Pub. Date:
Worth Publishers
Statistics for the Behavioral Sciences / Edition 2

Statistics for the Behavioral Sciences / Edition 2

by Susan A. Nolan, Thomas HeinzenSusan A. Nolan
Current price is , Original price is $201.75. You

Temporarily Out of Stock Online

Please check back later for updated availability.

Temporarily Out of Stock Online


What do Florence Nightingale, a failed Antarctic expedition, the Guinness Ale Company, and the Challenger space shuttle disaster have in common? Discover the answer in this new statistics textbook for the behavioral sciences.

In Statistics for the Behavioral Sciences, Susan Nolan and Tom Heinzen convincingly convey the social power and the personal benefits of statistical reasoning while faithfully teaching its underlying mathematical principles. Chapter-specific narratives let students encode and retain their learning through vivid associative memory hooks that all carry the same message: Statistics has a success story to tell. Embedding that story within each chapter makes learning statistics easier by making it more meaningful.

The resulting text conforms to the traditional structure and elements of a statistics textbook, but it shifts the paradigm for teaching behavioral statistics from apology (for being so difficult) to opportunity (to create a meaningful career). You, and your students, will rave about a statistics textbook! Welcome to Nolan and Heinzen’s Statistics for the Behavioral Sciences!

Product Details

ISBN-13: 9781429232654
Publisher: Worth Publishers
Publication date: 02/01/2011
Edition description: Second Edition
Pages: 736
Product dimensions: 8.20(w) x 10.00(h) x 1.10(d)

About the Author

Susan Nolan turned to psychology after suffering a career-ending accident on her second workday as a bicycle messenger. A native of Boston, she graduated from the College of the Holy Cross and earned her Ph.D. in clinical psychology from Northwestern University. Her research involves experimental investigations of the role of gender in the interpersonal consequences of depression and studies of gender and mentoring in science and technology, funded in part by the National Science Foundation. Susan is the Associate Dean of Graduate Studies for the College of Arts and Sciences, as well as an Associate Professor of Psychology, at Seton Hall University in New Jersey. She has served as a statistical consultant to researchers at several universities, medical schools, corporations, and nongovernmental organizations. Recently, she advised Bosnian high school students conducting public opinion research.

Susan's academic schedule allows her to pursue one travel adventure per year, a tradition that she relishes. In recent years she rode her bicycle across the U.S. (despite her earlier crash), swapped apartments to live in Montreal, and explored the Adriatic coast in an intermittently roadworthy 1985 Volkswagen Scirocco. She wrote much of this book while spending a sabbatical year in rural Bosnia-Herzegovina, where her husband, Ivan Bojanic, worked as an advisor to regional governments. Susan and Ivan fell in love with Bosnia – a beautiful country – and bought a small house in the city of Banja Luka as a base for future adventures. They currently reside in New York City, where Susan roots feverishly, if quietly, for the Red Sox.

Tom Heinzen was a 29 year-old college freshman, began graduate school when their fourth daughter was one week old, and is still amazed that he and Donna somehow managed to stay married. A magna cum laude graduate of Rockford College, he earned his Ph.D. in social psychology at the State University of New York at Albany in just three years. He published his first book on frustration and creativity in government two years later, was a research associate in public policy until he was fired over the shape of a graph, consulted for the Johns Hopkins Center for Talented Youth, and then began a teaching career at William Paterson State University of New Jersey. He founded the psychology club, established an undergraduate research conference, and has been awarded various teaching honors while continuing to write journal articles, books, plays, and two novels that support the teaching of general psychology and statistics. He is also the editor of Many Things to Tell You, a volume of poetry by elderly writers.

Tom's wife Donna is a physician assistant who has also volunteered her time in relief work following Hurricane Mitch and Hurricane Katrina. Their daughters are now scattered from Bangladesh to Mississippi to New Jersey and work in public health, teaching, and medicine. He is a mediocre French horn player, an enthusiastic but mediocre tennis player, and an ardent baseball fan (Go Cubs!).

