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An Introduction to Statistics for Canadian Social Scientists

Third Edition

Michael Haan and Jenny Godley

Helping first-time students establish a solid foundation in analysis, this ground-up Canadian text uses a conversational tone, a wealth of practice problems and exercises, and clear examples to teach the universal language of statistics. Fully up-to-date, the third edition has been rigorously revised to ensure the precision and accuracy of all concepts, equations, problems, and solutions.

Readership : An Introduction to Statistics for Canadian Social Scientists, third edition, is suited to students in their second, third, or fourth years of study in universities and colleges. Introduction to statistics, or quantitative methods, is offered primarily out of sociology departments, but is also found in social science, political science, criminology, and communications departments.

Note: Each chapter includes:
- Introduction (Except chapters 2, 3, and 5)
- Conclusion
- Glossary terms
- Practice questions
Part I: Introduction and Univariate Statistics
1. Why Should I Want to Learn Statistics?
Why Do So Many People Dislike Statistics?
When Did People Start to Think Statistically?
If I Don't Plan to Use Statistics in My Career, Should I Still Learn About Them?
Organization of the Book
2. How Much Math Do I Need to Learn Statistics?
BEDMAS and the Order of Operations
Fractions and Decimals
Data, Variables, and Observations
Levels of Measurement
- When Four Levels of Measurement Become Three . . . or Even Two
3. Univariate Statistics
Learning Objectives
- Translating Frequencies
Rules for Creating Bar Charts
Rates and Ratios
Percentages and Percentiles
4. Introduction to Probability
Some Necessary Terminology
- Sample Space
- Random Variables
- Trials and Experiments
- The Law of Large Numbers
Types of Probabilities
- Empirical versus Theoretical Probabilities
- Discrete Probabilities
- The Probability of Unrelated Events
- The Probability of Related Events
- Mutually Exclusive Probabilities
- Non-Mutually Exclusive Probabilities
- Continuous Probabilities
5. The Normal Curve
The History of the Normal (Gaussian) Distribution
Illustrating the Normal Curve
Some Useful Terms for Describing Distributions
6. Measures of Central Tendency and Dispersion
Measures of Central Tendency
- Mode
- Median
- Mean
Measures of Variability
- Range
- Mean Deviation
- Variance and the Standard Deviation
7. Standard Deviations, Standard Scores, and the Normal Distribution
How Does the Standard Deviation Relate to the Normal Curve?
- More on the Normal Distribution
An Extension of the Standard Deviation: The Standard Score
One-Tailed Assessments
Probabilities and the Normal Distribution
8. Sampling
Probability Samples
- Simple Random Sample
- Systematic Random Sample
- Stratified/Hierarchical Random Sample
- Cluster Sample
Non-Probability/Non-Random Sampling Strategies
- Convenience Sample
- Snowball Sample
- Quota Sample
Sampling Error
- Tips for Reducing Sampling Error
9. Generalizing from Samples to Populations
The Sample Distribution of Means and the Central Limit Theorem
Confidence Intervals
The t-Distribution
- What Is a Degree of Freedom?
- One-Tailed Versus Two-Tailed Estimates
The Sampling Distribution of Proportions
- Using Degrees of Freedom and the t-Distribution to Estimate Population Proportions
- The Binomial Distribution
Part II: Bivariate Statistics
10. Testing Hypotheses: Comparing Large and Small Samples to a Known Population
What's a Hypothesis?
One-Tailed and Two-Tailed Hypothesis Tests
The Return of Gossett: Student's t-Distribution
Hypothesis Testing with One Small Sample and a Population
- Calculating Confidence Intervals in the One-Sample Case
- Single Sample Proportions
Measuring Association with the Same Group Measured Twice
11. Testing Hypotheses: Comparing Two Samples
The Standard Error of the Difference between Means
Comparing Proportions with Two Samples
One- and Two-Tailed Tests, Again
12. Bivariate Statistics for Nominal Data
Analysis with Two Nominal Variables
The Chi-Square Test of Significance
Measures of Association for Nominal Data
- Phi
- Cramer's V
- The Proportional Reduction of Error: Lambda
13. Bivariate Statistics for Ordinal Data
Contingency Tables/Cross-Tabulations
Kruskal's Gamma (y)
Somers' d
Kendall's Tau-b
Spearman's rho
What about Statistical Significance?
Conclusion: Which One to Use?
14. Bivariate Statistics for Interval/Ratio Data
Pearson's r : The Correlation Coefficient
- A Rough Interpretation of r
- A Visual Representation of r
- What r Tells Us about Explained Variance
- A More Precise Interpretation of r
The Correlation Matrix
Using a t-Test to Assess the Significance of r
What to Do When Your Independent and Dependent Variables Are Measured at Different Levels of Measurement
- Measuring Association between Interval/Ratio and Nominal or Ordinal Variables: Using the Lowest Common Measure of Association
15. One-Way Analysis of Variance
What Is ANOVA?
The Sum of Squares: An Easier Way
The F-Distribution
Is This New?
Limitations of ANOVA
Part III: Multivariate Techniques
16. Regression 1-Modelling Continuous Outcomes
Ordinary Least-Squares Regression: The Idea
Onward from Bivariate Correlation: Multivariate Analysis
- Regression: The Formula
Multiple Regression
- Standardized Partial Slopes (Beta Weights)
- The Multiple Correlation Coefficient
Requirements/Assumptions of Ordinary Least Squares Regression
Creating and Working with Dummy Variables
- Interpreting Dummy Variable Coefficients
Inference and Regression
Conclusion: A Final Note on OLS Regression
17. Regression 2-Modelling Discrete/Dichotomous Outcomes with Logistic Regression
Logistic Regression: The Idea
Logistic Regression: The Formula
Modelling Logistic Regression
Interpreting the Coefficients of a Logistic Regression Equation
A Note on Estimating Logistic Regressions
Part IV: Advanced Topics
18. Regression Diagnostics
When Ordinary Least Squares Regression Goes Wrong
- Influential Cases as a Source of Error
- Heteroscedasticity as a Source of Error
- Multicollinearity as a Source of Error
19. Strategies for Dealing with Missing Data
What Effect Does Non-Response Have on Results?
The Four Kinds of Item Non-Response
What to Do about Missing Data
1. Do Nothing: List-Wise and Pair-Wise Deletion
2. Do Something: Single Imputation Strategies
3. Do Multiple Things: Multiple Imputation
Multiple Imputation: Advantages and Disadvantages over Single Imputation
Appendix A: Area under the Normal Curve
Appendix B: The Student's t-Table
Appendix C: Chi-Square
Appendix D: The F-distribution
Appendix E: Area under the Normal Curve: A Condensed Version
Appendix F: Random Numbers between 1 and 1,000
Appendix G: Summary of Equations and Symbols
Solutions Keys for Practice Questions
Solution for Keys for Boxes
An Introduction to Statistics for Canadian Social Scientists: IBM SPSS Lab Manual
An Introduction to Statistics for Canadian Scientists: STATA Lab Manual
2013 Alberta Study Questionnaire-Codebook (online)

