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Regression Models for Categorical Dependent Variables Using Stata, Second Edition 

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Comment from the Stata technical groupRegression Models for Categorical Dependent Variables Using Stata, Second Edition, by J. Scott Long and Jeremy Freese, shows how to use Stata to fit and interpret regression models for categorical data. Nearly 50% longer than the previous edition, the second edition covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zerotruncated count models. Many of the interpretation techniques have been updated to include interval and point estimates. Although 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. To accompany the book, Long and Freese provide a suite of commands for hypothesis testing and model diagnostics. The second edition begins with an excellent introduction to Stata and follows with general treatments of estimation, testing, fit, and interpretation in this class of models. Long and Freese detail binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter explains how to fit and interpret models with special characteristics, such as interaction, nonlinear terms, and ordinal and nominal independent variables. One appendix explains the syntax of the authorwritten commands, and a second appendix details the book's datasets. Long and Freese use many concrete examples in their second edition. All the examples, datasets, and authorwritten commands are available on the authors’ website, so readers can easily replicate the examples when using Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data. 

Table of contentsView table of contents >> Preface (pdf)
Part I General Information
1 Introduction
1.1 What is this book about?
1.2 Which models are considered? 1.3 Whom is this book for? 1.4 How is the book organized? 1.5 What software do you need?
1.5.1 Updating Stata 9
1.6 Where can I learn more about the models?
1.5.2 Installing SPost
Installing SPost using search
1.5.3 What if commands do not work? Installing SPost using net install 1.5.4 Uninstalling SPost 1.5.5 Using spex to load data and run examples 1.5.6 More files available on the web site 2 Introduction to Stata
2.1 The Stata interface
Changing the scrollback buffer size
2.2 Abbreviations Changing the display of variable names in the Variables window 2.3 How to get help
2.3.1 Online help
2.4 The working directory 2.3.2 Manuals 2.3.3 Other resources 2.5 Stata file types 2.6 Saving output to log files
Options
2.6.1 Closing a log file
2.7 Using and saving datasets
2.6.2 Viewing a log file 2.6.3 Converting from SMCL to plain text or PostScript
2.7.1 Data in Stata format
2.8 Size limitations on datasets* 2.7.2 Data in other formats 2.7.3 Entering data by hand 2.9 Dofiles
2.9.1 Adding comments
2.10 Using Stata for serious data analysis 2.9.2 Long lines 2.9.3 Stopping a dofile while it is running 2.9.4 Creating dofiles
Using Stata's Dofile Editor
2.9.5 Recommended structure for dofiles
Using other editors to create dofiles 2.11 Syntax of Stata commands
2.11.1 Commands
2.12 Managing data
2.11.2 Variable lists 2.11.3 if and in qualifiers
Examples of if qualifier
2.11.4 Options
2.12.1 Looking at your data
2.13 Creating new variables
2.12.2 Getting information about variables 2.12.3 Missing values 2.12.4 Selecting observations 2.12.5 Selecting variables
2.13.1 generate command
2.14 Labeling variables and values
2.13.2 replace command 2.13.3 recode command 2.13.4 Common transformations for RHS variables
Breaking a categorical variable into a set of binary variables
More examples of creating binary variables Nonlinear transformations Interaction terms
2.14.1 Variable labels
2.15 Global and local macros 2.14.2 Value labels 2.14.3 notes command 2.16 Graphics
2.16.1 graph command
2.17 A brief tutorial
2.16.2 Displaying previously drawn graphs 2.16.3 Printing graphs 2.16.4 Combining graphs
A batch version
3 Estimation, testing, fit, and interpretation
3.1 Estimation
3.1.1 Stata’s output for ML estimation
3.2 Postestimation analysis 3.1.2 ML and sample size 3.1.3 Problems in obtaining ML estimates 3.1.4 Syntax of estimation commands
Variable lists
3.1.5 Reading the output
Specifying the estimation sample Weights Options
Header
3.1.6 Storing estimation results Estimates and standard errors Confidence intervals 3.1.7 Reformatting output with estimates table 3.1.8 Reformatting output with estout 3.1.9 Alternative output with listcoef
Options for types of coefficients
Options for mlogit, mprobit, and slogit Other options Standardized coefficients Factor and percent change 3.3 Testing
3.3.1 Wald tests
3.4 estat command
The accumulate option
3.3.2 LR tests
Avoiding invalid LR tests
3.5 Measures of fit
Syntax of fitstat
3.6 Interpretation
Options Models and measures Example of fitstat Methods and formulas for fitstat
3.6.1 Approaches to interpretation
3.7 Confidence intervals for prediction 3.6.2 Predictions using predict 3.6.3 Overview of prvalue, prchange, prtab, and prgen
Specifying the levels of variables
3.6.4 Syntax for prvalue
Options controlling output
Options
3.6.5 Syntax for prchange
Options for confidence intervals Options used for bootstrapped confidence intervals