Table of Contents

1. An Introduction to Statistics and Research Design: The Elements of Statistical Reasoning
Two Branches of Statistics: Growing Our Knowledge about Human Behavior
Descriptive Statistics: Organizing, Summarizing, and Communicating Numerical Information
Inferential Statistics: Using Samples to Draw Conclusions about a Population
Distinguishing Between a Sample and a Population

Variables: Transforming Observations into Numbers

Independent and Dependent Variables: The Main Ingredients of Statistical Thinking
Putting Variables to Work: Independent, Dependent, and Confounding Variables
Developing and Assessing Variables: The Reliability and Validity of Tests

An Introduction to Hypothesis Testing: From Hunch to Hypothesis

Types of Research Designs: Experiments, Non-Experiments, and Quasi-Experiments
Experiments and Causality: Control the Confounding Variables
Research Designs Other than Experiments: Non-Experiments and Quasi-Experiments
One Goal, Two Strategies: Between-subjects Designs vs. Within-subjects Designs

Curiosity, Joy, and the Art of Research Design

Digging Deeper Into the Data: Variations on Standard Research Designs
Outlier Analyses: Does the Exception Prove the Rule?
Archival Studies: When the Data Already Exist

Chapter 2: Descriptive Statistics: Organizing, Summarizing, and Graphical Individual Variables
Organizing Our Data: A First Step in Identifying Patterns

Distributions: Four Different Ways to Describe Just One Variable
Applying Visual Depictions of Data: Generating Research Questions

Central Tendency: Determining the Typical Score
The Need for Alternative Measures of Central Tendency: Bipolar Disorder
Mean: The Arithmetic Average
Median: The Middle Score
Mode: The Most Common Score
The Effect of Outliers on Measures of Central Tendency
An Early Lesson in Lying With Statistics: Which Central Tendency is “Best?”

Measures of Variability: Everyone Can’t Be “Typical”
Range: From the Lowest to the Highest Score
Variance: The First Step in Calculating Standard Deviation
Standard Deviation: Variation from the Mean

Shapes of Distributions: Applying the Tools of Descriptive Statistics
Normal Distributions: The Silent Power Behind Statistics
Skewed Distributions: When Our Data Are Not Symmetrical
Bimodal and Multimodal Distributions: Identifying Distinctive Populations
Kurtosis and Distributions: Tall and Skinny Versus Short and Wide

Digging Deeper into the Data: Alternate Approaches to Descriptive Statistics
The Interquartile Range: An Alternative to the Range
Statistics that Don’t Focus on the Mean: Letting the Distribution Guide our Choice of Statistics

Chapter 3: Visual Displays of Data: Graphs That Tell a Story
Uses of Graphs: Clarifying Danger, Exposing Lies, and Gaining Insight
Graphing in the Information Age: A Critical Skill
“The Most Misleading Graph Ever Published”: The Cost and Quality of Higher Education
“The Best Statistical Graph Ever Created”: Napoleon’s Disastrous March to Moscow

Common Types of Graphs: A Graph Designer’s Building Blocks
Scatterplots: Observing Every Data Point
Line Graphs: Searching for Trends
Bar Graphs: An Efficient Communicator
Pictorial Graphs: Choosing Clarity over Cleverness
Pie Charts: Are Pie Charts Passé?

How to Build a Graph: Dos and Don’ts
APA Style: Graphing Guidelines for Psychologists
Choosing the Type of Graph: Understanding Our Variables
The Limitations of Graphic Software: Who is Responsible for the Visual Display?
Creating the Perfect Graph: General Guidelines

Graphing Literacy: Learning to Lie Versus Creating Knowledge
Lying with Statistics and Graphs: Eleven Sophisticated Techniques
The Future of Graphs: Breaking the Fourth Wall
The Uses and Misuses of Statistics: It’s Not Just What You Draw, It’s How You Draw It

Digging Deeper into the Data: The Box Plot

Chapter 4: Probabilities and Research: The Risks and Rewards of Scientific Sampling
Samples and Their Populations: Why Statisticians Are Stingy!
Decision Making: The Risks and Rewards of Sampling
Random Sampling: An Equal Chance of Being Selected
Variations on Random Sampling: Cluster Sampling and Stratified Sampling
Convenience Sampling: Readily Available Participants
Random Assignment: An Equal Chance of Being Assigned to a Condition
Variations on Random Assignment: Block Design and Replication

Sampling in the Behavioral Sciences: Why Sampling is Both an Art and a Science
Neither Random Selection, Nor Random Assignment: A Study of Torture
Random Assignment, But Not Random Selection: A Study of Expert Testimony
Random Selection, But Not Random Assignment: A Study of Children’s Literature

