Regression Models for Categorical Dependent Variables Using Stata (2nd Edition)
by J. Scott Long and Jeremy Freese, (2006)

Publisher: Stata Press
ISBN: 1-59718-011-4
Pages: 528 pages
Price: £48.00 + p&p

Contents

Table of Contents
Book Order Form
Table of Contents

Part I General Information

1 Introduction

1.1 What is this book about?
1.2 Which models are considered?
1.3 Who is this book for?
1.4 How is the book organized?
1.5 What software do you need?
1.5.1 Updating Stata 8
1.5.2 Installing SPost
1.5.3 What if commands do not work?
1.5.4 Uninstalling SPost
1.5.5 Additional files available on the web site
1.6 Where can I learn more about the models?

2 Introduction to Stata

2.1 The Stata interface
2.2 Abbreviations
2.3 How to get help
2.3.1 Online help
2.3.2 Manuals
2.3.3 Other resources
2.4 The working directory
2.5 Stata file types
2.6 Saving output to log files
2.6.1 Closing a log file
2.6.2 Viewing a log file
2.6.3 Converting from SMCL to plain text or PostScript
2.7 Using and saving datasets
2.7.1 Data in Stata format
2.7.2 Data in other formats
2.7.3 Entering data by hand
2.8 Size limitations on datasets
2.9 Do-files
2.9.1 Adding comments
2.9.2 Long lines
2.9.3 Stopping a do-file while it is running
2.9.4 Creating do-files
2.9.5 A recommended structure for do-files
2.10 Using Stata for serious data analysis
2.11 The syntax of Stata commands
2.11.1 Commands
2.11.2 Variable lists
2.11.3 if and in qualifiers
2.11.4 Options
2.12 Managing data
2.12.1 Looking at your data
2.12.2 Getting information about variables
2.12.3 Missing values
2.12.4 Selecting observations
2.12.5 Selecting variables
2.13 Creating new variables
2.13.1 generate command
2.13.2 replace command
2.13.3 recode command
2.13.4 Common transformations for RHS variables
2.14 Labeling variables and values
2.14.1 Variable labels
2.14.2 Value labels
2.14.3 notes command
2.15 Global and local macros
2.16 Graphics
2.16.1 The graph command
2.16.2 Displaying previously drawn graphs
2.16.3 Printing graphs
2.16.4 Combining graphs
2.17 A brief tutorial

3 Estimation, Testing, Fit, and Interpretation

3.1 Estimation
3.1.1 Stata's output for ML estimation
3.1.2 ML and sample size
3.1.3 Problems in obtaining Ml estimates
3.1.4 The syntax of estimation commands
3.1.5 Reading the output
3.1.6 Reformatting output with estimates table
3.1.7 Reformatting output with outreg
3.1.8 Alternative output with listcoef
3.1.9 Storing estimation results
3.2 Post-estimation analysis
3.3 Testing
3.3.1 Wald tests
3.3.2 LR tests
3.4 Measures of fit
3.5 Interpretation
3.5.1 Approaches to interpretation
3.5.2 Predictions using predict
3.5.3 Overview of prchange, prgen, prtab, and prvalue
3.5.4 Syntax for prchange
3.5.5 Syntax for prgen
3.5.6 Syntax for prtab
3.5.7 Syntax for prvalue
3.5.8 Computing marginal effects using mfx compute
3.6 Next steps

Part II Models for Specific Kinds of Outcomes

4 Models for Binary Outcomes

4.1 The statistical model
4.1.1 A latent variable model
4.1.2 A nonlinear probability model
4.2 Estimation using logit and probit
4.2.1 Observations predicted perfectly
4.3 Hypothesis testing with test and lrtest
4.3.1 Testing individual coefficients
4.3.2 Testing multiple coefficients
4.3.3 Comparing LR and Wald tests
4.4 Residuals and influence using predict
4.4.1 Residuals
4.4.2 Influential cases
4.5 Scalar measures of fit using fitstat
4.6 Interpretation using predicted values
4.6.1 Predicted probabilities with predict
4.6.2 Individual predicted probabilities with prvalue
4.6.3 Tables of predicted probabilities with prtab
4.6.4 Graphing predicted probabilities with prgen
4.6.5 Changes in predicted probabilities
4.7 Interpretation using odds ratios with listcoef
4.8 Other commands for binary outcomes

