Table of contents for Quantitative data analysis : doing social research to test ideas / Donald J. Treiman.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

List of Tables, Figures, and Exhibits	xv
Preface	xxxi
For instructors	xxxiv
Acknowledgments	xxxv
The Author	xxxvii
Introduction	1
Overview of Chapters	2
1.	Cross-tabulations	7
What this Chapter Is about	7
Introduction to the Book via a Concrete Example	7
Cross-tabulations	19
In Which Direction to Percentage the Table?	24
Control Variables	25
What this Chapter Has Shown	 36
2.	More on Tables 	37
What this Chapter Is about	 37
The Logic of Elaboration	 37
Suppressor Variables	 41
Additive and Interaction Effects	43
Direct Standardization	 47
Example 1: Religiosity by Militancy among U.S. Urban Blacks	 47
Example 2: Belief That Humans Evolved from Animals (Direct Standardization
With Two or More Control Variables)	 51
Example 3: Occupational Status by Race in South Africa	 61
Example 4: Level of Literacy by Urban Vs. Rural Residence in China	 65
A Final Note, on Statistical Controls vs. Experiments	 67
What this Chapter Has Shown	 71
3.	Still More on Tables	73
What this Chapter Is about	 73
Reorganizing Tables in Order to Extract New Information	.73
Collapsing Dimensions	74
Transforming Property Space	75
When to Percentage a Table ¿Backwards¿	78
Cross-tabulations in Which the Dependent Variable Is Represented by a Mean	 80
Inferences from Information on Missing Data	 85
Still Another Way of Presenting the Same Data	 85
Index of Dissimilarity	 89
Writing about Cross-tabulations	 92
What this Chapter Has Shown	 96
4.	On the Manipulation of Data by Computer	98
What this Chapter Is about	 98
Introduction	 98
How Files Are Organized	 100
A Digression on Card Desks and Card Image Files	105
Transforming Data	 110
Recoding	 110
Arithmetic Transformations	 114
Contingency Transformations	 115
Missing Data	 116
Analyzing Surveys with Missing Data	 119
What this Chapter Has Shown	 121
Appendix 4.A: Doing Analysis Using Stata 10.0	 123
Tips on Doing Analysis Using Stata	123
Do Everything with -do- Files	 123
Build in Extensive Checks of Your Work	 127
Document Your -do- File Exhaustively	 127
Include ¿Side¿ Computations in Your -do- File	 128
Rerun Your -do- File as a Final Check	 129
Make Active Use of the Stata Manuals	 129
Some Particularly Useful Stata 10.0 Commands	 129
Appendix 4.B: The Stata -for- Command	134
5.	Introduction to Correlation and Regression (Ordinary Least Squares)	135
What this Chapter Is about	135
Introduction	135
Quantifying the Size of a Relationship: Regression Analysis	136
Assessing the Strength of a Relationship: Correlation Analysis	139
The Relationship Between Correlation and Regression Coefficients	142
Factors Affecting the Size of Correlation (And Regression) Coefficients	143
Outliers and Leverage Points	143
Truncation	 145
Regression Toward the Mean	 147
Aggregation	 149
Correlation Ratios	 150
Testing Linearity	 153
What this Chapter Has Shown	 154
6.	Introduction to Multiple Correlation and Regression (Ordinary Least Squares)	155
What This Chapter Is About	155
Introduction	155
Metric Regression Coefficients	158
Testing the Significance of Individual Coefficients	160
Standardized Coefficients	162
Coefficient of Determination (R2)	 166
Standard Error of Estimate (Root MSE)	 169
A Worked Example: the Determinants of Literacy in China	 170
Graphic Representation of Results	 178
Dummy Variables	 181
A Strategy for Comparisons Across Groups	 185
Re-expressing Variables as Deviations from Their Means	 196
Testing Additional Hypotheses: Constraining Coefficients to Zero or 
to Equality	 197
A Bayesian Alternative for Comparing Models	 198
Independent Validation	 202
What this Chapter Has Shown	 204
7.	Multiple Regression Tricks: Techniques for Handling Special Analytic Problems	206
What this Chapter Is about	206
Non-linear Transformations	206
Curvilinear Relationships: Age and Income	207
Semi-log Transformations: Income	209
Mobility Effects	 214