Probability Theory: Distinguishing Between Mere Coincidence and Real Connections
Coincidence and Probability: Why Healthy Skepticism Is Healthy
Beyond Confirmation Biases: The Dangers of Groupthink

Probability Theory: The Basics
Expected Relative-Frequency Probability: The Probability of Statistics
Independence and Probability: The Gambler’s Fallacy
Statistician Sleuths: The Case of Chicago’s Cheating Teachers

Statistics and Probability: The Logic of Inferential Statistics
Dead Grandmothers: Using Probability to Make Decisions
Consideration of Future Consequences: Developing Hypotheses
Consideration of Future Consequences: Making a Decision about Our Hypotheses

Type I and Type II Errors: Statistical Inferences Can Be Wrong
Type I Errors: Sins of Commission
Type II Errors: Sins of Omission

Statistics in Everyday Life: Tying It All Together
The Case of Lush: Testimonial to a Moisturizer
Understanding the Meaning of Proof: Statistical Literacy in Consumer Research

Digging Deeper into the Data: The Shocking Prevalence of Type I Errors
Estimating Type I Error in the Medical Literature
Medical Findings and Our Own Confirmation Biases

Chapter 5. Correlation: Quantifying the Relation between Two Variables
Correlation: Assessing Associations between Variables
The Need for Standardization: Putting Two Different Variables on the Same Scale
The z Score: Transforming Raw Scores into Standardized Scores

The Pearson Correlation Coefficient: Quantifying a Linear Association
Everyday Correlation Reasoning: Asking Better Questions
Calculation of the Pearson Correlation Coefficient: Harnessing the Power of z Scores

Misleading Correlations: Considering the Stories behind the Numbers
Correlation is Not Causation: Invisible Third Variables
A Restricted Range: When the Values of One Variable Are Limited
The Effect of an Outlier: The Influence of a Single Data Point
Reliability and Validity: A Correlation Coefficient Is Only as Good as Our Data

Reliability and Validity: Correlation in Test Construction
Correlation, Psychometrics, and a Super-Heated Job Market: Creating the Measures behind the Research
Reliability: Using Correlation to Create a Consistent Test
Validity: Using Correlation to Determine Whether We Are Measuring What We Intend to Measure

Digging Deeper into the Data: Partial Correlation

Chapter 6. Regression: Tpols for Predicting Behavior
Regression: Building on Correlation
The Difference between Regression and Correlation: Prediction Versus Relation
Linear Regression: Calculating the Equation for a Line using z Scores Only
Reversing the Formula: Transforming z Scores to Raw Scores
Linear Regression: Calculating the Equation for a Line by Converting Raw Scores to z Scores
Linear Regression: Calculating the Equation for a Line with Raw Scores

Drawing Conclusions from a Regression Equation: Interpretation and Prediction
Regression: Now Think Again (Realistically)!
What Correlation Can Teach Us about Regression: Correlation Still Isn’t Causation
Regression to the Mean: The Patterns of Extreme Scores
The Effect Size for Regression: Proportionate Reduction in Error

Multiple Regression: Predicting from More than One Variable
Multiple Regression: Understanding the Equation
Stepwise Multiple Regression and Hierarchical Multiple Regression: A Choice of Tactics

Digging Deeper Into the Data: Structural Equation Modeling (SEM)

Chapter 7. The Power of Standardization: From Description to Inference
The Normal Curve: It’s Everywhere!
Standardization, z Scores, and the Normal Curve: Discovering Reason behind the Randomness
Standardization: Comparing z Scores
Putting z Scores to Work: Transforming z Scores to Percentiles

The Central Limit Theorem: How Sampling Creates a Less Variable Distribution
Creating a Distribution of Means: Understanding Why It Works
Characteristics of the Distribution of Means: Understanding Why It’s So Powerful

How to Take Advantage of the Central Limit Theorem: Beginning With z Scores
Creating Comparisons: Applying z Scores to a Distribution of Means
Estimating Population Parameters from Sample Statistics: Connecting Back

Digging Deeper into the Data: The History of the Normal Curve

Chapter 8. Hypothesis Testing With z Tests: Making Fair Comparisons
The Versatile z Table: Raw Scores, z Scores, and Percentages
From z Scores to Percentages: The Benefits of Standardization
From Percentages to z Scores: The Benefits of Sketching the Normal Curve
The z Table and Distributions of Means: The Benefits of Unbiased Comparisons

