Quantitative Data Analysis
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"Quantitative Data Analysis, by Donald J. Treiman, isa well-written demonstration of how to answer questions usingstatistics. The range of techniques is broad, rangingfrom simple advice for making tables readily readable throughlinear and logistic regression to log-linear and random-effectsmodels... Treiman writes using clear, precise language...Treiman also takes the time and effort to explain how to avoidcommon pitfalls of data analysis... worth a look forthose wanting to see the applications of a wide variety ofstatistical techniques to a variety of problems or for those whoare interested in the thought process behind assessing the resultsof techniques." -- STATA Bookstore reviewMore details
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Content
Tables, Figures, Exhibits, and Boxes xi
Preface xxiii
The Author xxvii
Introduction xxix
1 CROSS-TABULATIONS 1
What This Chapter Is About 1
Introduction to the Book via a Concrete Example 2
Cross-Tabulations 8
What This Chapter Has Shown 19
2 MORE ON TABLES 21
What This Chapter Is About 21
The Logic of Elaboration 22
Suppressor Variables 25
Additive and Interaction Effects 26
Direct Standardization 28
A Final Note on Statistical Controls Versus Experiments 43
What This Chapter Has Shown 45
3 STILL MORE ON TABLES 47
What This Chapter Is About 47
Reorganizing Tables to Extract New Information 48
When to Percentage a Table "Backwards" 50
Cross-Tabulations in Which the Dependent Variable Is Represented by a Mean 52
Index of Dissimilarity 58
Writing About Cross-Tabulations 61
What This Chapter Has Shown 63
4 ON THE MANIPULATION OF DATA BY COMPUTER 65
What This Chapter Is About 65
Introduction 66
How Data Files Are Organized 67
Transforming Data 72
What This Chapter Has Shown 80
Appendix 4.A Doing Analysis Using Stata 80
Tips on Doing Analysis Using Stata 80
Some Particularly Useful Stata 10.0 Commands 84
5 INTRODUCTION TO CORRELATION AND REGRESSION (ORDINARY LEAST SQUARES) 87
What This Chapter Is About 87
Introduction 88
Quantifying the Size of a Relationship: Regression Analysis 89
Assessing the Strength of a Relationship: Correlation Analysis 91
The Relationship Between Correlation and Regression Coeffi cients 94
Factors Affecting the Size of Correlation (and Regression) Coeffi cients 94
Correlation Ratios 99
What This Chapter Has Shown 102
6 INTRODUCTION TO MULTIPLE CORRELATION AND REGRESSION (ORDINARY LEAST SQUARES) 103
What This Chapter Is About 103
Introduction 104
A Worked Example: The Determinants of Literacy in China 113
Dummy Variables 120
A Strategy for Comparisons Across Groups 124
A Bayesian Alternative for Comparing Models 133
Independent Validation 135
What This Chapter Has Shown 136
7 MULTIPLE REGRESSION TRICKS: TECHNIQUES FOR HANDLING SPECIAL ANALYTIC PROBLEMS 139
What This Chapter Is About 139
Nonlinear Transformations 140
Testing the Equality of Coeffi cients 147
Trend Analysis: Testing the Assumption of Linearity 149
Linear Splines 152
Expressing Coeffi cients as Deviations from the Grand Mean (Multiple Classifi cation Analysis) 164
Other Ways of Representing Dummy Variables 166
Decomposing the Difference Between Two Means 172
What This Chapter Has Shown 179
8 MULTIPLE IMPUTATION OF MISSING DATA 181
What This Chapter Is About 181
Introduction 182
A Worked Example: The Effect of Cultural Capital on Educational Attainment in Russia 187
What This Chapter Has Shown 194
9 SAMPLE DESIGN AND SURVEY ESTIMATION 195
What This Chapter Is About 195
Survey Samples 196
Conclusion 223
What This Chapter Has Shown 224
10 REGRESSION DIAGNOSTICS 225
What This Chapter Is About 225
Introduction 226
A Worked Example: Societal Differences in Status Attainment 229
Robust Regression 237
Bootstrapping and Standard Errors 238
What This Chapter Has Shown 240
11 SCALE CONSTRUCTION 241
What This Chapter Is About 241
Introduction 242
Validity 242
Reliability 243
Scale Construction 246
Errors-in-Variables Regression 258
What This Chapter Has Shown 261
12 LOG-LINEAR ANALYSIS 263
What This Chapter Is About 263
Introduction 264
Choosing a Preferred Model 265
Parsimonious Models 277
A Bibliographic Note 294
What This Chapter Has Shown 295
Appendix 12.A Derivation of the Effect Parameters 295
Appendix 12.B Introduction to Maximum Likelihood Estimation 297
Mean of a Normal Distribution 298
Log-Linear Parameters 299
13 BINOMIAL LOGISTIC REGRESSION 301
What This Chapter Is About 301
Introduction 302
Relation to Log-Linear Analysis 303
A Worked Logistic Regression Example:
Predicting Prevalence of Armed Threats 304
A Second Worked Example: Schooling Progression Ratios in Japan 314
A Third Worked Example (Discrete-Time Hazard-Rate Models): Age at First Marriage 318
A Fourth Worked Example (Case-Control Models):
Who Was Appointed to a Nomenklatura Position in Russia? 327
What This Chapter Has Shown 329
Appendix 13.A Some Algebra for Logs and Exponents 330
Appendix 13.B Introduction to Probit Analysis 330
14 MULTINOMIAL AND ORDINAL LOGISTIC REGRESSION AND TOBIT REGRESSION 335
What This Chapter Is About 335
Multinomial Logit Analysis 336
Ordinal Logistic Regression 342
Tobit Regression (and Allied Procedures) for Censored Dependent Variables 353
Other Models for the Analysis of Limited Dependent Variables 360
What This Chapter Has Shown 361
15 IMPROVING CAUSAL INFERENCE: FIXED EFFECTS AND RANDOM EFFECTS MODELING 363
What This Chapter Is About 363
Introduction 364
Fixed Effects Models for Continuous Variables 365
Random Effects Models for Continuous Variables 371
A Worked Example: The Determinants of Income in China 372
Fixed Effects Models for Binary Outcomes 375
A Bibliographic Note 380
What This Chapter Has Shown 380
16 FINAL THOUGHTS AND FUTURE DIRECTIONS: RESEARCH DESIGN AND INTERPRETATION ISSUES 381
What this Chapter is About 381
Research Design Issues 382
The Importance of Probability Sampling 397
A Final Note: Good Professional Practice 400
What This Chapter Has Shown 405
Appendix A: Data Descriptions and Download Locations for the Data Used in This Book 407
Appendix B: Survey Estimation with the General Social Survey 411
References 417
Index 431
