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Quantitative Data Analysis: Doing Social Research to Test Ideas

Author:
Donald J. Treiman
Publisher: Jossey-Bass (a Wiley imprint)
Copyright: 2009
ISBN-13: 978-0-470-38003-1
Pages: 480; paperback
Price: $54.50
Supplements:Datasets and do-files

Comment from the Stata technical group

Quantitative Data Analysis, by Donald J. Treiman, is a well-written demonstration of how to answer questions using statistics. While the preface states that the book is “designed for a course to be taken after a first-year graduate statistics course in the social sciences”, the thought processes and techniques illustrated are useful and interesting to a much wider audience. The range of techniques is broad, ranging from simple advice for making tables readily readable through linear and logistic regression to log-linear and random-effects models.

Treiman writes using clear, precise language. This style makes the book accessible to readers from many fields and especially worthwhile to statistical consultants or others who work with clients of different backgrounds. The book is highly applied—the examples stem from published papers with real datasets. Many of the examples stem from Treiman’s long history of research in applied sociology, yet these examples are still interesting and approachable to those outside the field. The main material is nicely supplemented with “callouts” containing biographical and historical background information, as well as tips on Stata usage. Treiman also takes the time and effort to explain how to avoid common pitfalls of data analysis.

Because this is an applied book, there is little derivation of the mathematics behind the statistical techniques. This is not a drawback, though, because Treiman includes references and a large bibliography, which can be followed by those curious about statistical theory. Stata was used for the computation of all statistical results, and all the Stata do-files (Stata code) and datasets are available from the web. (In the book itself there is advice about how to use Stata for some analyses, but there is no explicit Stata code.)

Quantitative Data Analysis is worth a look for those wanting to see the applications of a wide variety of statistical techniques to a variety of problems or for those who are interested in the thought process behind assessing the results of techniques.


Table of contents

Tables, Figures, Exhibits, and Boxes
Preface
The Author
Introduction
1 Cross-Tabulations
What This Chapter Is About
Introduction to the Book via a Concrete Example
Cross-Tabulations
What This Chapter Has Shown
2 More on Tables
What This Chapter Is About
The Logic of Elaboration
Suppressor Variables
Additive and Interaction Effects
Direct Standardization
A Final Note on Statistical Controls Versus Experiments
What This Chapter Has Shown
3 Still More on Tables
What This Chapter is About
Reorganizing Tables to Extract New Information
When to Percentage a Table “Backwards”
Cross-Tabulations in Which the Dependent Variable is Represented by a Mean
Index of Dissimilarity
Writing About Cross-Tabulations
What This Chapter Has Shown
4 On the Manipulation of Data by Computer
What This Chapter Is About
Introduction
How Data Files Are Organized
Transforming Data
What This Chapter Has Shown
Appendix 4.A Doing Analysis Using Stata
Tips on Doing Analysis Using Stata
Some Particularly Useful Stata 10.0 Commands
5 Introduction to Correlation and Regression (Ordinary Least Squares)
What This Chapter Is About
Introduction
Quantifying the Size of a Relationship: Regression Analysis
Assessing the Strength of a Relationship: Correlation Analysis
The Relationship Between Correlation and Regression Coefficients
Factors Affecting the Size of Correlation (and Regression) Coefficients
Correlation Ratios
What This Chapter Has Shown
6 Introduction to Multiple Correlation and Regression (Ordinary Least Squares)
What This Chapter is About
Introduction
A Worked Example: The Determinants of Literacy in China
Dummy Variables
A Strategy for Comparisons Across Groups
A Bayesian Alternative for Comparing Models
Independent Validation
What This Chapter Has Shown
7 Multiple Regression Tricks: Techniques for Handling Special Analytic Problems
What This Chapter Is About
Nonlinear Transformations
Testing the Equality of Coefficients
Trend Analysis: Testing the Assumption of Linearity
Linear Splines
Expressing Coefficients as Deviations from the Grand Mean (Multiple Classification Analysis)
Other Ways of Representing Dummy Variables
Decomposing the Difference Between Two Means
What This Chapter Has Shown
8 Multiple Imputation of Missing Data
What This Chapter Is About
Introduction
A Worked Example: The Effect of Cultural Capital on Educational Attainment in Russia
What This Chapter Has Shown
9 Sample Design and Survey Estimation
What This Chapter Is About
Survey Samples
Conclusion
What This Chapter Has Shown
10 Regression Diagnostics
What This Chapter Is About
Introduction
A Worked Example: Societal Differences in Status Attainment
Robust Regression
Bootstrapping and Standard Errors
What This Chapter Has Shown
11 Scale Construction
What This Chapter Is About
Introduction
Validity
Reliability
Scale Construction
Errors-in-Variables Regression
What This Chapter Has Shown
12 Log-Linear Analysis
What This Chapter Is About
Introduction
Choosing a Preferred Model
Parsimonious Models
A Bibliographic Note
What This Chapter Has Shown
Appendix 12.A Derivation of the Effect Parameters
Appendix 12.B Introduction to Maximum Likelihood Estimation
Mean of a Normal Distribution
Log-Linear Parameters
13 Binomial Logistic Regression
What This Chapter Is About
Introduction
Relation to a Log-Linear Analysis
A Worked Logistic Regression Example: Predicting Prevalence of Armed Threats
A Second Worked Example: Schooling Progression Ratios in Japan
A Third Worked Example (Discrete-Time Hazard-Rate Models): Age at First Marriage
A Fourth Worked Example (Case–Control Models): Who Was Appointed to a Nomenklatura Position in Russia?
What This Chapter Has Shown
Appendix 13.A Some Algebra for Logs and Exponents
Appendix 13.B Introduction to Probit Analysis
14 Multinomial and Ordinal Logistic Regression and Tobit Regression
What This Chapter Is About
Multinomial Logit Analysis
Ordinal Logistic Regression
Tobit Regression (and Allied Procedures) for Censored Dependent Variables
Other Models for the Analysis of Limited Dependent Variables
What This Chapter Has Shown
15 Improving Causal Inference: Fixed Effects and Random Effects Modeling
What This Chapter Is About
Introduction
Fixed Effects Models for Continuous Variables
Random Effects Models for Continuous Variables
A Worked Example: The Determinants of Income in China
Fixed Effects Models for Binary Outcomes
A Bibliographic Note
What This Chapter Has Shown
16 Final Thoughts and Future Directions: Research Design and Interpretation Issues
What This Chapter Is About
Research Design Issues
The Importance of Probability Sampling
A Final Note: Good Professional Practice
What This Chapter Has Shown
Appendix A: Data Descriptions and Download Locations for the Data Used in This Book
Appendix B: Survey Estimation with the General Social Survey
References
Index
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