Social Statistics in Action

Every chapter includes:
- Learning Objectives
- Introduction
- Step-by-Step 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
- Cross-Tabulations
- 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 Ratio-Level Variables
- Describing the Centre of a Ratio-Level Variable: The Mean
- Describing the Dispersion of a Ratio-Level Variable: Standard Deviation and Variance
- Standardized Scores
- How Outliers Affect the Mean and Standard Deviation
- Using Histograms to Show the Dispersion of Ratio-Level 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
-- Error-Bar 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: T-Tests
- 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 T-Tests of Independent Means
-- One-Tailed and Two-Tailed Tests
-- Other Types of T-Tests
- Best Practices in Presenting Results
-- Writing about Tests of Statistical Significance
8. Assessing Relationships by Comparing Group Means: ANOVA Tests
- Means Comparison in Action
- One-Way ANOVA Tests
-- Calculating One-Way ANOVA Tests
-- Calculating a F-Statistic
- One-Way ANOVA Tests in Action
-- Other Types of ANOVA Tests
-- When to Use T-Tests and When to Use ANOVA tests
- Best Practices in Presenting Results
-- Writing about the Results of One-Way ANOVA Tests
9. Assessing Relationships between Categorical Variables
- Assessing the Magnitude of Relationships between Categorical Variables
-- Proportionate Reduction in Error Measures
- The Chi-Square Test of Independence
-- Chi-Square-Based Measures of Association
- Extending Cross-Tabulations: The Elaboration Model
-- Replication and Specification
-- Explanation and Interpretation
-- Suppression and Distortion
-- The Elaboration Model in Action
- Best Practices in Presenting Results
-- Writing about Cross-Tabulation Results
-- Showing Multiple Bivariate Relationships in a Single Table
10. Assessing Relationships between Ratio-Level Variables
- Describing Relationships between Ratio-Level Variables
- Pearson's Correlation Coefficient
-- Calculating Pearson's Correlations Coefficient
-- Reading Correlation Matrices
-- Interpreting T-Statistics for Pearson's Correlation Coefficient
- Spearman's Rank-Order Correlation Coefficient
-- Interpreting T-Statistics 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: R-Squared
- 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 Odd-Numbered "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
- 5-10 suggested class activities
- 5-10 suggested teaching aids
Test Generator:
For each chapter:
- 40-50 multiple choice questions
- 40-50 true-or-false questions
- 20 short answer questions
- 1-5 multi-step questions
Image Bank:
- All figures, tables, examples, and formulas from the text
PowerPoint slides:
For each chapter:
- 30-35 lecture outline slides with animations and figures, tables, and formulas from the text
Student Study Guide:
For each chapter:
- Chapter summary
- 5-10 key terms and concepts
- Self-assessment quizzes
-- 20 multiple choice questions
-- 20 true-or-false questions
Interactive Investigations:
For each chapter:
- One multi-step interactive activity that helps students further investigate main chapter theme
- Each investigation outlines a scenario and has students perform 7-10 steps/calculations
Interactive Calculation Practice Spreadsheets:
- Spreadsheets allow students to practice key calculations covered in the text (Chi-Square, 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 odd-numbered "Practice Using Statistical Software (IBM SPSS) questions"
Appendix C: Key Formulas:
- List of all formulas found in the text along with page references
E-book 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!). Social-justice activities and community-engaged 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 - Lori Wilkinson, Gary D. Bouma and Susan Carland
Simple Statistics - Terance D. Miethe and Jane Florence Gauthier
The Statistics Coach - Lance W. Roberts, Tracey Peter and Karen Kampen

Special Features

• Each chapter focuses on a social issue as a research example - including young people's wages, student loans, gender relations, racial inequalities, and mental health - helping students learn statistical procedures by demonstrating their application to everyday life.
• Extensive use of Canadian datasets throughout - including the Labour Force Survey, the General Social Survey, and the Canadian Community Health Survey - offering data and examples that are relevant to students in this country.
• Applied approach, including practice problems and activities in every chapter, gives students the opportunity to actively hone their skills using hand calculations as well as computer software to analyze and interpret data.
• Coverage of the most commonly used statistical models in the social sciences, including linear regression, multiple linear regression, and nested regression.
• SPSS examples help familiarize students with software that is used for statistical analysis in the social sciences.
• Vibrant four-colour design and photos bring social statistics to life and help students connect statistics to the world around them.
• An accessible, reader-friendly narrative features a conversational tone and easy-to-understand explanations, making the study of statistics approachable for students.
• An engaging box program invites students to think about social statistics from many angles.
• - Step-by-Step boxes guide students through the calculation of key statistical procedures, one step at a time.
• - How Does It Look in SPSS? boxes show SPSS output related to the topic under discussion and explain each part of the output.
• - Spotlight on Data boxes highlight Canadian datasets, including how data was collected and who information was collected from, showing the wide range of data available for analysis, as well as areas where data is currently lacking.
• - Hands-on Data Analysis boxes illustrate key data management skills - including how to deal with missing answers, grouping or combining answers, and selecting only some cases to analyze - equipping students with technical skills to analyze real data themselves.
• Student-friendly pedagogy in each chapter encourages active learning of key concepts.
• - Best Practices in Presenting Results sections outline strategies for clearly displaying and writing about statistical information, helping students present their statistical results in a meaningful way and use their results to support a larger argument.
• - Practice What You Have Learned problems give students an opportunity to analyze data, apply procedures, and calculate statistics by hand.
• - Practice Using Statistical Software (IBM SPSS) activities for students to practice using IBM SPSS to conduct data analysis and interpret results.
• - Check Your Understanding questions ask students to test their understanding of the key concepts in the chapter.
• - What You Have Learned sections summarize the key concepts of the chapter.
• - Marginal glossary terms and marginal flags for key formulas reinforce learning throughout.
• - Key formulas listed at the end of relevant chapters provide an at-a-glance guide.
• Appendices offers further learning support for students:
• - A Brief Math Refresher provides a quick review of some basic mathematical concepts and skills.
• - SPSS Basics provides an overview of fundamental IBM SPSS procedures.
• - Answers to odd-numbered "Practice What You Have Learned" problems help students check their work and track progress.
• Guide to frequently used formulas and list of frequently used symbols on the inside front and back covers provide a helpful quick-reference for students.