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# An Introduction to Statistics for Canadian Social Scientists

## byMichael Haan, Jenny Godley

### Paperback | October 5, 2016

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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.

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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

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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)