Hypothesis Tests: An Introduction
Assumptions: The Requirements to Conduct Analyses
The Six Steps of Hypothesis Testing

Hypothesis Tests: The Single Sample z Test
The z Test: When We Know the Population Mean and the Standard Deviation
The z Test: The Six Steps of Hypothesis Testing

The Effect of Sample Size: A Means to Increase the Test Statistic
Increasing Our Test Statistic through Sample Size: A Demonstration
The Effect of Increasing Sample Size: What’s Going On

Digging Deeper in the Data: What to Do with Dirty Data

Chapter 9. Hypothesis Testing with t Tests: Making Fair Comparisons between Two Groups
The t Distributions: Distributions of Means When the Parameters Are Not Known
Using a t Distribution: Estimating a Population Standard Deviation from a Sample
Calculating a t Statistic for the Mean of a Sample: Using the Standard Error
When t and z Are Equal: Very Large Sample Sizes
The t Distributions: Distributions of Differences Between Means

Hypothesis Tests: The Single Sample t Test
The Single Sample t Test: When We Know the Population Mean, But Not the Standard Deviation
The t Table: understanding Degrees of Freedom
The t Test: The Six Steps of Hypothesis Testing

Hypothesis Tests: Tests for Two Samples
The Paired Samples t Test: Two Sample Means and a Within-Groups Design
The Independent Samples t Test: Two Sample Means and a Between-Groups Design

Digging Deeper into the Data: Exploring Two Group Comparisons
Difference Scores: Are All Differences Created Equal?
Graphing Two Samples: Visualizing Two Sets of Scores

Chapter 10. Hypothesis Testing Using One-Way ANOVA: Comparing Three or More Groups
When to Use the F Distribution: Working With More than Two Samples

A Mnemonic for When to Use a t Distribution or the F Distribution: 't' for Two
The F Distribution: Analyzing Variability to Compare Means
Relation of F to t (and z): F as a Squared t for Two Groups and Large Samples

Analysis of Variance (ANOVA): Beyond t Tests
The Problem of Too Many t Tests: Fishing for a Finding
The Assumptions for ANOVA: Naming the Ideal Conditions for the Perfect Study

The One-Way Between-Groups ANOVA: Applying the Six Steps of Hypothesis Testing
Everything ANOVA but the Calculations: The Six Steps of Hypothesis Testing
The F Statistic: Logic and Calculations
Bringing It All Together: What Is the ANOVA Telling Us to Do About the Null Hypothesis?
Why the ANOVA is Not Sufficient: Post-Hoc Tests

Digging Deeper into the Data: Post-Hoc Tests to Determine Which Groups Are Different
Planned and A Priori Comparisons: When Comparisons between Pairs Are Guided by Theory
Tukey HSD: An Honest Approach
The Bonferroni Test: A More Stringent Post-Hoc Test

Chapter 11. Two-Way ANOVA: Understanding Interactions
Two-Way ANOVA: When the Outcome Depends on More Than One Variable
Why Use a Two-Way ANOVA: The Practicalities and Aesthetics
The More Specific Vocabulary of Two-Way ANOVAs: Name That ANOVA Part II
Two Main Effects and an Interaction: Three F Statistics and Their Stories

The Layers of ANOVA: Understanding Interactions
Interactions and Public Policy: Using Two-Factor ANOVA to Improve Planning
Interpreting Interactions: Understanding Complexity
Visual Representations of Main Effects and Interactions: Bar Graphs

The Expanded Source Table: Conducting a Two-Way Between Subjects ANOVA
Two-Way ANOVA: The Six Steps of Hypothesis Testing
Two-Way ANOVA: Identifying Four Sources of Variability

Interactions: A More Precise Interpretation
Interpreting Interactions: Towards a More Precise Statistical Understanding
Residuals: Separating the Interaction from the Main Effect

Digging Deeper into the Data: More Sophisticated Versions of ANOVA
Within-Groups and Mixed Designs: When the Same Participants Experience More than One Condition
MANOVA, ANCOVA, and MANCOVA: Multiple Dependent Variables and Covariates

Chapter 12. Beyond Hypothesis Testing: Confidence Intervals, Effect Size, and Power
Beyond Hypothesis Testing: Reducing Misinterpretations
Men, Women, and Math: An Accurate Understanding of Gender Differences
Beyond Hypothesis Testing: Enhancing Our Samples’ Stories