Testing the Equality of Coefficients	 215
Trend Analysis: Testing the Assumption of Linearity	 218
Predicting Variation in Gender Role Attitudes over Time: A Worked Example	 220
Linear Splines	 223
A Worked Example: Trends in Educational Attainment over Time in the U.S.	 224
A Second Worked Example, with a Discontinuity: Quality of Education in 
China Before, During, and after the Cultural Revolution	229
Expressing Coefficients as Deviations from the Grand Mean (Multiple 
Classification Analysis)	 235
Other Ways of Representing Dummy Variables	 239
Effect Coding	 243
Contrast Coding	 244
Sequential Coefficients	 246
Decomposing the Difference Between Two Means	 247
A Worked Example: Factors Affecting Racial Differences in Educational 
Attainment	249
Additional Reading on Decomposing the Difference Between Means	256
What this Chapter Has Shown	 .256
9.	Multiple Imputation of Missing Data	258
What this Chapter Is about	258
Introduction	258
Casewise Deletion	260
Weighted Casewise Deletion	262
Mean Substitution	262
Hotdeck Imputation	264
Full Bayesian Multiple Imputation	265
A Worked Example: the Effect of Cultural Capital on Educational Attainment in 
Russia	268
Creating the Substantive Model	 269
Creating the Imputation Model	273
Comparing Casewise Deletion and Multiple Imputation Results	 275
What this Chapter Has Shown	 277
9.	Sample Design and Survey Estimation	278
What this Chapter Is about	278
Survey Samples	278
Simple Random Samples	279
Multistage Samples	281
Population Register Samples	289
Household Samples	 290
Random Walk Samples	 294
Design Effects	 296
Stratifying to Offset the Effect of Clustering	 301
Weighting	 303
Survey Estimation Using Stata	 307
Literacy in China	 308
Meff	 311
Deff	 312
Analysis of Subpopulations: Effect of Education and Race on Income among
 				Women	 316
 Combining GSS Datasets for Multiple Years	 320
Conclusion	 322
What this Chapter Has Shown	 323
10.	Regression Diagnostics	324
What this Chapter Is about	 324
Introduction	 324
A Worked Example: Societal Differences in Status Attainment Processes	 328
Preliminaries	 329
Leverage	 331
Outliers	 331
Influence	 332
Plots for Assessing Influence	 332
Added-variable Plots	 333
Residual-versus-fitted Plots and Formal Tests for Patterns in the Data.	 334
Robust Regression	 338
Bootstrapping Standard Errors	 340
What this Chapter Has Shown	 343
11.	Scale Construction	344
What this Chapter Is about	 344
Introduction	 344
Validity	 345
Reliability	 347
Scale Construction	 351
Additive Scaling	 351
Factor-based Scaling	 353
A Worked Example: Religiosity and Abortion Attitudes (Again)	 357
Seemingly Unrelated Regression	 366
Effect-proportional Scaling	 367
Errors-in-variables Regression	 374
What this Chapter Has Shown	 377
12. Log-linear Analysis	379
What this Chapter Is about	 379
Introduction	 379
Choosing a Preferred Model	 382
Model Selection Based on Goodness-of-fit	 383
Theory-based Model Selection	 390
Effect Parameters	 391
Another Worked Example: Anti-communist Sentiment	 393
Analysis Strategy	 394
Implementation	 395
Doing Log-linear Analysis with Polytomous Variable	399
Doing Log-linear Analysis with Individual Level Data	 401
More Parsimonious Models	 401
Topological, or Levels, Models	 407
Quasi-independence Models	 408
Quasi-symmetry Models	 409
Crossings Models	 410
Uniform Association Models 	 412
Linear-by-linear Association Models	 413
Row-effects (And Column-effects) Models	 414
Row-and-column-effects Model I	 417
Row-and-column-effects Model Ii (The RC or Log-multiplicative Model)	 418
Extensions	 423
A Bibliographic Note	 423
What this Chapter Has Shown	 425
Appendix 12.A: Derivation of the Effect Parameters	 426
Appendix 12.B: Introduction to Maximum Likelihood Estimation	 429
Mean of a Normal Distribution	 430
Log-linear Parameters	 432
13.	Binomial Logistic Regression	434
What this Chapter Is about	 434
Introduction	 434
Relation to Log-linear Analysis	 436
A Worked Logistic Regression Example: Predicting Prevalence of 
