9780803973749

Regression Models for Categorical and Limited Dependent Variables Vol. 7

by
  • ISBN13:

    9780803973749

  • ISBN10:

    0803973748

  • Format: Hardcover
  • Copyright: 1997-01-09
  • Publisher: SAGE Publications, Inc
  • Purchase Benefits
  • Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
  • We Buy This Book Back!
    In-Store Credit: $15.75
    Check/Direct Deposit: $15.00
List Price: $119.46 Save up to $3.58
  • Buy New
    $115.88
    Add to Cart Free Shipping

    USUALLY SHIPS IN 7-10 BUSINESS DAYS

Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

Summary

THE APPROACH "J. Scott Long's approach is one that I highly commend. There is a decided emphasis on the application and interpretation of the specific statistical techniques. Long works from the premise that the major difficulty with the analysis of limited and categorical dependent variables (LCDVs) is the complexity of interpreting nonlinear models, and he provides tools for interpretation that can be widely applied across the different techniques." --Robert L. Kaufman, Sociology, Ohio State University "A thorough and comprehensive introduction to analyzing categorical and limited dependent variables from a traditional regression perspective that provides unusually clear discussions concerning estimation, identification, and the multiplicity of models available to the researcher to analyze such data." --Scott Hershberger, Psychology, University of Kansas THE ORGANIZATION "The thing that impresses me the most about this book is how organized it is. The chapters are in excellent logical sequence. There is a useful repetition of important concepts (e.g., estimation, hypothesis testing) from chapter to chapter. J. Scott Long has done a terrific job of organizing like things from disparate literatures, such as the scaler measures of fit in Chapter 4." --Herbert L. Smith, Sociology, University of Pennsylvania "A major strength of the book is the way that it is organized. The chapter about each technique is written in a highly organized and parallel format. First the statistical basis and assumptions for the particular model are developed, then estimation issues are considered, then issues of testing and interpretation are considered, then variations and extensions are explored." --Robert L. Kaufman, Sociology, Ohio State University FOR THE COURSE "I have been teaching a course on categorical data analysis to sociology graduate students for close to 20 years, but I have never found a book with which I was happy. J. Scott Long's book, on the other hand, is nearly ideal for my objectives and preferences, and I expect that many other social scientists will feel the same way. I will definitely adopt it the next time I teach the course. It deals with the right topics in the most desirable sequence and it is clearly written." --Paul D. Allison, Sociology, University of Pennsylvania Class-tested at two major universities and written by an award-winning teacher, J. Scott Long's book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader to see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity. The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted, reviews standard statistical tests associated with maximum likelihood estimation, and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample

Table of Contents

List of Figures
xi(4)
List of Tables
xv(6)
Series Editor's Introduction xix(2)
Preface xxiii(2)
Acknowledgments xxv(2)
Abbreviations and Notation xxvii
1. Introduction
1(10)
1.1. Linear and Nonlinear Models
3(3)
1.2. Organization
6(3)
1.3. Orientation
9(1)
1.4. Bibliographic Notes
10(1)
2. Continuous Outcomes: The Linear Regression Model
11(23)
2.1. The Linear Regression Model
11(3)
2.2. Interpreting Regression Coefficients
14(4)
2.3. Estimation by Ordinary Least Squares
18(2)
2.4. Nonlinear Linear Regression Models
20(2)
2.5. Violations of the Assumptions
22(3)
2.6. Maximum Likelihood Estimation
25(8)
2.7. Conclusions
33(1)
2.8. Bibliographic Notes
33(1)
3. Binary Outcomes: The Linear Probability, Probit, and Logit Models
34(51)
3.1. The Linear Probability Model
35(5)
3.2. A Latent Variable Model for Binary Variables
40(7)
3.3. Identification
47(3)
3.4. A Nonlinear Probability Model
50(2)
3.5. ML Estimation
52(2)
3.6. Numerical Methods for ML Estimation
54(7)
3.7. Interpretation
61(18)
3.8. Interpretation Using Odds Ratios
79(4)
3.9. Conclusions
83(1)
3.10. Bibliographic Notes
83(2)
4. Hypothesis Testing and Goodness of Fit
85(29)
4.1. Hypothesis Testing
85(13)
4.2. Residuals and Influence
98(4)
4.3. Scalar Measures of Fit
102(10)
4.4. Conclusions
112(1)
4.5. Bibliographic Notes
113(1)
5. Ordinal Outcomes: Ordered Logit and Ordered Probit Analysis
114(34)
5.1. A Latent Variable Model for Ordinal Variables
116(6)
5.2. Identification
122(1)
5.3. Estimation
123(4)
5.4. Interpretation
127(13)
5.5. The Parallel Regression Assumption
140(5)
5.6. Related Models for Ordinal Data
145(1)
5.7. Conclusions
146(1)
5.8. Bibliographic Notes
147(1)
6. Nominal Outcomes: Multinomial Logit and Related Models
148(39)
6.1. Introduction to the Multinomial Logit Model
149(2)
6.2. The Multinomial Logit Model
151(5)
6.3. ML Estimation
156(2)
6.4. Computing and Testing Other Contrasts
158(2)
6.5. Two Useful Tests
160(4)
6.6. Interpretation
164(14)
6.7. The Conditional Logit Model
178(4)
6.8. Independence of Irrelevant Alternatives
182(2)
6.9. Related Models
184(1)
6.10. Conclusions
185(1)
6.11. Bibliographic Notes
186(1)
7. Limited Outcomes: The Tobit Model
187(30)
7.1. The Problem of Censoring
188(4)
7.2. Truncated and Censored Distributions
192(4)
7.3. The Tobit Model for Censored Outcomes
196(8)
7.4. Estimation
204(2)
7.5. Interpretation
206(5)
7.6. Extensions
211(5)
7.7. Conclusions
216(1)
7.8. Bibliographic Notes
216(1)
8. Count Outcomes: Regression Models for Counts
217(34)
8.1. The Poisson Distribution
218(3)
8.2. The Poisson Regression Model
221(9)
8.3. The Negative Binomial Regression Model
230(9)
8.4. Models for Truncated Counts
239(3)
8.5. Zero Modified Count Models
242(5)
8.6. Comparisons Among Count Models
247(2)
8.7. Conclusions
249(1)
8.8. Bibliographic Notes
249(2)
9. Conclusions
251(13)
9.1. Links Using Latent Variable Models
252(5)
9.2. The Generalized Linear Model
257(1)
9.3. Similarities Among Probability Models
258(1)
9.4. Event History Analysis
258(1)
9.5. Log-Linear Models
259(5)
A. Answers to Exercises 264(10)
References 274(9)
Author Index 283(4)
Subject Index 287(10)
About the Author 297

Rewards Program

Write a Review