Andrea M. Noack
Using social issue examples and Canadian data throughout, this engaging introduction to social statistics guides students stepbystep through statistical analysis with plenty of opportunities to practice and apply concepts. Social Statistics in Action will not only show students how statistics
can be used as a tool for investigating social issues and inequalities, but will also prepare them to conduct their own analyses.
Every chapter includes:
 Learning Objectives
 Introduction
 StepbyStep box(es)
 How Does It Look in SPSS? box(es)
 Hands on Data Analysis box
 Spotlight on Data box
 What You Have Learned
 Check Your Understanding questions
 Practice What You Have
Learned problems
 Practice Using Statistical Software (IBM SPSS) activities
 Key Formulas (listed in applicable chapters)
 References
1. Learning to Think Statistically
 Features of this Book
 The Research Process and Statistical Analysis
 The Building
Blocks of Data Analysis: Variables and Values
 Levels of Measurement
 Understanding Computerized Data
 Identifying the Unit of Analysis
 Using Descriptive and Inferential Statistics
 Best Practices in Presenting Results
 Writing about Statistical Results
PART
I: Describing the Social World
2. Summarizing Data Using Numbers and Graphics
 Frequency Distributions
 Proportions and Percentages
 Ratios and Rates
 Visualizing Data
 Visualizing the Distribution of a Single Variable
 Displaying Change across Space
or Time
 CrossTabulations
 Visualizing the Relationship between Two Variables
 Panelling Techniques
 Clustering and Stacking Techniques
 Displaying Two Variables on the Same Chart
 Best Practices in Presenting Results
 Writing about Numbers
 Formatting
Tables
 Constructing Graphs
 Creating Infographics
3: Describing the Centre and Dispersion of a Distribution: Focus on Categorical Variables
 Describing the Centre of a Variable: Mode and Median
 Describing the Dispersion of a Variable: Percentiles and
Quantiles
 Describing the Dispersion of a Variable: Range and Interquartile Range
 Using Box Plots to Show the Centre and Dispersion of a Variable
 Using Boxplots to Show Relationships
 Best Practices in Presenting Results:
 Writing about the Centre and Dispersion of a
Variable
4. Describing the Centre, Dispersion, and Shape of a Distribution: Focus on RatioLevel Variables
 Describing the Centre of a RatioLevel Variable: The Mean
 Describing the Dispersion of a RatioLevel Variable: Standard Deviation and Variance
 Standardized
Scores
 How Outliers Affect the Mean and Standard Deviation
 Using Histograms to Show the Dispersion of RatioLevel Variables
 Key Features of the The Normal Distribution
 Standard Deviation and the Normal Distribution
 Finding the Area under the Normal Curve

Describing the Shape of a Variable: Skew and Kurtosis
 Best Practices in Presenting Results
 Writing about the Centre, Dispersion and Shape of Distributions
PART II: Making Claims about Populations
5. Probability, Sampling, and Weighting
 Probability: Some Basic
Concepts
 Probabilities, Frequency Distributions, and the Normal Distribution
 Sampling
 Sample Statistics and Population Parameters
 Types of Probability Sampling
 Estimating Variation using a Sample
 Weighting in Sample Surveys
 Misconceptions about
Weighting
 Best Practices in Presenting Results
 What Goes in a Methodology Section?
6. Making Population Estimates: Sampling Distributions, Standard Errors, and Confidence Intervals
 The Sampling Distribution of a Mean
 The Central Limit Theorem
 Standard
Error of a Mean
 Confidence Intervals
 Confidence Intervals in Action
 Using Confidence Intervals to Compare Group Means
 ErrorBar Graphs
 Standard Errors and Confidence Intervals for Proportions
 The Margin of Error
 Best Practices in Presenting Results

Writing about Confidence Intervals
7. Assessing Relationships by Comparing Group Means: TTests
 Making and Testing Hypotheses
 Assessing the Magnitude of a Relationship
 Comparing Means
 Effect Size: Cohen's d
 Assessing the Reliability of a Relationship

The Logic of Statistical Significance Tests
 The TTests of Independent Means
 OneTailed and TwoTailed Tests
 Other Types of TTests
 Best Practices in Presenting Results
 Writing about Tests of Statistical Significance
8. Assessing Relationships by Comparing
Group Means: ANOVA Tests
 Means Comparison in Action
 OneWay ANOVA Tests
 Calculating OneWay ANOVA Tests
 Calculating a FStatistic
 OneWay ANOVA Tests in Action
 Other Types of ANOVA Tests
 When to Use TTests and When to Use ANOVA tests
 Best
Practices in Presenting Results
 Writing about the Results of OneWay ANOVA Tests
9. Assessing Relationships between Categorical Variables
 Assessing the Magnitude of Relationships between Categorical Variables
 Proportionate Reduction in Error Measures
 The
ChiSquare Test of Independence
 ChiSquareBased Measures of Association
 Extending CrossTabulations: The Elaboration Model
 Replication and Specification
 Explanation and Interpretation
 Suppression and Distortion
 The Elaboration Model in Action
 Best
Practices in Presenting Results
 Writing about CrossTabulation Results
 Showing Multiple Bivariate Relationships in a Single Table
10. Assessing Relationships between RatioLevel Variables
 Describing Relationships between RatioLevel Variables
 Pearson's
Correlation Coefficient
 Calculating Pearson's Correlations Coefficient
 Reading Correlation Matrices
 Interpreting TStatistics for Pearson's Correlation Coefficient
 Spearman's RankOrder Correlation Coefficient
 Interpreting TStatistics for Pearson's Correlation
Coefficient
 Analyzing Partial Correlations
 Best Practices in Presenting Results
 Writing about Correlations
PART III: Modelling Relationships
11. Introduction to Linear Regression
Linear Regression Basics
 Describing a Regression Line

