# Essential Statistics for the Behavioral Sciences

- Gregory J. Privitera - St. Bonaventure University

**Essentials of Statistics for the Behavioral Sciences is a concise version of ***Statistics for the Behavioral Sciences *by award-winning teacher, author, and advisor Gregory J. Privitera.

The **Second Edition** provides balanced coverage for today’s students, connecting the relevance of core concepts to daily life with new introductory vignettes for every chapter, while speaking to the reader as a researcher when covering statistical theory, computation, and application. Robust pedagogy allows students to continually check their comprehension and hone their skills while working through carefully developed problems and exercises that include current research and seamless integration of IBM® SPSS® Statistics.

Readers will welcome Privitera’s thoughtful instruction, conversational voice, and application of statistics to real-world problems.

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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