"This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. The Second Edition presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models"--
Logistic Regression: A Primer helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. In the Second Edition, Fred C. Pampel presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models.
| Series Editor Introduction |
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ix | |
| Preface |
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xi | |
| Acknowledgments |
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xiii | |
| About the Author |
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xv | |
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Chapter 1 The Logic of Logistic Regression |
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1 | (18) |
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Regression With a Binary Dependent Variable |
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1 | (8) |
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Transforming Probabilities Into Logits |
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9 | (5) |
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Linearizing the Nonlinear |
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14 | (3) |
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17 | (2) |
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Chapter 2 Interpreting Logistic Regression Coefficients |
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19 | (32) |
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19 | (4) |
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23 | (3) |
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26 | (15) |
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Standardized Coefficients |
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41 | (6) |
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Group and Model Comparisons of Logistic Regression Coefficients |
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47 | (2) |
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49 | (2) |
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Chapter 3 Estimation and Model Fit |
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51 | (18) |
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Maximum Likelihood Estimation |
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51 | (5) |
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Tests of Significance Using Log Likelihood Values |
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56 | (6) |
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62 | (5) |
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67 | (2) |
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Chapter 4 Probit Analysis |
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69 | (12) |
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Another Way to Linearize the Nonlinear |
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69 | (3) |
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The Probit Transformation |
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72 | (1) |
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73 | (4) |
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Maximum Likelihood Estimation |
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77 | (2) |
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79 | (2) |
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Chapter 5 Ordinal and Multinomial Logistic Regression |
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81 | (28) |
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Ordinal Logistic Regression |
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82 | (13) |
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Multinomial Logistic Regression |
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95 | (13) |
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108 | (1) |
| Notes |
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109 | (6) |
| Appendix: Logarithms |
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115 | (1) |
| The Logic of Logarithms |
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115 | (2) |
| Properties of Logarithms |
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117 | (3) |
| Natural Logarithms |
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120 | (2) |
| Summary |
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122 | (3) |
| References |
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125 | (2) |
| Index |
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127 | |
FRED C. PAMPEL is Research Professor of Sociology and a Research Associate in the Population Program at the University of Colorado Boulder. He received a Ph.D. in sociology from the University of Illinois, Champaign-Urbana, in 1977, and has previously taught at the University of Iowa, University of North Carolina, and Florida State University. His research focuses on socioeconomic disparities in health behaviors, smoking in particular, and on the experimental and quasi-experimental methods for evaluation of social programs for youth. He is the author of several books on population aging, cohort change, and public policy, and his work has appeared in the American Sociological Review, the American Journal of Sociology, Demography, Social Forces, and the European Sociological Review.
