Regression Models for Categorical Dependent Variables Using Stata, Second EditionAlthough regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata. The authors also provide a suite of commands for hypothesis testing and model diagnostics to accompany the book. The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. It covers in detail binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates. New to the Second Edition: Because all the examples, datasets, and author-written commands are available from the authors' Web site, readers can easily replicate the concrete examples using Stata, making it ideal for students or applied researchers who want to know how to fit and interpret models for categorical data. |
Contents
Introduction to Stata | 15 |
3 | 20 |
2 | 25 |
8 | 26 |
15 | 52 |
Estimation testing fit and interpretation | 75 |
5 | 81 |
2 | 99 |
8 | 277 |
Models for nominal outcomes with alternativespecific data | 293 |
3 | 313 |
4 | 325 |
Models for count outcomes | 349 |
7 | 405 |
More topics | 415 |
Testing whether treating an ordinal variable as interval loses | 421 |
Example of fitstat | 107 |
6 | 113 |
7 | 126 |
Models for Specific Kinds of Outcomes | 129 |
3 | 140 |
HosmerLemeshow statistic | 154 |
Discrete change | 169 |
8 | 181 |
2 | 187 |
4 | 195 |
8 | 202 |
9 | 220 |
17 | 242 |
Plotting probabilities for one outcome and two groups | 251 |
Plotting estimates from matrices with mlogplot | 268 |
3 | 427 |
5 | 444 |
Syntax | 449 |
brant | 452 |
fitstat | 459 |
misschk | 468 |
mlogview | 477 |
prchange | 483 |
prtab | 490 |
spex | 498 |
Description | 499 |
| 509 | |
Author index | 515 |
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Common terms and phrases
_cons age ed prst age wc hc alternative base category baseoutcome binary logit BlueCol Change in Predictions chapter Coef coefficients Conf confidence intervals count dataset default delta method diff discrete change do-files effect estimates store estimation commands example factor change fitstat graph twoway hc lwg inc independent variables interpretation k618 age wc Likelihood-ratio test listcoef Log likelihood logistic regression logit lfp k5 logit model LR test male white age marginal change Menial missing data missing values mlogtest MNLM multinomial multinomial logit multinomial probit nolog output omitted Number of obs observations odds ratios option ordered logit outcome categories parameters plot Poisson Pr(y prchange predicted probabilities prgen Prob probit model Prof prvalue regression model rest(mean specified SPost standard deviation standard errors standardized coefficients Stata statistics syntax value labels variables constant varlist Wald test wc hc lwg WhiteCol yr89 male white z-test zinb