## Specifications

book-author | Gregory J. Privitera |
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publisher | SAGE Publications; Inc; Second edition (August 8; 2014) |
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file-type | PDF |
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pages | 768 pages |
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language | English |
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isbn10 | 1452286906 |
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isbn13 | 9781452286907 |
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## Book Description

**Privitera’s Statistics for the Behavioral Sciences 2nd Edition**** (PDF)**; engages college students in an ongoing spirit of discovery by illustrating how statistics apply to modern-day research problems. By integrating robust pedagogy as well as practical examples and screenshots for using IBM SPSS® Statistics software; award-winning author Gregory J. Privitera balances statistical theory; application and computation with the technical instruction needed for statistics students to succeed in the modern era of data analysis; collection and statistical interpretation. Fully updated with current research and a full-color design; this new 2nd edition features even more real-world examples and an updated *Student Study Guide with SPSS Workbook (sold separately)*.

## Reviews

“Privitera does an EXCELLENT job of balancing clarity with depth.” ?**Ronald W. Stoffey**; *Kutztown University of Pennsylvania*

“Privitera ties research methods; SPSS; and statistics together in a SEAMLESS fashion.” ?**Walter M. Yamada**; *Azusa Pacific University*

“The writing style and the presentation of material are not only enjoyable to read but are easy to understand and follow.” ?**Joshua J. Dobias**; *Rutgers University*

“I like the objectives; the readability of the textbook; the straightforwardness of the presentations of concepts; the problems that are quite appropriate on many levels (theory; computation; etc.); and the emphasis on SPSS.” ?**Ted R. Bitner**; *DePauw University*

## Table of contents

Table of contents :

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

1.3 Research Methods and Statistics

1.4 Scales of Measurement

1.5 Types of Data

1.6 Research in Focus: Types of 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 Pictorial Frequency Distributions

2.9 Graphing Distributions: Continuous Data

2.10 Graphing Distributions: Discrete and Categorical Data

2.11 Research in Focus: Frequencies and Percents

2.12 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

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

4.3 Research in Focus: Reporting the Range

4.4 Quartiles and Intequartiles

4.5 The Variance

4.6 Explaining Variance for Populations and Samples

4.7 The Computational Formula for Variance

4.8 The Standard Deviation

4.9 What Does the Standard Deviation Tell Us?

4.10 Characteristics of the Standard Deviation

4.11 SPSS in Focus: Range, Variance, and Standard Deviation

Part II: Probability and the Foundations of Inferential Statistics

Chapter 5: Probability

5.1 Introduction to Probability

5.2 Calculating Probability

5.3 Probability and Relative Frequency

5.4 The Relationship Between Multiple Outcomes

5.5 Conditional Probabilities and Bayes’ Theorem

5.6 SPSS in Focus: Probability Tables

5.7 Probability Distributions

5.8 The Mean of a Probability Distribution and Expected Value

5.9 Research in Focus: When Are Risks Worth Taking?

5.10 The Variance and Standard Deviation of a Probability Distribution

5.11 Expected Value and the Binomial Distribution

5.12 A Final Thought on the Likelihood of Random Behavioral Outcomes

Chapter 6: Probability, Normal Distributions, and z Scores

6.1 The Normal Distribution in Behavioral Science

6.2 Characteristics of the Normal Distribution

6.3 Research in Focus: The Statistical Norm

6.4 The Standard Normal Distribution

6.5 The Unit Normal Table: A Brief Introduction

6.6 Locating Proportions

6.7 Locating Scores

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

6.9 Going From Binomial to Normal

6.10 The Normal Approximation to the Binomial Distribution

Chapter 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 APA in Focus: Reporting the Standard Error

