An Introduction to Statistics for Canadian Social Scientists by Michael HaanAn Introduction to Statistics for Canadian Social Scientists by Michael Haan

An Introduction to Statistics for Canadian Social Scientists

byMichael Haan, Jenny Godley

Paperback | October 5, 2016

Pricing and Purchase Info


Earn 450 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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 rigorouslyrevised to ensure the precision and accuracy of all concepts, equations, problems, and solutions.
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 t...
Title:An Introduction to Statistics for Canadian Social ScientistsFormat:PaperbackDimensions:496 pages, 10 × 8 × 0.72 inPublished:October 5, 2016Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199020590

ISBN - 13:9780199020591

Look for similar items by category:


Table of Contents

Note: Each chapter includes:- Introduction (Except chapters 2, 3, and 5)- Conclusion- Glossary terms- Practice questionsPart I: Introduction and Univariate Statistics1. 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 Book2. How Much Math Do I Need to Learn Statistics?BEDMAS and the Order of OperationsFractions and DecimalsExponentsLogarithmsData, Variables, and ObservationsLevels of Measurement- When Four Levels of Measurement Become Three . . . or Even Two3. Univariate StatisticsLearning ObjectivesFrequencies- Translating FrequenciesRules for Creating Bar ChartsRates and RatiosPercentages and Percentiles4. Introduction to ProbabilitySome Necessary Terminology- Sample Space- Random Variables- Trials and Experiments- The Law of Large NumbersTypes 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 Probabilities5. The Normal CurveThe History of the Normal (Gaussian) DistributionIllustrating the Normal CurveSome Useful Terms for Describing Distributions6. Measures of Central Tendency and DispersionMeasures of Central Tendency- Mode- Median- MeanMeasures of Variability- Range- Mean Deviation- Variance and the Standard Deviation7. Standard Deviations, Standard Scores, and the Normal DistributionHow Does the Standard Deviation Relate to the Normal Curve?- More on the Normal DistributionAn Extension of the Standard Deviation: The Standard ScoreOne-Tailed AssessmentsProbabilities and the Normal Distribution8. SamplingProbability Samples- Simple Random Sample- Systematic Random Sample- Stratified/Hierarchical Random Sample- Cluster SampleNon-Probability/Non-Random Sampling Strategies- Convenience Sample- Snowball Sample- Quota SampleSampling Error- Tips for Reducing Sampling Error9. Generalizing from Samples to PopulationsThe Sample Distribution of Means and the Central Limit TheoremConfidence IntervalsThe t-Distribution- What Is a Degree of Freedom?- One-Tailed Versus Two-Tailed EstimatesThe Sampling Distribution of Proportions- Using Degrees of Freedom and the t-Distribution to Estimate Population Proportions- The Binomial DistributionPart II: Bivariate Statistics10. Testing Hypotheses: Comparing Large and Small Samples to a Known PopulationWhat's a Hypothesis?One-Tailed and Two-Tailed Hypothesis TestsThe Return of Gossett: Student's t-DistributionHypothesis Testing with One Small Sample and a Population- Calculating Confidence Intervals in the One-Sample Case- Single Sample ProportionsMeasuring Association with the Same Group Measured Twice11. Testing Hypotheses: Comparing Two SamplesThe Standard Error of the Difference between MeansComparing Proportions with Two SamplesOne- and Two-Tailed Tests, Again12. Bivariate Statistics for Nominal DataAnalysis with Two Nominal VariablesThe Chi-Square Test of SignificanceMeasures of Association for Nominal Data- Phi- Cramer's V- The Proportional Reduction of Error: Lambda13. Bivariate Statistics for Ordinal DataContingency Tables/Cross-TabulationsKruskal's Gamma (y)Somers' dKendall's Tau-bSpearman's rhoWhat about Statistical Significance?Conclusion: Which One to Use?14. Bivariate Statistics for Interval/Ratio DataPearson'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 rThe Correlation MatrixUsing a t-Test to Assess the Significance of rWhat 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 Association15. One-Way Analysis of VarianceWhat Is ANOVA?The Sum of Squares: An Easier WayThe F-DistributionIs This New?Limitations of ANOVAPart III: Multivariate Techniques16. Regression 1-Modelling Continuous OutcomesOrdinary Least-Squares Regression: The IdeaOnward from Bivariate Correlation: Multivariate Analysis- Regression: The FormulaMultiple Regression- Standardized Partial Slopes (Beta Weights)- The Multiple Correlation CoefficientRequirements/Assumptions of Ordinary Least Squares RegressionCreating and Working with Dummy Variables- Interpreting Dummy Variable CoefficientsInference and RegressionConclusion: A Final Note on OLS Regression17. Regression 2-Modelling Discrete/Dichotomous Outcomes with Logistic RegressionLogistic Regression: The IdeaLogistic Regression: The FormulaModelling Logistic RegressionInterpreting the Coefficients of a Logistic Regression EquationA Note on Estimating Logistic RegressionsPart IV: Advanced Topics18. Regression DiagnosticsWhen Ordinary Least Squares Regression Goes Wrong- Influential Cases as a Source of Error- Heteroscedasticity as a Source of Error- Multicollinearity as a Source of Error19. Strategies for Dealing with Missing DataWhat Effect Does Non-Response Have on Results?The Four Kinds of Item Non-ResponseWhat to Do about Missing Data1. Do Nothing: List-Wise and Pair-Wise Deletion2. Do Something: Single Imputation Strategies3. Do Multiple Things: Multiple ImputationMultiple Imputation: Advantages and Disadvantages over Single ImputationAppendix A: Area under the Normal CurveAppendix B: The Student's t-TableAppendix C: Chi-SquareAppendix D: The F-distributionAppendix E: Area under the Normal Curve: A Condensed VersionAppendix F: Random Numbers between 1 and 1,000Appendix G: Summary of Equations and SymbolsSolutions Keys for Practice QuestionsSolution for Keys for BoxesReferencesAn Introduction to Statistics for Canadian Social Scientists: IBM SPSS Lab ManualAn Introduction to Statistics for Canadian Scientists: STATA Lab ManualIndex2013 Alberta Study Questionnaire-Codebook (online)