for
Chapter 10
Chapter 11: Power Analysis and Hypothesis Testing
11.1 Decision Making While Hypothesis Testing
11.2 Why Study Power?
11.3 The Five Factors that Influence Power
11.4 Decision Criteria that Influence Power
11.5 Using the Power Table
11.6 Determining Effect Size: The Achilles Heel of the Power Analysis BOX
11.1 Is the Scientific Method Broken? The Need to Take Our Own Advice
11.7 Determining Sample Size for a Single-Sample Test
11.8 Failing to Reject the Null Hypothesis: Can a Power Analysis Help? BOX
11.2 Psychopathy and Frontal Lobe Damage Summary Key Formulas for
Chapter 11 Key Term for
Chapter 11 Questions and Exercises for
Chapter 11
PART 3 REVIEW: The z Test, t Tests, and Power Analysis Review of Concepts Presented in
Part 3 Questions and Exercises for
Part 3 Review
PART 4: Inferential Statistics: Analysis of Variance
Chapter 12: One-Way Analysis of Variance
12.1 The Research Context SPOTLIGHT
12.1 Sir Ronald Fisher
12.2 The Conceptual Basis of ANOVA: Sources of Variation
12.3 The Assumptions of the one-way ANOVA
12.4 The Conceptual Basis of ANOVA: Hypotheses and Error Terms
12.5 Computing the F Ratio in ANOVA
12.6 Testing Null Hypotheses
12.7 The ANOVA Summary Table
12.8 An Example of ANOVA with Unequal Numbers of Participants
12.9 Measuring Effect Size for a One-Way ANOVA
12.10 Locating the Source(s) of Significance SPOTLIGHT
12.2 John Wilder Tukey BOX
12.1 Initiation Rites and Club Loyalty
12.11 How to Present Formally the Conclusions for a One-Way ANOVA Summary Using Microsoft® Excel and SPSS® to Run a One-Way ANOVA Key Formulas for
Chapter 12 Key Terms for
Chapter 12 Questions and Exercises for
Chapter 12
Chapter 13: Two-Way Analysis of Variance
13.1 The Research Context
13.2 The Logic of the Two-Way ANOVA
13.3 Definitional and Computational Formulas for the Two-Way ANOVA
13.4 Using the F Ratios to Test Null Hypotheses