Instructor's Manual:
- Chapter summaries
- Lecture outlines
- List of key terms and key formulas/symbols
- Suggested online teaching resources
- Suggestions for real-life examples of statistics at work (e.g. newspaper/magazine articles, newscasts, popular reporting, etc.) (NEW)
PowerPoint slides:
For each chapter:
- 15-25 lecture outline slides
Test Generator:
For each chapter:
- 25-50 multiple choice questions
- 10-15 true-or-false questions (NEW)
- 5-10 short answer questions
Student Study Guide:
- Key concept cue cards
- Links to statistical resources
- Links to sample data sets
- Links to statistician profiles
E-Book (ISBN 9780199020607)

Michael Haan is Associate Professor in the Department of Sociology and Canada Research Chair in Migration and Ethnic Relations at Western University. He studies why immigrants make the location choices they do, and what impact these choices have on both their well-being and that of the communities they join. This research is critical to understanding the relationship between location choice and socio-economic status, and to preventing over-urbanization in some parts of Canada and population decline in others. Haan has contributed to a number of journals, including Social History/Histoire sociale, Critical Social Policy, International Migration Review, and Social Science History. He is also sole author of the first two editions of An Introduction to Statistics for Canadian Social Scientists, published by OUP Canada.

Jenny Godley is Associate Professor in the Department of Sociology at the University of Calgary where she also currently serves as Director of Undergraduate Studies. Her research interests include health, social networks, the life course, demography, gender, and qualitative and mixed methods. She teaches introductory social statistics as well as a graduate seminar on quantitative research methods. Godley is widely published in journals, including the Canadian Review of Sociology, Health and Place, American Journal of Community Psychology, Alberta Journal of Educational Research, The Journal of Research Administration, and The International Journal of Public Health.

Understanding Social Statistics - Lance W. Roberts, Jason Edgerton, Tracey Peter and Lori Wilkinson
Intermediate Social Statistics - Robert Arnold
Simple Statistics - Terance D. Miethe and Jane Florence Gauthier
The Statistics Coach - Lance W. Roberts, Tracey Peter and Karen Kampen
Social Research Methods - Alan Bryman and Edward Bell
The Research Process - Gary D. Bouma, Rod Ling and Lori Wilkinson
Making Sense in the Social Sciences - Margot Northey, Lorne Tepperman and Patrizia Albanese

Special Features

  • Approaches social statistics from a Canadian perspective, offering data, examples, and references that will be relatable to students in this country.
  • Learning-through-discovery approach uses straightforward language, examples, and offers ample practice problems and exercises to encourage hands-on learning.
  • Mathematics refresher (Ch. 2) provides an overview of basic concepts for students who may be out of practice or uncomfortable with their math skills.
  • Statistical software lab manuals (IBM SPSS and STATA) help familiarize students with the basic software packages that are used for statistical analysis in the social sciences.
  • Everyday Statistics boxes offer examples of the real-world applications of statistics and conclude with a critical-thinking question to encourage students to consider how statistics impact our daily lives.
  • Guide to frequently used formulas and symbols on the inside front and back covers provide a quick-reference for students.
  • List of key terms provide students with a review of the concepts covered in each chapter.
New to this Edition
  • Rigorous technical check and review conducted by statistical experts ensures accuracy in all key concepts and exercises.
  • Co-authored by Jenny Godley, who brings her expertise to this edition to give students a text that is mathematically and conceptually sound.
  • Revised lab manuals include new screenshots of IBM SPSS and STATA - plus a real data set (the 2013 Alberta survey) - to help students understand how statistics can be used in practical research and analysis situations.
  • Chapter conclusions help tie the material together and create a fluid reading experience for students.
  • New content - including additional key terms (Ch. 1); concepts of unit of analysis, datasets, cases, and the process of coding variables (Ch. 2); an introduction to histograms (Ch. 3); and an explanation of exp(b) (Ch. 17) - gives students a more thorough overview of essential topics.
  • Expanded explanations of BEDMAS (Ch. 2); probabilities (Ch. 4); measures of central tendency (Ch. 6); treating a random sample (Ch. 8); interpreting ANOVA results (Ch. 15), and more, help to clarify complex ideas.