Neil J. Salkind received his PhD in human development from the University of Maryland, and after teaching for 35 years at the University of Kansas, he was Professor Emeritus in the Department of Psychology and Research in Education, where he collaborated with colleagues and work with students. His early interests were in the area of children's cognitive development, and after research in the areas of cognitive style and (what was then known as) hyperactivity, he was a postdoctoral fellow at the University of North Carolina's Bush Center for Child and Family Policy. His work then changed direction to focus on child and family policy, specifically the impact of alternative forms of public support on various child and family outcomes. He delivered more than 150 professional papers and presentations; written more than 100 trade and textbooks; and is the author of Statistics for People Who (Think They) Hate Statistics (SAGE), Theories of Human Development (SAGE), and Exploring Research (Prentice Hall). He has edited several encyclopedias, including the Encyclopedia of Human Development, the Encyclopedia of Measurement and Statistics, and the Encyclopedia of Research Design. He was editor of Child Development Abstracts and Bibliography for 13 years. He lived in Lawrence, Kansas, where he liked to read, swim with the River City Sharks, work as the proprietor and sole employee of big boy press, bake brownies (see [...] for the recipe), and poke around old Volvos and old houses.

A Note to the Student: Why We Wrote This Book
Acknowledgements
And Now, About the Seventh Edition
About the Authors
Part I: Yippee! I¿m in Statistics!
Chapter 1. Statistics or Sadistics? It's Up to You
What You Will Learn in This Chapter
Why Statistics?
A 5-Minute History of Statistics
Statistics: What It Is (and Isn't)
What Am I Doing in a Statistics Class?
Ten Ways to Use This Book (and Learn Statistics at the Same Time!)
About the Book's Features
Key to Difficulty Icons
Glossary
Summary
Part II: Zigma Freud and Descriptive Statistics
Chapter 2. Computing and Understanding Averages: Means to an End
What You Will Learn in This Chapter
Computing the Mean
Computing the Median
Computing the Mode
When to Use What Measure of Central Tendency (and All You Need to Know About Scales of Measurement for Now)
Using SPSS to Compute Descriptive Statistics
Real-World Stats
Summary
Time to Practice
Chapter 3. Understanding Variability: Vivé la Différence
What You Will Learn in This Chapter
Why Understanding Variability Is Important
Computing the Range
Computing the Standard Deviation
Computing the Variance
Using SPSS to Compute Measures of Variability
Real-World Stats
Summary
Time to Practice
Chapter 4. Creating Graphs: A Picture Really Is Worth a Thousand Words
What You Will Learn in This Chapter
Why Illustrate Data?
Ten Ways to a Great Figure (Eat Less and Exercise More?)
First Things First: Creating a Frequency Distribution
The Plot Thickens: Creating a Histogram
The Next Step: A Frequency Polygon
Other Cool Ways to Chart Data
Using the Computer (SPSS, That Is) to Illustrate Data
Real-World Stats
Summary
Time to Practice
Chapter 5. Computing Correlation Coefficients: Ice Cream and Crime
What You Will Learn in This Chapter
What Are Correlations All About?
Computing a Simple Correlation Coefficient
Understanding What the Correlation Coefficient Means
Squaring the Correlation Coefficient: A Determined Effort
Other Cool Correlations
Parting Ways: A Bit About Partial Correlation
Real-World Stats
Summary
Time to Practice
Chapter 6. An Introduction to Understanding Reliability and Validity: Just the Truth
What You Will Learn in This Chapter
An Introduction to Reliability and Validity
Reliability: Doing It Again Until You Get It Right
Different Types of Reliability
How Big Is Big? Finally: Interpreting Reliability Coefficients
Validity: Whoa! What Is the Truth?
A Last Friendly Word
Validity and Reliability: Really Close Cousins
Real-World Stats
Summary
Time to Practice
Part III: Taking Chances for Fun and Profit
Chapter 7. Hypotheticals and You: Testing Your Questions
What You Will Learn in This Chapter
So You Want to Be a Scientist?.?.?.
Samples and Populations
The Null Hypothesis
The Research Hypothesis
What Makes a Good Hypothesis?
Real-World Stats
Summary
Time to Practice
Chapter 8. Probability and Why it Counts: Fun with a Bell-Shaped Curve
What You Will Learn in This Chapter
Why Probability?
