Quantitative Data Analysis: Doing Social Research To Test Ideas

This book is an accessible introduction to quantitative data analysis, concentrating on the key issues facing those new to research, such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results. Each chapter includes illustrative examples and a set of exercises that allows readers to test their understanding of the topic. The book, written for graduate students in the social sciences, public health, and education, offers a practical approach to making sociological sense out of a body of quantitative data. The book also will be useful to more experienced researchers who need a readily accessible handbook on quantitative methods.

Donald J. Treiman, PhD, is Distinguished Professor, Department of Sociology, University of California, Los Angeles. He was also, until recently, director of the California Center for Population Research.

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 Classifi cation 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 Show.

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 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.