7.9 Standard Normal Transformations With Sampling Distributions

Part III: Probability and the Foundations of Inferential Statistics

Chapter 8: Hypothesis Testing: Significance, Effect Size, and Power

8.1 Inferential Statistics and Hypothesis Testing

8.2 Four Steps to Hypothesis Testing

8.3 Hypothesis Testing and Sampling Distributions

8.4 Making a Decision: Types of Error

8.5 Testing for Significance: Examples Using the z Test

8.6 Research in Focus: Directional Versus Nondirectional Tests

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

8.8 Effect Size, Power, and Sample Size

8.9 Additional Factors That Increase Power

8.10 SPSS in Focus: A Preview for Chapters 9 to 18

8.11 APA in Focus: Reporting the Test Statistic and Effect Size

Chapter 9: Testing Means: One-Sample and Two-Independent Sample t Tests

9.1 Going From z to t

9.2 The Degrees of Freedom

9.3 Reading the t Table

9.4 One Sample t Test

9.5 Effect Size for the One Sample t Test

9.6 SPSS in Focus: One Sample t Test

9.7 Two–Independent Sample t Test

9.8 Effect Size for the Two–Independent Sample t Test

9.9 SPSS in Focus: Two–Independent Sample t Test

9.10 APA in Focus: Reporting the t Statistic and Effect Size

Chapter 10: Testing Means: Related Samples t Test

10.1 Related and Independent Samples

10.2 Introduction to the Related Samples t Test

10.3 Related Samples t Test: Repeated-Measures Design

10.4 SPSS in Focus: The Related Samples t Test

10.5 Related Samples t Test: Matched-Pairs Design

10.6 Measuring Effect Size for the Related Samples t Test

10.7 Advantages for Selecting Related Samples

10.8 APA in Focus: Reporting the t Statistic and Effect Size for Related Samples

Chapter 11: Estimation and Confidence Intervals

11.1 Point Estimation and Interval Estimation

11.2 The Process of Estimation

11.3 Estimation for the One–Sample z Test

11.4 Estimation for the One–Sample t Test

11.5 SPSS in Focus: Confidence Intervals for the One–Sample t Test

11.6 Estimation for the Two–Independent Sample t Test

11.7 SPSS in Focus: Confidence Intervals for the Two–Independent Sample t Test

11.8 Estimation for the Related Samples t Test

11.9 SPSS in Focus: Confidence Intervals for the Related Samples t Test

11.10 Characteristics of Estimation: Precisions and Certainty

11.11: APA in Focus: Reporting Confidence Intervals

Part IV: Making Inferences About the Variability of Two or More Means

Chapter 12. Analysis of Variance: One-Way Between-Subjects Design

12.1 Increasing k: A Shift to Analyzing Variance

12.2 An Introduction to Analysis of Variance

12.3 Sources of Variation and the Test Statistic

12.4 Degrees of Freedom

12.5 The One-Way Between-Subjects ANOVA

12.6 What Is the Next Step?

12.7 Post Hoc Comparisons

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

12.9 Measuring Effect Size

12.10 APA in Focus: Reporting the F Statistic, Significance, and Effect Size

Chapter 13: Analysis of Variance: One-Way Within-Subjects (Repeated Measures) Design