Armed Threats	438
A Second Worked Example: Schooling Progression Ratios in Japan	 454
A Third Worked Example (Discrete-time Hazard-rate Models): 
Age at First Marriage	 461
A Fourth Worked Example (Case-control Models): Who Became a Member
Of the Nomenklatura in Russia?	 469
What this Chapter Has Shown	 473
Appendix 13.A: Some Algebra for Logs and Exponents	 475
Appendix 13.B: Introduction to Probit Analysis	 476
14.	Multinomial and Ordinal Logistic Regression and Tobit Regression	483
 		What this Chapter Is about	483
Multinomial Logit Analysis	 483
A Worked Example: Foreign Language-competence in the Czech Republic	 485
Independence of Irrelevant Alternatives (IIA)	 490
Ordinal Logistic Regression	 492
A Worked Example: Political Party Identification in the U.S., 1998	 494
Converting the Logits to Y*-standardized Form	 497
Getting Predicted Percentages	 498
Constructing Odds Ratios.	 500
Comparisons to Other Estimating Procedures: -Gologit2-	 501
Ordinary Least Squares as an Alternative	 505
Tobit Regression (and Allied Procedures) for Censored Dependent Variables	 506
A Worked Example: Frequency of Sex	 510
Other Models for the Analysis of Limited Dependent Variables	 515
What this Chapter Has Shown	 516
15.	Improving Causal Inference: Fixed Effects and Random Effects Modeling	517
What this Chapter Is about	 517
Introduction	517
Fixed Effects Models for Continuous Variables	 519
Fundamental FE Equation	 520
Allowing the Slopes of the x¿s to Vary	 521
Testing Whether the Effects of the Time-invariant Variables Vary over Time	 522
Interactions Between Time-constant and Time-varying Variables	 522
Analyzing More than Two Time Points	 523
Fixing Effects Across Individuals Rather than over Time	 526
Limitations of Fixed Effects Approaches and Cautions to Keep in Mind	 527
Random Effects Models for Continuous Variables	 528
A Worked Example: the Determinants of Income in China	 531
Fixed Effects Models for Binary Outcomes	 536
Random Effects Models for Binary Outcomes	 538
A Worked Example with a Binary Outcome: the Effect of Migration on School Enrollment among South African Blacks	 538
A Bibliographic Note	 544
What this Chapter Has Shown	 544
16.	Final Thoughts and Future Directions: Research Design and Interpretation Issues	545
What this Chapter Is about	 545
Research Design Issues	 545
Comparisons Are the Essence	 545
Population Subgroups, Populations, and Historical Periods	 546
Natural Experiments	 552
Multilevel Analysis	 553
Endogeneity, Sample Selection Bias, and Other Threats to Correct Causal
 Inference	 555
Treatment Effects	 555
Instrumental Variables (IV) Regression	 557
Sample Selection Bias	 559
Heckman Selection Model	 560
Endogenous Switching Regression	 561
Propensity Score Matching	 562
Structural Equation Models	 565
The Importance of Probability Sampling	 570
The Concept of a ¿Super-population¿	 571
Pooling Data from Multiple Surveys	 574
A Final Note: Good Professional Practice	 576
Understand the Properties of Your Data	 576
Explore Alternatives to Your a Priori Hypotheses	 578
Conduct Sensitivity Analysis	 580
Document Your Work	 581
Error Checking	 583
What this Chapter Has Shown	 584
Appendix A: Data Descriptions and Download Locations for the Data Used in 
This Text	585
China	585
Eastern Europe	586
South Africa	587
U.S. General Social Survey	588
Appendix B: Doing Survey Estimation with the General Social Survey	590
Introduction	590
Analyzing Data from a Single Year	591
The 1972-1976 Block Quota Samples	591
The 1977-2002 Surveys, Except 1982 and 1987	592
The 1982 and 1987 Surveys with Oversamples of Blacks	594
The 2004 and 2006 Surveys	594
The FORMWT Variable	595
Pooling Surveys from More Than One Year	596
References	597
Index	635

Library of Congress Subject Headings for this publication:

Social sciences -- Research -- Statistical methods.
Sociology -- Research -- Statistical methods.
Sociology -- Statistical methods.
Social sciences -- Statistical methods -- Computer programs.