Introduction to Statistics for Canadian Social Scientists

Preface
Part I: Introduction and Univariate Statistics
1. Why Should I Want to Learn Statistics?
Learning Objectives
Introduction
Why Do So Many People Hate Statistics?
Learning to Think Statistically
Understanding the World with Numbers
If I Don't Plan to Ever Use Statistics in My Career, Should I Still Learn Them?
Organization of the Book
2. How Much Math Do I Need to Learn Statistics?
Learning Objectives
BEDMAS and the Order of Operation
Fractions and Decimals
Exponents
Logarithms
When 4 Levels of Measurement Become 3 . . . or Even 2
Practice Questions
3. Univariate Statistics
Learning Objectives
Frequencies
Rules for Creating Bar Charts
Translating Frequencies
Percentiles
Practice Questions
4. Introduction to Probability
Learning Objectives
Introduction
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 That Are Interchangeable
Mutually Exclusive Probabilities That Are Interchangeable
Continuous Probabilities
Conclusion
Practice Questions
5. The Normal Curve
Learning Objectives
The History of the Normal (Gaussian) Distribution
Illustrating the Normal Curve
Some Useful Terms for Describing Distributions
Practice Questions
6. Measures of Central Tendency and Dispersion
Learning Objectives
Measures of Central Tendency
Mode
Median
Measures of Variability
The Range
Mean Deviation
Variance and the Standard Deviation
Practice Questions
7. Standard Deviations, Standard Scores, and the Normal Distribution
Learning Objectives
Introduction
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
Practice Questions
8. Sampling
Learning Objectives
Introduction
Probability Samples
Simple Random Sample
Systematic Random Sample
Stratified/Hierarchical Random Sample
Cluster Sampling
Non-Probability/Non-Random Sampling Strategies
Convenience Sample
Snowball Sample
Quota Sample
Sampling Error
Tips for Reducing Sampling Error
Practice Questions
9. Generalizing from Samples to Populations
Learning Objectives
Introduction
Confidence Intervals
The T-Distribution
What Is a Degree of Freedom?
One-Tailed versus Two-Tailed Estimates
Using Degrees of Freedom and the T-Distribution to Estimate Population Proportions
Practice Questions
Part II: Bivariate Statistics
10. Using the T-Distribution to Compare the Means of Population Subgroups to Population Means
Learning Objectives
Introduction
Measuring Association between Dummy and Interval-Ratio Variables: The One Sample T-Test
The Return of Gossett: The Student's t-Distribution
Calculating Confidence Intervals in the One Sample Case
Single Sample Proportions
Measuring Association between Dummy and Interval-Ratio Variables with the same Group Measured Twice
Practice Questions
11. Measuring Association between Dummy and Interval-Ratio Variables: T-Tests with Two Samples
Learning Objectives
Introduction
Comparing Proportions with Two Samples
One- and Two-Tailed Tests, Again
Practice Questions
12. Bivariate Statistics for Nominal Data
Learning Objectives
Introduction
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
Practice Questions
13. Bivariate Statistics for Ordinal Data
Learning Objectives
Introduction
Contingency Tables/Crosstabulations
Kruskal's Gamma
Spearman's Rho
Somer's d
Kendall's Tau-b
Conclusion: Which One to Use?
Practice Questions
14. Bivariate Statistics for Interval/Ratio Data
Learning Objectives
Introduction
Pearson's r: The Correlation Coefficient
A Rough Interpretation of r
A Visual Representation of r
Explained Variance
A More Precise Interpretation of r
The Correlation Matrix
Using a t-Test to Assess the Significance of r
Assumptions of Linearity
Measuring Association between Interval/Ratio and Nominal or Ordinal Variables: Using the Lowest Common Measure of Association
Practice Questions
15. One-Way Analysis of Variance
Learning Objectives
Introduction
What Is ANOVA?
The Sum of Squares: An Easier Way
The F-Distribution
Is This New?
Limitations of ANOVA
Practice Questions
Part III: Multivariate Techniques
16. Regression 1 - Modeling Continuous Outcomes
Learning Objectives
Introduction
Ordinary Least Squares Regression: The Idea
Regression: The Formula
Multiple Regression
Standardized Partial Slopes (beta-weights)
The Multiple Correlation Coefficient
Requirements of Ordinary Least Squares Regression
Dummy Variables
Interpreting Dummy Variable Coefficients
A Final Note on OLS Regression
Practice Questions
17. Regression 2 - Modeling Discrete/Qualitative Outcomes with Logistic Regression
Learning Objectives
Introduction
Logistic Regression: The Idea
Logistic Regression: The Formula
Modeling Logistic Regression
Interpreting the Coefficients of a Logistic Regression Equation
A Note on Estimating Logistic Regressions
Practice Questions
Part IV: Advanced Topics
18. Regression Diagnostics
Learning Objectives
Introduction
When Ordinary Least Squares Regression Goes Wrong
Influential Cases
Cook's Distance
Homoscedasticity
Collinearity (aka Multicollinearity)
Identifying and Dealing with Multicollinearity
Conclusion
Practice Questions
19. Strategies for Dealing with Missing Data
Learning Objectives
Introduction
What Effect Does Non-response Bias Have?
The Four Forms of Missing Data
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 over Single Imputation
Multiple Imputation - Disadvantages
What Difference Does It Make?
Appendix A: Area Under the Normal Curve
Appendix B: The Student's Table
Appendix C: Chi-Square
Appendix D: The F-Distribution
Appendix E: Random Numbers between 1 and 1000
Appendix F: Equation Summary
Appendix G: Some Statistical Terms to Remember
Answer Key for Practice Questions
Solution Key for Boxes
Bibliography
A Companion Lab Manual for SPSS
A Companion Lab Manual for Strata
Index

Test Bank
PowerPoint Slides

Michael Haan is Assistant Professor in the Department of Sociology at the University of Alberta. He has contributed papers to various journals, including Social History/Histoire Sociale, Critical Social Policy, International Migration Review, and Social Science History.

Special Features

• Canadian. Canadian scholarship and examples are used to teach the universal language of statistics, making the text relevant and relatable for Canadian social sciences students.
• "Knowledge-Through-Discovery" approach. The text takes the approach that students learn by doing, and as such provides them with "hands-on" cases and questions to work through. The result is that students move beyond mere memorization to understand how statistics can be used in practical research and analysis situations.
• Lab exercises. A series of engaging lab exercises help to bridge the gap between theory and practice for students.
• Mathematics refresher. Includes an overview of basic mathematics for students who may be uncomfortable or out-of-practice with their math skills.
• Statistical software manuals. SPSS and STATA lab manuals are included in the Appendix to outline the basic software packages that are used for statistical analysis in the social sciences.
• Useful references. The Appendix includes Area under the Normal Curve, Chi-Square, The Student's T Table, The F-Distribution, Random Numbers between 1 and 1,000, Equation Summary, and more, which provide helpful, quick reference guides for students.
• Outstanding pedagogy. Learning objectives, chapter introductions, and case boxes highlight key concepts for students and make the text easy to navigate.