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Essential Statistics for the Behavioral Sciences
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Essential Statistics for the Behavioral Sciences

Second Edition (International Student Edition)
Experience with SAGE edge


April 2018 | SAGE Publications, Inc

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.



 
PART I: Introduction and Descriptive Statistics
 
Chapter 1:Introduction to Statistics
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  
 
Chapter 2: Summarizing Data: Frequency Distributions in Tables and Graphs
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  
 
Chapter 3: Summarizing Data: Central Tendency
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  
 
Chapter 4: Summarizing Data: Variability
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  
 
PART II: Probability and the Foundations of Inferential Statistics
 
Chapter 5: Probability, Normal Distributions, and z Scores
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  
 
Chapter 6: Characteristics of the Sample Mean
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  
 
Chapter 7: Hypothesis Testing: Significance, Effect Size, and Power
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  
 
PART III: Making Inferences About One or Two Means
 
Chapter 8: Testing Means: One-Sample t Test With Confidence Intervals
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  
 
Chapter 9: Testing Means: Two-Independent-Sample t Test With 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  
 
Chapter 10: Testing Means: Related-Samples t Test With 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  
 
PART IV: Making Inferences About The Variability of Two or More Means
 
Chapter 11: One-Way Analysis of Variance: Between-Subjects and Within-Subjects (Repeated-Measures) Designs
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  
 
Chapter 12: Two-Way Analysis of Variance: Between- Subjects Factorial Design
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  
 
PART V: Making Inferences About Patterns, Prediction, and Nonparametric Tests
 
Chapter 13: Correlation and Linear Regression
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  
 
Chapter 14: Chi-Square Tests: Goodness-of-Fit and the Test for Independence
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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