LEADER 05022nam 2200601 a 450 001 9910829830503321 005 20230421051843.0 010 $a1-282-25166-X 010 $a9786613813916 010 $a1-118-03313-2 010 $a1-118-03134-2 035 $a(CKB)2560000000049018 035 $a(EBL)661496 035 $a(OCoLC)705538498 035 $a(SSID)ssj0000473441 035 $a(PQKBManifestationID)11346231 035 $a(PQKBTitleCode)TC0000473441 035 $a(PQKBWorkID)10448015 035 $a(PQKB)11076948 035 $a(MiAaPQ)EBC661496 035 $a(EXLCZ)992560000000049018 100 $a19930812d1993 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical models for causal analysis$b[electronic resource] /$fRobert D. Retherford, Minja Kim Choe 210 $aNew York $cWiley$d1993 215 $a1 online resource (274 p.) 300 $a"A Wiley-Interscience publication." 311 $a0-471-55802-8 320 $aIncludes bibliographical references (p. 251-253) and index. 327 $aSTATISTICAL MODELS FOR CAUSAL ANALYSIS; CONTENTS; 1 Bivariate Linear Regression; 1.1. Terminology; 1.2. Fitting a Least-Squares Line; 1.3. The Least-Squares Line as a Causal Model; 1.4. The Bivariate Linear Regression Model as a Statistical Model; 1.4.1. Simplifying Assumptions; 1.5. Statistical Inference: Generalizing from Sample to Underlying Population; 1.5.1. Hypothesis Testing; 1.5.2. Confidence Intervals; 1.5.3. t Values and Z Values; 1.5.4. p Value; 1.5.5. Importance of a Good Spread of Values of the Predictor Variable; 1.5.6. Beware of Outliers! 327 $a1.5.7. Beware of Selection on the Response Variable!1.5.8. Presentation of Results; 1.6. Goodness of Fit; 1.6.1. Standard Error of the Estimate, s; 1.6.2. Coefficient of Determination, r2 , and Correlation Coefficient, r; 1.7. Further Reading; 2 Multiple Regression; 2.1. The Problem of Bias in Bivariate Linear Regression; 2.2. Multiple Regression with Two Predictor Variables; 2.3. Multiple Regression with Three or More Predictor Variables; 2.4. Dummy Variables to Represent Categorical Variables; 2.4.1. Categorical Variables with Two Categories 327 $a2.4.2. Categorical Variables with More Than Two Categories2.5. Multicollinearity; 2.6. Interaction; 2.6.1. Model Specification; 2.6.2. More Complicated Interactions; 2.6.3. Correlation without Interaction; 2.6.4. Interaction without Correlation; 2.7. Nonlinearities; 2.7.1. Quadratic Specification; 2.7.2. Dummy Variable Specification; 2.8. Goodness of Fit; 2.8.1. Standard Error of the Estimate, s; 2.8.2. Coefficient of Determination, R2, and Multiple Correlation Coefficient, R; 2.8.3. Corrected R2 and Corrected R; 2.8.4. Partial Correlation Coefficient; 2.9. Statistical Inference 327 $a2.9.1. Hypothesis Testing, Confidence Intervals, and p Values for a Single Regression Coefficient2.9.2. Testing the Difference Between Two Regression Coefficients, ?i and ?j; 2.9.3. Testing Effects When There Is Interaction; 2.9.4. Testing Effects When There Is a Nonlinearity; 2.9.5. The ANOVA Table; 2.9.6. The Omnibus F Test of the Hypothesis ?1 = ?2 = ... = ?k =0; 2.9.7. Test of the Hypothesis That Some of the ?j Are Zero; 2.10. Stepwise Regression; 2.11. Illustrative Examples; 2.11.1. Example 1; 2.11.2. Example 2; 2.12. Further Reading; 3 Multiple Classification Analysis 327 $a3.1. The Basic MCA Table3.1.1. Unadjusted Values; 3.1.2. Adjusted Values; 3.1.3. Unadjusted and Adjusted, R; 3.1.4. A Numerical Example; 3.2. The MCA Table in Deviation Form; 3.2.1. First Approach to Table Set-up; 3.2.2. Second Approach to Table Set-up; 3.2.3. A Numerical Example; 3.3. MCA with Interactions; 3.3.1. Table Set-up; 3.3.2. A Numerical Example; 3.4. MCA with Additional Quantitative Control Variables; 3.4.1. Table Set-up; 3.4.2. A Numerical Example; 3.5. Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative); 3.5.1. Table Set-up 327 $a3.5.2. A Numerical Example 330 $aSimplifies 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 regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters. 606 $aMultivariate analysis 615 0$aMultivariate analysis. 676 $a519.5/35 676 $a519.535 700 $aRetherford$b Robert D$01643851 701 $aChoe$b Minja Kim$f1941-$0104800 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829830503321 996 $aStatistical models for causal analysis$93989348 997 $aUNINA