Statistical Applications for the Behavioral and Social Sciences
2nd Edition
Published on 18. January 2019
Book
Hardback
960 pages
978-1-119-35539-7 (ISBN)
Description
An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS
Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas.
The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory.
This important resource:
Contains information regarding the use of statistical software packages; both Excel and SPSS
Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios
Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes
Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences
Puts renewed emphasis on presentation of data and findings using the APA format
Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use
Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.
Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas.
The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory.
This important resource:
Contains information regarding the use of statistical software packages; both Excel and SPSS
Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios
Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes
Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences
Puts renewed emphasis on presentation of data and findings using the APA format
Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use
Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.
More details
Edition
2nd edition
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 231 mm
Width: 160 mm
Thickness: 46 mm
Weight
1565 gr
ISBN-13
978-1-119-35539-7 (9781119355397)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
K. Paul Nesselroade | Laurence G. Grimm
Essentials of Behavioral and Social Science Statistics
Book
09/2024
3rd Edition
Wiley
€156.18
Shipment within 15-20 days
Previous edition
Laurence G. Grimm
Statistical Applications for the Behavioral Sciences
Book
07/1993
Wiley
€235.00
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Persons
K. PAUL NESSELROADE, JR, PHD, is Professor and Chair of the Psychology Department at Asbury University.
THE LATE LAURENCE G. GRIMM, PHD, was a clinical psychologist and Emeritus Associate Professor, University of Illinois at Chicago.
THE LATE LAURENCE G. GRIMM, PHD, was a clinical psychologist and Emeritus Associate Professor, University of Illinois at Chicago.
Content
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 (R) Excel and SPSS (R)
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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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
BOX 13.1 Do Firearms Create Aggression?
13.5 Assumptions of the Two-Way ANOVA
13.6 Measuring Effect Sizes for a Two-Way ANOVA
13.7 Multiple Comparisons
BOX 13.2 Next Steps with ANOVA
13.8 Interpreting the Factors in a Two-Way ANOVA
13.9 How to Present Formally the Conclusions for a Two-Way ANOVA
Summary
Using Microsoft (R) Excel and SPSS (R) to Run a Two-Way ANOVA
Key Formulas for Chapter 13
Key Terms for Chapter 13
Questions and Exercises for Chapter 13
Chapter 14: Repeated-Measures Analysis of Variance
14.1 The Research Context
14.2 The Logic of the Repeated-Measures ANOVA
14.3 The Formulas for the Repeated-Measures ANOVA
14.4 Using the F Ratio to Test the Null Hypothesis
14.5 Interpreting the Findings
14.6 The ANOVA Summary Table
BOX 14.1 Next Steps for Repeated-Measures ANOVA's: Mixed-Designs and Quasi-Experimentation
14.7 Assumptions of the Repeated-Measures ANOVA
14.8 Measuring Effect Size for Repeated-Measures ANOVA
14.9 Locating the Source(s) of Statistical Evidence
BOX 14.2 The Inverted U Relationship between Arousal and Task Performance
14.10 How to Present Formally the Conclusions for a Repeated-Measures ANOVA
Summary
Using Microsoft (R) Excel and SPSS (R) to Run a Repeated-Measures ANOVA
Key Formulas for Chapter 14
Key Terms for Chapter 14
Questions and Exercises for Chapter 14
PART 4 REVIEW: Analysis of Variance
Review of Concepts Presented in Part 4
Questions and Exercises for Part 4 Review
PART 5: Inferential Statistics: Bivariate Data Analyses
Chapter 15: Linear Correlation
15.1 The Research Context
SPOTLIGHT 15.1 Karl Pearson
15.2 The Correlation Coefficient and Scatter Diagrams
15.3 The Coefficient of Determination
BOX 15.1 Next Steps with Correlations: Scale Development
15.4 Using the Pearson r for Hypothesis Testing
BOX 15.2 Maternal Cognitions and Aggressive Children
15.5 Factors That Can Create Misleading Correlation Coefficients
15.6 How to Present Formally the Conclusions of a Pearson r
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate Pearson r
Key Formulas for Chapter 15
Key Terms for Chapter 15
Questions and Exercises for Chapter 15
Chapter 16: Linear Regression
16.1 The Research Context
16.2 Overview of Regression
16.3 Establishing the Regression Line
SPOTLIGHT 16.1 Sir Francis Galton
16.4 Putting It All Together: A Worked Problem
BOX 16.1 Why is a Prediction Equation Called a Regression Equation?
