Statistical Applications for the Behavioral and Social Sciences, 2nd Edition

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thumbnail image: Statistical Applications for the Behavioral and Social Sciences, 2nd Edition

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. 

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

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® Excel and SPSS® 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® Excel and SPSS® 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® Excel and SPSS® 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® Excel and SPSS® 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 × 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® Excel and SPSS® 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® Excel and SPSS® 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® Excel and SPSS®

Glossary

References

List of Formulas

List of symbols

Index

Related Topics

Related Publications

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