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Price: $76.95

Format:
Paperback 352 pp.
154 figures; 69 tables, 7" x 9"

ISBN-10:
0199012075

ISBN-13:
9780199012077

Copyright Year:
2015

Imprint: OUP Canada

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Intermediate Social Statistics

A Conceptual and Graphic Approach

Robert Arnold

Offering a rich blend of accessible examples, illustrative graphics, and easy-to-follow instruction, Intermediate Social Statistics takes a conceptual approach to guide students through a broad range of statistical methods and ensure they understand both how to apply statistical techniques and why such techniques are used in the social sciences.

Readership : University courses on intermediate and advanced statistics and quantitative methods offered out of sociology departments and other programs in the social sciences.

Reviews

  • "Intermediate Social Statistics approaches concepts in a way that emphasizes the rationale behind statistical methods. The instruction is great, and the concepts are supported by good examples and well-represented graphically. Students will get everything they need from this text."
    --Tom Buchanan, Mount Royal University

  • "I was impressed with the thorough approach. . . . Few books have as many examples completely worked out as this book does."
    --Kenneth MacKenzie, McGill University

Part One: Descriptive Statistics for One Variable
1. Levels of Measurement
Learning Objectives
The Stevens Classification
Two Related Classifications
Graphing
Summary
Review Questions on Levels of Measurement
Notes
2. Measures of Central Tendency
Learning Objectives
Definitions and Notation
Links to Levels of Measurement
Potentially Unstable Results
Measures of Central Tendency as Averages
Summary
Review Questions on Central Tendency
Notes
3. Measures of Dispersion
Learning Objectives
Measures for Nominal Variables
Quantile-Based Measures of Dispersion
Summary
Review Questions on Measures of Dispersion
Notes
4. Describing the Shape of a Distribution
Learning Objectives
Modes
Skewness and Kurtosis
Formulae for Skewness and Kurtosis
Summary
Review Questions on Shapes of Distributions
Notes
5. Summarizing a Distribution
Learning Objectives
The Five-Number Summary
Graphing a Distribution
Creating a Mean and Standard Deviation Table
Summary
Review Questions on Summarizing a Distribution
Part Two: Statistical Inference
6. Sampling Distributions
Learning Objectives
The Normal Distribution
The t Distribution
The Chi-Square Distribution
Relations Among the Normal, t, and Chi-square Distributions
The Effect of Sample Size
Summary
Review Questions on Sampling Distributions
Notes
7. The Standard Model of Statistical Inference
Learning Objectives
Central Ideas
Summary
Review Questions on the Standard Model of Statistical Inference
Note
8. The Bayesian Alternative
Learning Objectives
Frequentist and Personal Probabilities
Bayes' Theorem
Estimating a Proportion
Credible Intervals
Numerical Equivalence to Standard Results
Summary
Review Questions on the Bayesian Alternative
Notes
Part Three: Measures of Association
Association and Independence
9. Measures for Nominal and Ordinal Variables
Learning Objectives
PRE Measures
The Odds Ratio
Summary
Review Questions on Measures for Nominal and Ordinal Variables
Notes
10. Pearson's r
Learning Objectives
Explaining the Formula
Correlation Matrices
Graphic Displays for Interval or Ratio Data
Summary
Review Questions on Pearson's r
Notex
Part Four: Examining Crosstabulations
11. Two-Way Tables
Learning Objectives
Reading a Crosstabulation
Heavy and Light Cells
Setting Up a Crosstabulation for Presentation
Further Graphic Methods of Clarifying a Crosstabulation
Summary
Review Questions on Heavy and Light Cells and Graphic Methods
Notes
12. Conditional Tables
Learning Objectives
The Columbia Approach
Specification
Causal Chains
Spurious Association
Distortion
Conditional Probabilities
An Illustration with Polytomies
Summary
Review Questions on Conditional Tables
Notes
Part Five: Regression
13. Bivariate Regression
Learning Outcomes
Origins
The Principle of Least Squares
The Form of the Equation
Interpreting b
Graphic Display for Bivariate Regression
Non-linear Trends
Summary
Review Questions on Bivariate Regression
Notes
14. Multiple Regression
Learning Objectives
Why Multiple Regression (MR)?
Obtaining b's in Multiple Regression
Interpreting b's in Multiple Regression
Getting the Right Variables into the Equation
Interpreting a Table of Regression Results
Interaction Terms
Setting Up a Regression Table
Graphs Presenting Regression Results
The Special Case of Analysis of Variance (ANOVA)
Summary
Review Questions on Multiple Regression and ANOVA
Notes
15. Path Analysis
Learning Objectives
A Famous Path Model
Setting Up Path Diagrams
Equations
Decomposing a Correlation
Steps in Decomposing a Correlation
A Further Example
Summary
Review Questions on Path Analysis
Notes
16. Logistic Regression
Learning Objectives
Why Logistic Regression?
Logits
Interpreting the b's
A Sample Logistic Regression Table
An Extension of the Logistic Model: Multinomial Regression
Summary
Review Questions on Logistic Regression
Notes
Appendix A: Going a Step Further
A1: Minimizing the Sum of Squared Deviations
A2: The Mean and Standard Deviation of Z-Scores
A3: The Standard Deviation of a Proportion
Appendix B: Some Additional Explanations
B1: Basic Notes on Logarithms
B2: Obtaining Expected Values for Chi-Square
B3: Another Form of Bayesian Hypothesis Testing
Glossary of Statistical Terms
Credits
References
Index

Image Bank:
Images and figures from the text available for download
Solutions Manual:
Answer key for in-text review questions
E-Book (ISBN 9780199012084):
Available through CourseSmart.com

Robert Arnold worked for 13 years in applied research before becoming a university professor. He is currently an associate professor in the Department of Sociology, Anthropology, and Criminology at the University of Windsor. His primary area of interest is quantitative methodology and he has taught methods and statistics from the undergraduate to the doctoral level for the last 25 years. He has also taught introductory sociology, social psychology, and sociology of the contemporary family, which he has offered at the University of Windsor for the past six years. He has served as chair of the Advisory Committee for the University of Windsor's M.A. in Social Data Analysis (MASDA) and has co-authored academic papers in criminology and sociology of health as well as in program evaluation.

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Special Features

  • A practical, conceptual approach explains why we use particular statistics, what they tell us, how they can mislead us, and how to avoid being misled.
  • Canadian examples - adapted from the national Census, the General Social Survey, the Canadian Election Survey, and other well-respected sources - make key lessons highly relevant and relatable to Canadian students.
  • Two professional technical checks by a statistical expert ensure accuracy in all key concepts and exercises.
  • Abundant tables and graphs help students quickly and thoroughly grasp complicated concepts.
  • Classroom-tested lessons - developed by the author over 18 years of teaching - ensure that all examples, explanations, and exercises are clear and useful to students.