The Normal Curve (a.k.a. the Bell-Shaped Curve)
Our Favorite Standard Score: The z Score
Fat and Skinny Frequency Distributions
Real-World Stats
Summary
Time to Practice
Part IV Significantly Different: Using Inferential Statistics
Chapter 9. Significantly Significant: What It Means for You and Me
What You Will Learn in This Chapter
The Concept of Significance
Significance Versus Meaningfulness
An Introduction to Inferential Statistics
An Introduction to Tests of Significance
Be Even More Confident
Real-World Stats
Summary
Time to Practice
Chapter 10. The One-Sample Z Test: Only the Lonely
What You Will Learn in This Chapter
Introduction to the One-Sample Z Test
The Path to Wisdom and Knowledge
Computing the Z Test Statistic
Using SPSS to Perform a Z Test
Special Effects: Are Those Differences for Real?
Real-World Stats
Summary
Time to Practice
Chapter 11. t(ea) for Two: Tests Between the Means of Different Groups
What You Will Learn in This Chapter
Introduction to the t Test for Independent Samples
The Path to Wisdom and Knowledge
Computing the t Test Statistic
The Effect Size and t(ea) for Two
Using SPSS to Perform a t Test
Real-World Stats
Summary
Time to Practice
Chapter 12. t(ea) for Two (Again): Tests Between the Means of Related Groups
What You Will Learn in This Chapter
Introduction to the t Test for Dependent Samples
The Path to Wisdom and Knowledge
Computing the t Test Statistic
Using SPSS to Perform a Dependent t Test
The Effect Size for t(ea) for Two (Again)
Real-World Stats
Summary
Time to Practice
Chapter 13. Two Groups Too Many? Try Analysis of Variance
What You Will Learn in This Chapter
Introduction to Analysis of Variance
The Path to Wisdom and Knowledge
Different Flavors of Analysis of Variance
Computing the F Test Statistic
Using SPSS to Compute the F Ratio
The Effect Size for One-Way ANOVA
Real World Stats
Summary
Time to Practice
Chapter 14. Two Too Many Factors: Factorial Analysis of Variance-A Brief Introduction
What You Will Learn in This Chapter
Introduction to Factorial Analysis of Variance
The Path to Wisdom and Knowledge
A New Flavor of ANOVA
The Main Event: Main Effects in Factorial ANOVA
Even More Interesting: Interaction Effects
Using SPSS to Compute the F Ratio
Computing the Effect Size for Factorial ANOVA
Real World Stats
Summary
Time to Practice
Chapter 15. Testing Relationships Using the Correlation Coefficient: Cousins or Just Good Friends?
What You Will Learn in This Chapter
Introduction to Testing the Correlation Coefficient
The Path to Wisdom and Knowledge
Computing the Test Statistic
Using SPSS to Compute a Correlation Coefficient (Again)
Real World Stats
Summary
Time to Practice
Chapter 16. Using Linear Regression: Predicting the Future
What You Will Learn in This Chapter
Introduction to Linear Regression
What Is Prediction All About?
The Logic of Prediction
Drawing the World's Best Line (for Your Data)
How Good Is Your Prediction?
Using SPSS to Compute the Regression Line
The More Predictors the Better? Maybe
Real-World Stats
Summary
Time to Practice
Part V: More Statistics! More Tools! More Fun!
Chapter 17. Chi-Square and Some Other Nonparametric Tests: What to Do When You're Not Normal
What You Will Learn in This Chapter
Introduction to Nonparametric Statistics
Introduction to the Goodness of Fit (One-Sample) Chi-Square
Computing the Goodness of Fit Chi-Square Test Statistic
Introduction to the Test of Independence Chi-Square
Computing the Test of Independence Chi-Square Test Statistic
Using SPSS to Perform Chi-Square Tests
Other Nonparametric Tests You Should Know About
Real-World Stats
Summary
Time to Practice
Chapter 18. Some Other (Important) Statistical Procedures You Should Know About
What You Will Learn in This Chapter
Multivariate Analysis of Variance
Repeated Measures Analysis of Variance
Analysis of Covariance
Multiple Regression
Meta-analysis
Discriminant Analysis
Factor Analysis
Path Analysis
Structural Equation Modeling
Summary
Chapter 19. Data Mining: An Introduction to Getting the Most Out of Your BIG Data
What You Will Learn in This Chapter
Our Sample Data Set-Who Doesn't Love Babies?
Counting Outcomes
Pivot Tables and Cross-Tabulation: Finding Hidden Patterns
Summary
Time to Practice
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
Appendix H
Appendix I
Glossary