Helping firsttime students establish a solid foundation in analysis, this groundup Canadian text uses a conversational tone, a wealth of practice problems and exercises, and clear examples to teach the universal language of statistics. Fully uptodate, the third edition has been rigorously
revised to ensure the precision and accuracy of all concepts, equations, problems, and solutions.
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
Exponents
Logarithms
Data, Variables, and Observations
Levels of Measurement
 When Four Levels of Measurement Become Three . . . or Even Two
3. Univariate Statistics
Learning Objectives
Frequencies
 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
 NonMutually 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
OneTailed Assessments
Probabilities and the Normal Distribution
8. Sampling
Probability Samples
 Simple Random Sample
 Systematic Random Sample
 Stratified/Hierarchical Random Sample
 Cluster Sample
NonProbability/NonRandom 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
tDistribution
 What Is a Degree of Freedom?
 OneTailed Versus TwoTailed Estimates
The Sampling Distribution of Proportions
 Using Degrees of Freedom and the tDistribution 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?
OneTailed and TwoTailed Hypothesis Tests
The Return of Gossett: Student's tDistribution
Hypothesis Testing with One Small Sample and a Population

Calculating Confidence Intervals in the OneSample 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 TwoTailed Tests, Again
12. Bivariate Statistics for Nominal Data
Analysis with Two Nominal Variables
The ChiSquare 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/CrossTabulations
Kruskal's Gamma (y)
Somers' d
Kendall's Taub
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 tTest 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. OneWay Analysis of Variance
What
Is ANOVA?
The Sum of Squares: An Easier Way
The FDistribution
Is This New?
Limitations of ANOVA
Part III: Multivariate Techniques
16. Regression 1Modelling Continuous Outcomes
Ordinary LeastSquares 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 2Modelling 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 NonResponse Have on Results?
The Four Kinds of Item NonResponse
What to Do about Missing Data
1. Do Nothing:
ListWise and PairWise 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
tTable
Appendix C: ChiSquare
Appendix D: The Fdistribution
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
References
An Introduction to Statistics for Canadian Social Scientists: IBM SPSS Lab Manual
An Introduction to Statistics for Canadian Scientists: STATA Lab Manual
Index
2013 Alberta Study QuestionnaireCodebook (online)
Instructor's Manual:
 Chapter summaries
 Lecture outlines
 List of key terms and key formulas/symbols
 Suggested online teaching resources
 Suggestions for reallife examples of statistics at work (e.g. newspaper/magazine articles, newscasts, popular reporting,
etc.) (NEW)
PowerPoint slides:
For each chapter:
 1525 lecture outline slides
Test Generator:
For each chapter:
 2550 multiple choice questions
 1015 trueorfalse questions (NEW)
 510 short answer questions
Student Study Guide:
 Key concept
cue cards
 Links to statistical resources
 Links to sample data sets
 Links to statistician profiles
EBook (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 wellbeing and that of the
communities they join. This research is critical to understanding the relationship between location choice and socioeconomic status, and to preventing overurbanization 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