5 Models for Ordinal Outcomes

5.1 The statistical model
5.1.1 A latent variable model
5.1.2 A nonlinear probability model
5.2 Estimation using ologit and oprobit
5.2.1 Example of attitudes toward working mothers
5.2.2 Predicting perfectly
5.3 Hypothesis testing with test and lrtest
5.3.1 Testing individual coefficients
5.3.2 Testing multiple coefficients
5.4 Scalar measures of fit using fitstat
5.5 Converting to a different parameterization
5.6 The parallel regression assumption
5.7 Residuals and outliers using predict
5.8 Interpretation
5.8.1 Marginal change in y
5.8.2 Predicted probabilities
5.8.3 Predicted probabilities with predict
5.8.4 Individual predicted probabilities with prvalue
5.8.5 Tables of predicted probabilities with prtab
5.8.6 Graphing predicted probabilities with prgen
5.8.7 Changes in predicted probabilities
5.8.8 Odds ratios using listcoef
5.9 Less-common models for ordinal outcomes
5.9.1 Generalized ordered logit model
5.9.2 The stereotype model
5.9.3 The continuation ratio model

6 Models for Nominal Outcomes

6.1 The multinomial logit model
6.1.1 Formal statement of the model
6.2 Estimation using mlogit
6.2.1 Example of occupational attainment
6.2.2 Using different base categories
6.2.3 Predicting perfectly
6.3 Hypothesis testing of coefficients
6.3.1 mlogtest for tests of the MNLM
6.3.2 Testing the effects of the independent variables
6.3.3 Tests for combining dependent categories
6.4 Independence of irrelevant alternatives
6.5 Measures of fit
6.6 Interpretation
6.6.1 Predicted probabilities
6.6.2 Predicted probabilities with predict
6.6.3 Individual predicted probabilities with prvalue
6.6.4 Tables of predicted probabilities with prtab
6.6.5 Graphing predicted probabilities with prgen
6.6.6 Changes in predicted probabilities
6.6.7 Plotting discrete changes with prchange and mlogview
6.6.8 Odds ratios using listcoef and mlogview
6.6.9 Using mlogplot
6.6.10 Plotting estimates from matrices with mlogplot
6.7 The conditional logit model
6.7.1 Data arrangement for conditional logit
6.7.2 Fitting the conditional logit model
6.7.3 Interpreting results from clogit
6.7.4 Fitting the multinomial logit model using clogit
6.7.5 Using clogit to fit mixed models

7 Models for Count Outcomes

7.1 The Poisson distribution
7.1.1 Fitting the Poisson distribution with the poisson command
7.1.2 Computing predicted probabilities with prcounts
7.1.3 Comparing observed and predicted counts with prcounts
7.2 The Poisson regression model
7.2.1 Estimating the PRM with poisson
7.2.2 Example of fitting the PRM
7.2.3 Interpretation using the rate µ
7.2.4 Interpretation using predicted probabilities
7.2.5 Exposure time
7.3 The negative binomial regression model
7.3.1 Fitting the NBRM with nbreg
7.3.2 Example of fitting the NBRM
7.3.3 Testing for overdispersion
7.3.4 Interpretation using the rate µ
7.3.5 Interpretation using predicted probabilities
7.4 Zero-inflated count models
7.4.1 Estimation of zero-inflated models with zinb and zip
7.4.2 Example of fitting the ZIP and ZINB models
7.4.3 Interpretation of coefficients
7.4.4 Interpretation of predicted probabilities
7.5 Comparisons among count models
7.5.1 Comparing mean probabilities
7.5.2 Tests to compare count models

8 Additional Topics

8.1 Ordinal and nominal independent variables
8.1.1 Coding a categorical independent variable as a set of dummy variables
8.1.2 Estimation and interpretation with categorical independent variables
8.1.3 Tests with categorical independent variables
8.1.4 Discrete change for categorical independent variables
8.2 Interactions
8.2.1 Computing gender differences in predictions with interactions
8.2.2 Computing gender differences in discrete change with interactions
8.3 Nonlinear nonlinear models
8.3.1 Adding nonlinearities to linear predictors
8.3.2 Discrete change in nonlinear nonlinear models
8.4 Using praccum and forvalues to plot predictions
8.4.1 Example using age and age-squared
8.4.2 Using forvalues with praccum
8.4.3 Using praccum for graphing a transformed variable
8.4.4 Using praccum to graph interactions
8.5 Extending SPost to other estimation commands
8.6 Using Stata more efficiently
8.6.1 profile.do
8.6.2 Changing screen fonts and window preferences
8.6.3 Using ado-files for changing directories
8.6.4 me.hlp file
8.6.5 Scrolling in the Results Window in Windows
8.7 Conclusions

A Syntax for SPost Commands

A.1 brant
A.2 fitstat
A.3 listcoef
A.4 mlogplot
A.5 mlogtest
A.6 mlogview
A.7 Overview of prchange, prgen, prtab, and prvalue
A.8 praccum
A.9 prchange
A.10 prcounts
A.11 prgen
A.12 prtab
A.13 prvalue

B Description of Datasets

B.1 binlfp2
B.2 couart2
B.3 gsskidvalue2
B.4 nomocc2
B.5 ordwarm2
B.6 science2
B.7 travel2

Author Index
Subject Index
Author Index
Subject Index