16.5 The Coefficient of Determination in the Context of Prediction
16.6 The Pitfalls of Linear Regression
BOX 16.2 Next Steps with Regression Analyses
16.7 How to Present Formally the Conclusions of a Linear Regression Analysis
Summary
Using Microsoft (R) Excel and SPSS (R) to Create a Linear Regression Line
Key Formulas for Chapter 16
Key Terms for Chapter 16
Questions and Exercises for Chapter 16
PART 5 REVIEW: Linear Correlation and Linear Regression
Review of Concepts Presented in Part 5
Questions and Exercises for Part 5 Review
PART 6: Inferential Statistics: Nonparametric Tests
Chapter 17: The Chi-Square Test
17.1 The Research Context
17.2 The Chi-Square Test for One-Way Designs: The Goodness-of-Fit Test
17.3 The Chi-Square Distribution and Degrees of Freedom
17.4 Two-Way Designs: The Chi-Square Test for Independence
17.5 The Chi-Square Test for a 2 x 2 Contingency Table
BOX 17.1 What is Beautiful is Good
17.6 A Measure of Effect Size for the Chi-Square Test for Independence
17.7 Which Cells Are Major Contributors to a Significant Chi-Square Test?
17.8 Using the Chi-Square Test with Quantitative Variables
17.9 Assumptions of the Chi-Square Test
17.10 How to Present Formally the Conclusions for a Chi-Square Test
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate a Chi-Square
Key Formulas for Chapter 17
Key Terms for Chapter 17
Questions and Exercises for Chapter 17
Chapter 18: Other Nonparametric Tests
18.1 The Research Context
18.2 The Use of Ranked Data in Research
18.3 The Spearman Rank Correlation Coefficient
18.4 The Point-Biserial Correlation Coefficient
18.5 The Mann-Whitney U Test
18.6 The Wilcoxon Signed-Ranks Test
BOX 18.1 Do Infants Notice the Difference Between Lip Movement and Speech Sounds?
18.7 Using Nonparametric Tests
BOX 18.1 Is the Scientific Method Broken? The Limitations of Science
18.8 How to Present Formally the Conclusions for Various Nonparametric Tests
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate Various Nonparametrics
Key Formulas for Chapter 18
Key Terms for Chapter 18
Questions and Exercises for Chapter 18
PART 6 REVIEW: Nonparametric Tests
Review of Concepts Presented in Part 6
Questions and Exercises for Part 6 Review
Appendixes
A. Statistical Tables
1. z Table
2. t Table
3. Power Table (Finding Power)
4. Power Table (Finding Delta)
5. F Table
6. q Table (Studentized Range)
7. Pearson r Table
8. Spearman rs. Table
9. Chi-Square Table
10. Mann-Whitney U Table
11. Wilcoxon Signed-Ranks Table
B. Answers to Questions and Exercises
C. Basic Data Entry for Microsoft (R) Excel and SPSS (R)
Glossary
References
List of Formulas
List of symbols
Index
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 (R) Excel and SPSS (R)
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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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 (R) Excel and SPSS (R) 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
BOX 13.1 Do Firearms Create Aggression?
13.5 Assumptions of the Two-Way ANOVA
13.6 Measuring Effect Sizes for a Two-Way ANOVA
13.7 Multiple Comparisons
BOX 13.2 Next Steps with ANOVA
13.8 Interpreting the Factors in a Two-Way ANOVA
13.9 How to Present Formally the Conclusions for a Two-Way ANOVA
Summary
Using Microsoft (R) Excel and SPSS (R) to Run a Two-Way ANOVA
Key Formulas for Chapter 13
Key Terms for Chapter 13
Questions and Exercises for Chapter 13
Chapter 14: Repeated-Measures Analysis of Variance
14.1 The Research Context
14.2 The Logic of the Repeated-Measures ANOVA
14.3 The Formulas for the Repeated-Measures ANOVA
14.4 Using the F Ratio to Test the Null Hypothesis
14.5 Interpreting the Findings
14.6 The ANOVA Summary Table
BOX 14.1 Next Steps for Repeated-Measures ANOVA's: Mixed-Designs and Quasi-Experimentation
14.7 Assumptions of the Repeated-Measures ANOVA
14.8 Measuring Effect Size for Repeated-Measures ANOVA
14.9 Locating the Source(s) of Statistical Evidence
BOX 14.2 The Inverted U Relationship between Arousal and Task Performance
