Regression models for categorical and limited dependent variables:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Thousand Oaks, Calif. [u.a.]
Sage
2011
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| Edition: | [Nachdr.] |
| Series: | Advanced quantitative techniques in the social sciences
7 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 274 - 282 |
| Physical Description: | XXX, 297 S. graph. Darst. |
| ISBN: | 9780803973749 |
Staff View
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Record in the Search Index
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| adam_text | Contents List of Figures xi List of Tables XV Series Editor’s Introduction Preface Acknowledgments Abbreviations and Notation xix xxiii xxv xxvii 1. Introduction 1.1. Linear and Nonlinear Models 1.2. Organization 1.3. Orientation 1.4. Bibliographic Notes 1 3 6 9 10 2. Continuous Outcomes: The Linear Regression Model 2.1. The Linear Regression Model 2.2. Interpreting Regression Coefficients 2.3. Estimation by Ordinary Least Squares 2.4. Nonlinear Linear Regression Models 2.5. Violations of the Assumptions 11 11 14 18 20 22
2.6. Maximum Likelihood Estimation 2.7. Conclusions 2.8. Bibliographic Notes 3. Binary Outcomes: The Linear Probability, Probit, and Logit Models 3.1. The Linear Probability Model 3.2. A Latent Variable Model for BinaryVariables 3.3. Identification 3.4. A Nonlinear Probability Model 3.5. ML Estimation 3.6. Numerical Methods for ML Estimation 3.7. Interpretation 3.8. Interpretation Using Odds Ratios 3.9. Conclusions 3.10. Bibliographic Notes 25 33 33 34 35 40 47 50 52 54 61 79 83 83 4. Hypothesis Tèsting and Goodness of Fit 4.1. Hypothesis Testing 4.2. Residuals and Influence 4.3. Scalar Measures of Fit 4.4. Conclusions 4.5. Bibliographic Notes 85 85 98 102 112 113 5. Ordinal Outcomes: Ordered Logit and Ordered Probit Analysis 5.1. A Latent Variable Model for OrdinalVariables 5.2. Identification 5.3. Estimation 5.4. Interpretation 5.5. The Parallel Regression Assumption 5.6. Related Models for Ordinal Data 5.7. Conclusions 5.8. Bibliographic Notes 114 116 122 123 127 140 145 146 147 6. Nominal Outcomes: Multinomial Logit and Related Models 6.1. Introduction to the Multinomial LogitModel 6.2. The Multinomial Logit Model 6.3. ML Estimation 6.4. Computing and Testing Other Contrasts 6.5. TWo Useful Tests 6.6. Interpretation 6.7. The Conditional Logit Model 6.8. Independence of Irrelevant Alternatives 148 149 151 156 158 160 164 178 182
6.9. Related Models 6.10. Conclusions 6.11. Bibliographic Notes 184 185 186 7. Limited Outcomes: The Tbbit Model 7.1. The Problem of Censoring 7.2. Truncated and Censored Distributions 7.3. The Tbbit Model for Censored Outcomes 7.4. Estimation 7.5. Interpretation 7.6. Extensions 7.7. Conclusions 7.8. Bibliographic Notes 187 188 192 196 204 206 211 216 216 8. Count Outcomes: RegressionModels for Counts 8.1. The Poisson Distribution 8.2. The Poisson Regression Model 8.3. The Negative Binomial Regression Model 8.4. Models for Thincated Counts 8.5. Zero Modified Count Models 8.6. Comparisons Among Count Models 8.7. Conclusions 8.8. Bibliographic Notes 217 218 221 230 239 242 247 249 249 9. Conclusions 9.1. Links Using Latent Variable Models 9.2. The Generalized Linear Model 9.3. Similarities Among Probability Models 9.4. Event History Analysis 9.5. Log-Linear Models 251 252 257 258 258 259 A. Answers to Exercises 264 References 274 Author Index 283 Subject Index 287 About the Author 297
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| isbn | 9780803973749 |
| language | English |
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| physical | XXX, 297 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
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| series | Advanced quantitative techniques in the social sciences |
| series2 | Advanced quantitative techniques in the social sciences |
| spelling | Long, J. Scott Verfasser (DE-588)171773071 aut Regression models for categorical and limited dependent variables J. Scott Long [Nachdr.] Thousand Oaks, Calif. [u.a.] Sage 2011 XXX, 297 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced quantitative techniques in the social sciences 7 Literaturverz. S. 274 - 282 Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s DE-604 Nichtlineares Regressionsmodell (DE-588)4251078-8 s 1\p DE-604 Advanced quantitative techniques in the social sciences 7 (DE-604)BV023546702 7 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022545195&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Long, J. Scott Regression models for categorical and limited dependent variables Advanced quantitative techniques in the social sciences Regressionsanalyse (DE-588)4129903-6 gnd Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4251078-8 |
| title | Regression models for categorical and limited dependent variables |
| title_auth | Regression models for categorical and limited dependent variables |
| title_exact_search | Regression models for categorical and limited dependent variables |
| title_full | Regression models for categorical and limited dependent variables J. Scott Long |
| title_fullStr | Regression models for categorical and limited dependent variables J. Scott Long |
| title_full_unstemmed | Regression models for categorical and limited dependent variables J. Scott Long |
| title_short | Regression models for categorical and limited dependent variables |
| title_sort | regression models for categorical and limited dependent variables |
| topic | Regressionsanalyse (DE-588)4129903-6 gnd Nichtlineares Regressionsmodell (DE-588)4251078-8 gnd |
| topic_facet | Regressionsanalyse Nichtlineares Regressionsmodell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022545195&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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