Confidence Intervals: An Alternative to Hypothesis Testing
Interval Estimation: A Range of Plausible Means
z Distributions: Calculating Confidence Intervals
t Distributions: Calculating Confidence Intervals

Effect Size: Just How Big is the Difference?
Misunderstandings from Hypothesis Testing: When “Significant” Isn’t Very Significant
What Effect Size Is: Standardization across Studies
Cohen’s d: The Effect Size for a z Test or a t Test
R2: The Effect Size for ANOVA

Statistical Power and Sensitivity: Correctly Rejecting the Null Hypothesis
Calculation of Statistical Power: How Sensitive is a z Test?
Beyond Sample Size: Other Factors that Affect Statistical Power

Digging Deeper into the Data: Meta-Analysis
Meta-Analysis: A Study of Studies
The Steps to Conduct a Meta-Analysis
The File Drawer Statistic: Where Are All the Null Results?

Chapter 13. Chi Square: Quantifying the Difference between Expectations and Observations
Non-Parametric Statistics: When We’re Not Even Close to Meeting the Assumptions
Non-Parametric Tests: Using the Right Statistical Tool for the Right Statistical Job
Non-Parametric Tests: When to Use Them
Non-Parametric Tests: Why to Avoid Them Whenever Possible

Chi-square Test for Goodness-of-Fit: When We Have One Nominal Variable
Chi-Square Test for Goodness-of-Fit: The Six Steps of Hypothesis Testing
A More Typical Chi-Square Test for Goodness-of-Fit: Evenly Divided Expected Frequencies

Chi-square Test for Independence: When We Have Two Nominal Variables
Chi-Square Test for Independence: The Six Steps of Hypothesis Testing
Cramer’s Phi: The Effect Size for Chi square
Graphing Chi-square Percentages: Depicting the Relation Visually
Relative Risk: How Much Higher Are the Chances of an Outcome?

Digging Deeper into the Data: A Deeper Understanding of Chi square
Standardized Residuals: A Post-Hoc Test for Chi square
Chi-Square Controversies: Expectations about Expected Frequencies

Chapter 14. Beyond Chi Square: Commonly Used Non-parametric Tests with Ordinal Data
Non-Parametric Statistics: When the Data Are Ordinal
Hypothesis Tests with Ordinal Data: A Non-parametric Equivalent for Every Parametric Test
Examining the Data: Deciding to Use a Non-parametric Test for Ordinal Data

Spearman Rank Order Correlation Coefficient: Quantifying the Association between Two Ordinal Variables
Calculating Spearman’s Correlation: Converting Interval Observations to Rank-Ordered Observations
Eye-Balling the Data: Using Your Scientific Common Sense

Non-parametric Hypothesis Tests: Comparing Groups Using Ranks
The Wilcoxon Signed-Rank Test for Matched Pairs: A Non-parametric Test for Within-Subjects Designs
The Mann-Whitney U Test: Comparing Two Independent Groups Using Ordinal Data
Kruskal-Wallis H Test: Comparing the Mean Ranks of Several Groups

Digging Deeper into the Data: Transforming Skewed Data, the Meaning of Interval Data, and Bootstrapping

Coping with Skew: Data Transformations
Controversies in Non-Parametric Hypothesis Tests: What Really Is an Interval Variable?
Bootstrapping: When the Data Do the Work Themselves

Chapter 15. Choosing a Statistical Test and Reporting the Results: The Process of Statistics.
Before You Even Begin: Choosing the Right Statistical Test
Planning Your Statistics First: How to Avoid That Post-Data Collection Regret
Beyond the Statistical Plan: Tips for a Successful Study

Guidelines for Reporting Statistics: The Common Language of Research
Choosing the Right Statistical Test: Questions to Ask Yourself
Choosing the Right Statistical Test: Questions to Ask About the Data

Reporting the Statistics: The Results Section of an APA-Style Paper
Telling Your Story: What to Include in a Results Section
Defending Your Study: Convincing the Reader that the Results are Worth Reading
“Traditional” Statistics: The Longstanding Way of Reporting Results
Statistics Strongly Encouraged by APA: Essential Additions to the “Traditional” Statistics
What Not to Include in a Results Section: Keeping the Story Focused
Two Excerpts from Results Sections: Understanding the Statistical Story
Unfamiliar Statistics: How to Approach Any Results Section with Confidence

Digging Deeper into the Data: Reporting More Sophisticated Statistical Analyses

Customer Reviews