14.10 How to Present Formally the Conclusions for a Repeated-Measures ANOVA
Summary
Using Microsoft (R) Excel and SPSS (R) to Run a Repeated-Measures ANOVA
Key Formulas for Chapter 14
Key Terms for Chapter 14
Questions and Exercises for Chapter 14
PART 4 REVIEW: Analysis of Variance
Review of Concepts Presented in Part 4
Questions and Exercises for Part 4 Review
PART 5: Inferential Statistics: Bivariate Data Analyses
Chapter 15: Linear Correlation
15.1 The Research Context
SPOTLIGHT 15.1 Karl Pearson
15.2 The Correlation Coefficient and Scatter Diagrams
15.3 The Coefficient of Determination
BOX 15.1 Next Steps with Correlations: Scale Development
15.4 Using the Pearson r for Hypothesis Testing
BOX 15.2 Maternal Cognitions and Aggressive Children
15.5 Factors That Can Create Misleading Correlation Coefficients
15.6 How to Present Formally the Conclusions of a Pearson r
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate Pearson r
Key Formulas for Chapter 15
Key Terms for Chapter 15
Questions and Exercises for Chapter 15
Chapter 16: Linear Regression
16.1 The Research Context
16.2 Overview of Regression
16.3 Establishing the Regression Line
SPOTLIGHT 16.1 Sir Francis Galton
16.4 Putting It All Together: A Worked Problem
BOX 16.1 Why is a Prediction Equation Called a Regression Equation?
16.5 The Coefficient of Determination in the Context of Prediction
16.6 The Pitfalls of Linear Regression
BOX 16.2 Next Steps with Regression Analyses
16.7 How to Present Formally the Conclusions of a Linear Regression Analysis
Summary
Using Microsoft (R) Excel and SPSS (R) to Create a Linear Regression Line
Key Formulas for Chapter 16
Key Terms for Chapter 16
Questions and Exercises for Chapter 16
PART 5 REVIEW: Linear Correlation and Linear Regression
Review of Concepts Presented in Part 5
Questions and Exercises for Part 5 Review
PART 6: Inferential Statistics: Nonparametric Tests
Chapter 17: The Chi-Square Test
17.1 The Research Context
17.2 The Chi-Square Test for One-Way Designs: The Goodness-of-Fit Test
17.3 The Chi-Square Distribution and Degrees of Freedom
17.4 Two-Way Designs: The Chi-Square Test for Independence
17.5 The Chi-Square Test for a 2 x 2 Contingency Table
BOX 17.1 What is Beautiful is Good
17.6 A Measure of Effect Size for the Chi-Square Test for Independence
17.7 Which Cells Are Major Contributors to a Significant Chi-Square Test?
17.8 Using the Chi-Square Test with Quantitative Variables
17.9 Assumptions of the Chi-Square Test
17.10 How to Present Formally the Conclusions for a Chi-Square Test
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate a Chi-Square
Key Formulas for Chapter 17
Key Terms for Chapter 17
Questions and Exercises for Chapter 17
Chapter 18: Other Nonparametric Tests
18.1 The Research Context
18.2 The Use of Ranked Data in Research
18.3 The Spearman Rank Correlation Coefficient
18.4 The Point-Biserial Correlation Coefficient
18.5 The Mann-Whitney U Test
18.6 The Wilcoxon Signed-Ranks Test
BOX 18.1 Do Infants Notice the Difference Between Lip Movement and Speech Sounds?
18.7 Using Nonparametric Tests
BOX 18.1 Is the Scientific Method Broken? The Limitations of Science
18.8 How to Present Formally the Conclusions for Various Nonparametric Tests
Summary
Using Microsoft (R) Excel and SPSS (R) to Calculate Various Nonparametrics
Key Formulas for Chapter 18
Key Terms for Chapter 18
Questions and Exercises for Chapter 18
PART 6 REVIEW: Nonparametric Tests
Review of Concepts Presented in Part 6
Questions and Exercises for Part 6 Review
Appendixes
A. Statistical Tables
1. z Table
2. t Table
3. Power Table (Finding Power)
4. Power Table (Finding Delta)
5. F Table
6. q Table (Studentized Range)
7. Pearson r Table
8. Spearman rs. Table
9. Chi-Square Table
10. Mann-Whitney U Table
11. Wilcoxon Signed-Ranks Table
B. Answers to Questions and Exercises
C. Basic Data Entry for Microsoft (R) Excel and SPSS (R)
Glossary
References
List of Formulas
List of symbols
Index