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Statistical Models for Causal Analysis Book

Statistical Models for Causal Analysis
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  • Statistical Models for Causal Analysis
  • Written by author Robert D. Retherford
  • Published by Wiley, John & Sons, Incorporated, December 1993
  • Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regressio
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1Bivariate Linear Regression1
1.1Terminology1
1.2Fitting a Least-Squares Line2
1.3The Least-Squares Line as a Causal Model5
1.4The Bivariate Linear Regression Model as a Statistical Model7
1.5Statistical Inference: Generalizing from Sample to Underlying Population10
1.6Goodness of Fit23
2Multiple Regression29
2.1The Problem of Bias in Bivariate Linear Regression30
2.2Multiple Regression with Two Predictor Variables31
2.3Multiple Regression with Three or More Predictor Variables34
2.4Dummy Variables to Represent Categorical Variables34
2.5Multicollinearity38
2.6Interaction40
2.7Nonlinearities46
2.8Goodness of Fit51
2.9Statistical Inference55
2.10Stepwise Regression62
2.11Illustrative Examples63
3Multiple Classification Analysis69
3.1The Basic MCA Table70
3.2The MCA Table in Deviation Form78
3.3MCA with Interactions82
3.4MCA with Additional Quantitative Control Variables85
3.5Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative)88
3.6Presenting MCA Results Graphically90
4Path Analysis93
4.1Path Diagrams and Path Coefficients93
4.2Path Models with More Than One Exogenous Variable100
4.3Path Models with Control Variables105
4.4Saturated and Unsaturated Path Models107
4.5Path Analysis with Standardized Variables109
4.6Path Models with Interactions and Nonlinearities114
5Logit Regression119
5.1The Linear Probability Model119
5.2The Logit Regression Model121
5.3Statistical Inference137
5.4Goodness of Fit140
5.5MCA Adapted to Logit Regression142
5.6Fitting the Logit Regression Model147
5.7Some Limitations of the Logit Regression Model150
6Multinomial Logit Regression151
6.1From Logit to Multinomial Logit151
6.2Multinomial Logit Models with Interactions and Nonlinearities159
6.3A More General Formulation of the Multinomial Logit Model160
6.4Reconceptualizing Contraceptive Method Choice as a Two-Step Process163
7Survival Models, Part 1: Life Tables167
7.1Actuarial Life Table168
7.2Product-Limit Life Table175
7.3The Life Table in Continuous Form178
8Survival Models, Part 2: Proportional Hazard Models181
8.1Basic Form of the Proportional Hazard Model182
8.2Calculation of Life Tables from the Proportional Hazard Model194
8.3Statistical Inference and Goodness of Fit198
8.4A Numerical Example198
8.5Multiple Classification Analysis (MCA) Adapted to Proportional Hazard Regression205
9Survival Models, Part 3: Hazard Models with Time Dependence207
9.1Time-Dependent Predictor Variables207
9.2Time-Dependent Coefficients214
Appendix A Sample Computer Programs221
A.1Description of the FIJIDATA File221
A.2SAS Mainframe Programs223
A.3Preparing Data for Survival Analysis227
A.4BMDP Mainframe Programs230
A.5LIMDEP Programs for IBM-Compatible Personal Computers238
Appendix B Statistical Reference Tables245
References251
Index254


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