13.1 Observing the Same Participants Across Groups

13.2 Sources of Variation and the Test Statistic

13.3 Degrees of Freedom

13.4 The One-Way Within-Subjects ANOVA

13.5 Post Hoc Comparison: Bonferroni Procedure

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

13.7 Measuring Effect Size

13.8 The Within-Subjects Design: Consistency and Power

13.9 APA in Focus: Reporting the F Statistic, Significance, and Effect Size

Chapter 14. Analysis of Variance: Two-Way Between-Subjects Factorial Design

14.1 Observing Two Factors at the Same Time

14.2 New Terminology and Notation

14.3 Designs for the Two-Way ANOVA

14.4 Describing Variability: Main Effects and Interactions

14.5 The Two-Way Between-Subjects ANOVA

14.6 Analyzing Main Effects and Interactions

14.7 Measuring Effect Size

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

14.9 APA in Focus: Reporting Main Effects, Interactions, and Effect Size

Part V: Making Inferences About Patterns, Frequencies, and Ordinal Data

Chapter 15. Correlation

15.1 The Structure of a Correlational Design

15.2 Describing a Correlation

15.3 Pearson Correlation Coefficient

15.4 SPSS in Focus: Pearson Correlation Coefficient

15.5 Assumptions of Tests for Linear Correlations

15.6 Limitations in Interpretation: Causality, Outliers, and Restrictions of Range

15.7 Alternative to Pearson r: Spearman Correlation Coefficient

15.8 SPSS in Focus: Spearman Correlation Coefficient

15.9 Alternative to Pearson r: Point-Biserial Correlation Coefficient

15.10 SPSS in Focus: Point-Biserial Correlation Coefficient

15.11 Alternative to Pearson r: Phi Correlation Coefficient

15.12 SPSS in Focus: Phi Correlation Coefficient

15.13 APA in Focus: Reporting Correlations

Chapter 16: Linear Regression and Multiple Regression

16.1 From Relationships to Predictions

16.2 Fundamentals of Linear Regression

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

16.4 The Slope and y Intercept of a Straight Line

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

16.6 Using Analysis of Regression to Measure Significance

16.7 SPSS in Focus: Analysis of Regression

16.8 Using the Standard Error of Estimate to Measure Accuracy

16.9 Introduction to Multiple Regression

16.10 Computing and Evaluating Significance for Multiple Regression

16.11 The Beta Coefficient for Multiple Regression

16.12 Evaluating Significance for the Relative Contribution of Each Predictor Variable

16.13 SPSS in Focus: Multiple Regression Analysis

16.14 APA in Focus: Reporting Regression Analysis

Chapter 17: Nonparametric Tests: Chi-Square Tests

17.1 Tests for Nominal Data

17.2 The Chi-Square Goodness-of-Fit Test

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

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

17.5 Independent Observations and Expected Frequency Size

17.6 The Chi-Square Test for Independence

17.7 The Relationship Between Chi-Square and the Phi Coefficient

17.8 Measures of Effect Size

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

17.10 APA in Focus: Reporting the Chi-Square Test

Chapter 18: Nonparametric Tests: Tests for Ordinal Data

18.1 Tests for Ordinal Data

18.2 The Sign Test

18.3 SPSS in Focus: The Related Samples Sign Test

18.4. The Wilcoxon Signed-Ranks T Test

18.5 SPSS in Focus: The Wilcoxon Signed-Ranks T Test

18.6 The Mann-Whitney U Test

18.7 SPSS in Focus: The Mann-Whitney U Test

18.8 The Kruskal-Wallis H Test

18.9 SPSS in Focus: The Kruskal-Wallis H Test

18.10 The Friedman Test

18.11 SPSS in Focus: The Friedman Test

18.12 APA in Focus: Reporting Nonparametric Tests

Appendix A: Basic Math Review and Summation Notation

A.1 Positive and Negative Numbers

A.2 Addition

A.3 Subtraction

A.4 Multiplication

A.5 Division

A.6 Fractions

A.7 Decimals and Percents

A.8 Exponents and Roots

A.9 Order of Computation

A.10 Equations: Solving for x

A.11 Summation Notation

Appendix B: Statistical Tables

Table B.1 The Unit Normal Table

Table B.2 The t Distribution

Table B.3 Critical Values for F Distribution

Table B.4 The Studentized Range Statistic (q)

Table B.5 Critical Values for the Pearson Correlation

Table B.6 Critical Values for the Spearman Correlation

Table B.7 Critical Values of Chi-Square

Table B.8 Distribution of Binomial Probabilities

Table B.9 Wilcoxon Signed-Rank T Critical Values

Table B.10 Critical Values of the Mann-Whitney U

Appendix C: Chapter Solutions for Even-Numbered Problems