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Statistics for the Behavioral Sciences
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Statistics for the Behavioral Sciences

Fourth Edition
September 2023 | 960 pages | SAGE Publications, Inc
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Recipient of the 2024 Textbook & Academic Authors Association (TAA) Textbook Excellence Award
This award recognizes excellence in current textbooks and learning materials.

Statistics for the Behavioral Sciences by award-winning author Gregory Privitera aims to not only inspire students to use statistics properly to better understand the world around them, but also to develop the skills to be lab-ready in applied research settings. Incorporating examples from current, relatable research throughout the text, Privitera shows students that statistics can be relevant, interesting, and accessible. Robust pedagogy encourages students to continually check their comprehension and hone their skills by working through problem sets throughout the text, including exercises that seamlessly integrate SPSS. This new Fourth Edition gives students a greater awareness of the best practices of analysis in the behavioral sciences, with a focus on transparency in recording, managing, analyzing, and interpreting data.

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PART I. INTRODUCTION AND DESCRIPTIVE STATISTICS
 
1. Introduction to Statistics
1.1 The Use of Statistics in Science

 
1.2 Descriptive and Inferential Statistics

 
1.3 Research Methods and Statistics

 
1.4 Scales of Measurement

 
1.5 Types of Variables for Which Data Are Measured

 
1.6 SPSS in Focus: Entering and Defining Variables

 
 
2. Summarizing Data: Frequency Distributions in Tables and Graphs
2.1 Why Summarize Data?

 
2.2 Simple Frequency Distributions for Grouped Data

 
2.3 Other Ways of Summarizing Grouped Data in Frequency Distributions

 
2.4 Identifying Percentile Points and Percentile Ranks

 
2.5 SPSS in Focus: Frequency Distributions for Quantitative Data

 
2.6 Frequency Distributions for Ungrouped Data

 
2.7 SPSS in Focus: Frequency Distributions for Categorical Data

 
2.8 Pictorial Frequency Distributions

 
2.9 Graphing Distributions: Continuous Data

 
2.10 Stem-and-Leaf Displays

 
2.11 Graphing Distributions: Discrete and Categorical Data

 
2.12 SPSS in Focus: Histograms, Bar Charts, Pie Charts, and Stem-and-Leaf Displays

 
 
3. Summarizing Data: Central Tendency
3.1 Introduction to Central Tendency

 
3.2 Measures of Central Tendency: The Mean

 
3.3 Measures of Central Tendency: The Weighted Mean

 
3.4 Measures of Central Tendency: The Median and the Mode

 
3.5 Characteristics of the Mean

 
3.6 Choosing an Appropriate Measure of Central Tendency

 
3.7 SPSS in Focus: Mean, Median, and Mode

 
 
4. Summarizing Data: Variability
4.1 Introduction to Variability

 
4.2 The Range

 
4.3 Quartiles and Interquartiles

 
4.4 The Variance

 
4.5 The Computational Formula for Variance

 
4.6 Explaining Variance for Populations and Samples

 
4.7 The Standard Deviation

 
4.8 The Informativeness of Standard Deviation

 
4.9 SPSS in Focus: Range, Quartiles, Variance, and Standard Deviation

 
 
PART II. PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS
 
5. Probability
5.1 Introduction to Probability

 
5.2 Probability and Relative Frequency

 
5.3 The Relationship Between Multiple Outcomes

 
5.4 Conditional Probabilities and Bayes’s Theorem

 
5.5 SPSS in Focus: Probability Tables

 
5.6 Probability Distributions

 
5.7 The Mean of a Probability Distribution and Expected Value

 
5.8 The Variance and Standard Deviation of a Probability Distribution

 
5.9 Expected Value and the Binomial Distribution

 
 
6. Probability, Normal Distributions, and z Scores
6.1 Characteristics of the Normal Distribution

 
6.2 The Standard Normal Distribution and the z Transformation

 
6.3 The Unit Normal Table: A Brief Introduction

 
6.4 Locating Proportions

 
6.5 Locating Scores

 
6.6 SPSS in Focus: Converting Raw Scores to Standard z Scores

 
6.7 The Normal Approximation to the Binomial Distribution

 
 
7. Probability and Sampling Distributions
7.1 Selecting Samples From Populations

 
7.2 Selecting a Sample: Who’s In and Who’s Out?

 
7.3 Sampling Distributions: The Mean

 
7.4 Sampling Distributions: The Variance

 
7.5 The Standard Error of the Mean

 
7.6 Factors That Decrease Standard Error

 
7.7 SPSS in Focus: Estimating the Standard Error of the Mean

 
7.8 Standard Normal Transformations With Sampling Distributions

 
 
