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Essential Statistics for the Behavioral Sciences - International Student Edition

Essential Statistics for the Behavioral Sciences - International Student Edition

Second Edition (International Student Edition)

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.

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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ISBN: 9781544328010