Calculating Slope and Constant Coefficients
 How Well does the Line Fit?
 The Coefficient of Determination: RSquared
 Statistical Inference in Linear Regression
 Confidence Intervals for Regression Coefficients
 Linear Regression in Action
 Some Regression
Assumptions
 Best Practices in Presenting Results
 Writing about Regression Results
12. Linear Regression with Multiple Independent Variables
 Multiple Linear Regression
 Controlling for Independent Variables in Regression
 Creating Regression Models

Calculating Multiple Linear Regression Coefficients
 Standardized Slope Coefficients
 Categorical Variables as Independent Variables in Regression
 Using Categorical Variables with More than Two Attributes
 Multiple Linear Regression in Action
 Best Practices in Presenting
Results:
 More on Writing about Regression Results
13. Building Linear Regression Models
 Nested Regressions
 Strategies for Selecting Independent Variables
 Adjusted R²
 Collinearity
 How to Analyze Regression Residuals
 Using Residuals to Assess
Bias
 Best Practices in Presenting Results
 Writing about Regression Modelling
APPENDIX A: A Brief Math Refresher
APPENDIX B: SPSS Basics
Answers to the OddNumbered "Practice What You Have Learned" Questions
Glossary
Index
Online Chapters
PART IV:
More Regression Modelling Techniques
14. Manipulating Independent Variables in Linear Regression (ONLINE)
 Using Interaction Variables in Linear Regression
 Statistical Significance Tests When Interaction Variables are Used
 Using Linear Regression to Predict
Curvilinear Relationships
 Transforming Skewed Variables
15. Logistic Regression Basics (ONLINE)
 Understanding the Conceptual Framework of Logistic Regression
 Odds and Log Odds
 Interpreting Logistic Regression Coefficients
 Standardized Coefficients

Statistical Significance Tests and Confidence Intervals
 Calculating Predicted Probabilities
 Assessing Model Fit for Logistic Regression
Online Chapters (Advanced Topics):
 Ch. 14: Manipulating Independent Variables in Regression: Interactions, Quadratics, and Transformations
 Ch. 15: Introduction to Logistic Regression
Instructor's Manual:
 Sample syllabus
For each chapter:
 Chapter
overview
 Key terms and definition
 510 suggested class activities
 510 suggested teaching aids
Test Generator:
For each chapter:
 4050 multiple choice questions
 4050 trueorfalse questions
 20 short answer questions
 15 multistep
questions
Image Bank:
 All figures, tables, examples, and formulas from the text
PowerPoint slides:
For each chapter:
 3035 lecture outline slides with animations and figures, tables, and formulas from the text
Student Study Guide:
For each chapter:

Chapter summary
 510 key terms and concepts
 Selfassessment quizzes
 20 multiple choice questions
 20 trueorfalse questions
Interactive Investigations:
For each chapter:
 One multistep interactive activity that helps students further investigate main
chapter theme
 Each investigation outlines a scenario and has students perform 710 steps/calculations
Interactive Calculation Practice Spreadsheets:
 Spreadsheets allow students to practice key calculations covered in the text (ChiSquare, Lambda, and Gamma)
SPSS Screencast
Videos:
 Screencast videos on major SPSS procedures taught in the book
SPSS Datasets:
 Datasets for "Practice Using Statistical Software (IBM SPSS)" questions
 Includes a dataset designed for use with the full version of IBM SPSS and a dataset designed for use with student
version of IBM SPSS software
Answers to oddnumbered "Practice Using Statistical Software (IBM SPSS) questions"
Appendix C: Key Formulas:
 List of all formulas found in the text along with page references
Ebook ISBN 9780199015221
Andrea M. Noack is an Associate Professor of Sociology at Ryerson University. She has taught social statistics, as well as both quantitative and qualitative research methods for more than a decade (and loves it!). Socialjustice activities and communityengaged learning approaches are a
regular part of her courses. In 2010, she was awarded the Ryerson Provost's Experiential Teaching Award for these efforts.
An Introduction to Statistics for Canadian Social Scientists  Michael Haan and Jenny Godley
Understanding Social Statistics  Lance W. Roberts, Jason Edgerton, Tracey Peter and Lori Wilkinson
Making Sense in the Social Sciences  Margot Northey, Lorne Tepperman and Patrizia Albanese
Social Research Methods  Alan Bryman and Edward Bell
The Research Process  Gary D. Bouma, Rod Ling and Lori Wilkinson
Simple Statistics  Terance D. Miethe and Jane Florence Gauthier
The Statistics Coach  Lance W. Roberts, Tracey Peter and Karen Kampen