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.

**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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