8. Hypothesis Testing: Significance, Effect Size, Estimation, and Power
8.1 The Informativeness of Evaluating Effects in Science

 
8.2 Inferential Statistics and Applying the Steps to Hypothesis Testing

 
8.3 Making a Decision: Types of Error

 
8.4 Testing for Significance: Examples Using the z Test

 
8.5 Measuring the Size of an Effect: Cohen’s d

 
8.6 Confidence Intervals for the One-Sample z Test

 
8.7 Factors That Influence Power

 
8.8 Assumptions of Parametric Testing: Normality and Nonparametric Alternatives

 
8.9 SPSS in Focus: A Preview for Analyzing Inferential Statistics

 
 
PART III. MAKING INFERENCES ABOUT ONE OR TWO MEANS
 
9. Testing Means: One-Sample t Test With Confidence Intervals
9.1 Going From z to t

 
9.2 The Degrees of Freedom

 
9.3 Reading the t Table

 
9.4 Computing the One-Sample t Test

 
9.5 Effect Size for the One-Sample t Test

 
9.6 Confidence Intervals for the One-Sample t Test

 
9.7 Inferring Significance and Effect Size From a Confidence Interval

 
9.8 SPSS in Focus: One-Sample t Test and Confidence Intervals

 
 
10. Testing Means: Two-Independent-Sample t Tests With Confidence Intervals
10.1 Introduction to the Between-Subjects Design

 
10.2 Selecting Two Independent Samples

 
10.3 Variability and Comparing Differences Between Two Groups

 
10.4 Computing the Two-Independent-Sample t Test

 
10.5 Effect Size for the Two-Independent-Sample t Test

 
10.6 Confidence Intervals for the Two-Independent-Sample t Test

 
10.7 Inferring Significance and Effect Size From a Confidence Interval

 
10.8 SPSS in Focus: Two-Independent-Sample t Test and Confidence Intervals

 
 
11. Testing Means: The Related-Samples t Test With Confidence Intervals
11.1 Selecting Related Samples

 
11.2 Advantages of Selecting Related Samples

 
11.3 Introduction to the Related-Samples t Test

 
11.4 Computing the Related-Samples t Test

 
11.5 Measuring Effect Size for the Related-Samples t Test

 
11.6 Confidence Intervals for the Related-Samples t Test

 
11.7 Inferring Significance and Effect Size From a Confidence Interval

 
11.8 SPSS in Focus: Related-Samples t Test and Confidence Intervals

 
 
PART IV. MAKING INFERENCES ABOUT THE VARIABILITY OF TWO OR MORE MEANS
 
12. Analysis of Variance: One-Way Between-Subjects Design
12.1 Introduction to Analysis of Variance

 
12.2 Selecting Two or More Independent Samples

 
12.3 The Test Statistic and Sources of Variation

 
12.4 Degrees of Freedom

 
12.5 The One-Way Between-Subjects ANOVA

 
12.6 Post Hoc Tests

 
12.7 Measuring Effect Size

 
12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA

 
 
13. Analysis of Variance: One-Way Within-Subjects (Repeated-Measures) Design
13.1 Analysis of Variance for a Within-Subjects Factor

 
13.2 The Test Statistic and Sources of Variation

 
13.3 Degrees of Freedom

 
13.4 The One-Way Within-Subjects ANOVA

 
13.5 Post Hoc Comparisons: Bonferroni Procedure

 
13.6 Measuring Effect Size

 
13.7 SPSS in Focus: The One-Way Within-Subjects ANOVA

 
13.8 The Within-Subjects Design: Consistency and Power

 
 
14. Analysis of Variance: Two-Way Between-Subjects Factorial Design
14.1 Analysis of Variance With Two Factors

 
14.2 Designs for the Two-Way ANOVA

 
14.3 Describing Variability: Main Effects and Interactions

 
14.4 The Two-Way Between-Subjects ANOVA

 
14.5 Analyzing Main Effects and Interactions

 
14.6 Measuring Effect Size

 
14.7 SPSS in Focus: The Two-Way Between-Subjects ANOVA

 
 
PART V. MAKING INFERENCES ABOUT PATTERNS, FREQUENCIES, AND ORDINAL DATA
 
15. Correlation
15.1 The Structure of a Correlational Design

 
15.2 The Pearson Test Statistic and Sources of Variability

 
15.3 Assumptions for the Pearson Correlation Coefficient

 
15.4 Pearson Correlation Coefficient

 
15.5 Effect Size: The Coefficient of Determination

 
15.6 SPSS in Focus: Pearson Correlation Coefficient

 
15.7 Limitations in Interpretation: Causality, Outliers, and Restriction of Range

 
15.8 Alternative to Pearson’s r: Spearman Correlation Coefficient

 
15.9 SPSS in Focus: Spearman Correlation Coefficient

 
15.10 Alternative to Pearson’s r: Point-Biserial Correlation Coefficient

 
15.11 SPSS in Focus: Point-Biserial Correlation Coefficient

 
15.12 Alternative to Pearson’s r: Phi Correlation Coefficient

 
15.13 SPSS in Focus: Phi Correlation Coefficient

 
 
16. Linear Regression and Multiple Regression
16.1 The Structure of Linear Regression

 
16.2 What Makes the Regression Line the Best-Fitting Line?

 
16.3 The Slope and y-Intercept of a Straight Line

 
16.4 Using the Method of Least Squares to Find the Best Fit

 
16.5 Evaluating Significance Using Analysis of Regression

 
16.6 Using the Standard Error of Estimate to Measure Accuracy

 
16.7 SPSS in Focus: Analysis of Regression

 
16.8 Introduction to Multiple Regression

 
16.9 Evaluating Significance Using Multiple Regression

 
16.10 The ß Coefficient for Multiple Regression

 
16.11 Evaluating Significance for the Relative Contribution of Each Predictor Variable

 
16.12 SPSS in Focus: Multiple Regression Analysis

 
 
17. Nonparametric Tests: Chi-Square Tests
17.1 Introduction to the Chi-Square Test

 
17.2 Comparing Observed and Expected Frequencies for the Goodness-of-Fit Test

 
17.3 The Test Statistic and Degrees of Freedom for the Goodness-of-Fit Test

 
17.4 Computing the Chi-Square Goodness-of-Fit Test

 
17.5 Interpreting the Chi-Square Goodness-of-Fit Test

 
17.6 SPSS in Focus: The Chi-Square Goodness-of-Fit Test

 
17.7 Introduction to the Chi-Square Test for Independence

 
17.8 Computing the Chi-Square Test for Independence

 
17.9 The Relationship Between Chi-Square and the Phi Coefficient

 
17.10 Measures of Effect Size

 
17.11 SPSS in Focus: The Chi-Square Test for Independence

 
 
18. Nonparametric Tests: Tests for Ordinal Data
18.1 Tests for Ordinal Data

 
18.2 The Sign Test

 
18.3 SPSS in Focus: Computing the Related-Samples Sign Test

 
18.4 The Wilcoxon Signed-Ranks T Test

 
18.5 SPSS in Focus: Computing the Wilcoxon Signed-Ranks T Test

 
18.6 The Mann-Whitney U Test

 
18.7 SPSS in Focus: Computing the Mann-Whitney U Test

 
18.8 The Kruskal-Wallis H Test

 
18.9 SPSS in Focus: Computing the Kruskal-Wallis H Test

 
18.10 The Friedman Test

 
18.11 SPSS in Focus: Computing the Friedman Test

 
 
Appendix B. Basic Math Review and Summation Notation
 
Appendix A. Overview of Core Statistical Concepts in the Behavioral Sciences
 
Appendix C. SPSS General Instructions Guide With Steps for Evaluating Assumptions for Inferential Statistics
 
Appendix D. Statistical Tables
 
Appendix E. Chapter Solutions for Even-Numbered Problems
 
Glossary
 
References
 
Index

Supplements

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The online resources for your text are available via the password-protected Instructor Resource Site, which offers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides.
Student Resources

The clarity in which the information is covered and the variety of ways that the information is presented is extremely impressive. For every concept, there is the text, figures, examples, and applications.

Allyson Phillips
Ouachita Baptist University

I like the idea of some more comprehensive connections between what students learn in lab and class; I think the SPSS components in the text create that connectedness.

Dana K. Donohue
Northern Arizona University

I believe this approach is much more effective, and I am so glad to see it!

Hilary Campbell
Blue Ridge Community College

The book is extremely well written and easy to follow.

Robyn Cooper
Drake University

The author did a great job on explaining each key concepts, and how to analyze data with SPSS is also clear.

Yukiko Maeda
Purdue University

Very well written and easy to follow for students.

Joshua L. Karelitz
Pennsylvania State University

Review as the primary source for the my Psych Stats course. Has great examples and easy to read.

Dr Melissa C. Baron
Psychology Dept, Kean University - Union
March 29, 2024
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