Options
3.6.6 Syntax for prtab
Options
3.6.7 Syntax for prgen
Options
3.6.8 Computing marginal effects using mfx
Options for confidence intervals and marginals Variables generated 3.8 Next steps Part II Models for Specific Kinds of Outcomes
4 Models for binary outcomes
4.1 The statistical model
4.1.1 A latentvariable model
4.2 Estimation using logit and probit
4.1.2 A nonlinear probability model
Variable lists
Specifying the estimation sample Weights Options Example
4.2.1 Observations predicted perfectly
4.3 Hypothesis testing with test and lrtest
4.3.1 Testing individual coefficients
4.4 Residuals and influence using predict
One and twotailed tests
4.3.2 Testing multiple coefficients
Testing single coefficients using test Testing single coefficients using lrtest
Testing multiple coefficients using test
4.3.3 Comparing LR and Wald tests
Testing multiple coefficients using lrtest
4.4.1 Residuals
4.5 Measuring fit
Example
4.4.2 Influential cases 4.4.3 Least likely observations
Syntax
Options Options for controlling the list of values
4.5.1 Scalar measures of fit using fitstat
4.6 Interpretation using predicted values
4.5.2 Hosmer–Lemeshow statistic
4.6.1 Predicted probabilities with predict
4.7 Interpretation using odds ratios with listcoef
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 Plotting confidence intervals 4.6.6 Changes in predicted probabilities
Marginal change
Discrete change
Multiplicative coefficients
4.8 Other commands for binary outcomes
Effect of the base probability Percent change in the odds 5 Models for ordinal outcomes
5.1 The statistical model
5.1.1 A latentvariable model
5.2 Estimation using ologit and oprobit
5.1.2 A nonlinear probability model
Variable lists
Specifying the estimation sample Weights Options
5.2.1 Example of attitudes toward working mothers
5.3 Hypothesis testing with test and lrtest
5.2.2 Predicting perfectly
5.3.1 Testing individual coefficients
5.4 Scalar measures of fit using fitstat 5.3.2 Testing multiple coefficients 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*
5.9 Less common models for ordinal outcomes
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
Marginal change with prchange
5.8.8 Odds ratios using listcoef
Marginal change with mfx Discrete change with prchange Confidence intervals for discrete changes Computing discrete change for a 10year increase in age
5.9.1 The stereotype model
5.9.2 The generalized ordered logit model 5.9.3 The continuation ratio model 6 Models for nominal outcomes with casespecific data
6.1 The multinomial logit model
6.1.1 Formal statement of the model
6.2 Estimation using mlogit
Variable lists
Specifying the estimation sample Weights Options
6.2.1 Example of occupational attainment
6.3 Hypothesis testing of coefficients
6.2.2 Using different base categories 6.2.3 Predicting perfectly
6.3.1 mlogtest for tests of the MNLM
6.4 Independence of irrelevant alternatives
Options
6.3.2 Testing the effects of the independent variables
A likelihoodratio test
6.3.3 Tests for combining alternatives
A Wald test Testing multiple independent variables
A Wald test for combining alternatives
Using test [category]* An LR test for combining alternatives Using constraint with lrtest*
Hausman test of IIA
6.5 Measures of fit Small–Hsiao test of IIA 6.6 Interpretation
6.6.1 Predicted probabilities
6.7 Multinomial probit model with IIA 6.6.2 Predicted probabilities with predict
Using predict to compare mlogit and ologit
6.6.3 Predicted probabilities and discrete change with prvalue 6.6.4 Tables of predicted probabilities with prtab 6.6.5 Graphing predicted probabilities with prgen
Plotting probabilities for one outcome and two groups
6.6.6 Changes in predicted probabilities
Graphing probabilities for all outcomes for one group
Computing marginal and discrete change with prchange
6.6.7 Plotting discrete changes with prchange and mlogview Marginal change with mfx 6.6.8 Odds ratios using listcoef and mlogview
Listing odds ratios with listcoef
6.6.9 Using mlogplot* Plotting odds ratios 6.6.10 Plotting estimates from matrices with mlogplot*
Options for using matrices with mlogplot
Global macros and matrices used by mlogplot Example 6.8 Stereotype logistic regression
6.8.1 Formal statement of the onedimensional SLM
6.8.2 Fitting the SLM with slogit
Options
6.8.3 Interpretation using predicted probabilities Example 6.8.4 Interpretation using odds ratios 6.8.5 Distinguisability and the φ parameters 6.8.6 Ordinality in the onedimensional SLM
Higherdimension SLM
7 Models for nominal outcomes with alternativespecific data
7.1 Alternativespecific data organization
7.1.1 Syntax for case2alt
7.2 The conditional logit model
7.2.1 Fitting the conditional logit model
7.3 Alternativespecific multinomial probit
Example of the clogit model
7.2.2 Interpreting odds ratios from clogit 7.2.3 Interpreting probabilities from clogit
Using predict
7.2.4 Fitting the multinomial logit model using clogit
Using asprvalue
Setting up the data with case2alt
7.2.5 Using clogit with case and alternativespecific variables
Fitting multinomial logit with clogit
Example of a mixed model
Interpretation of odds ratios using listcoef Interpretation of predicted probabilities using asprvalue Allow the effects of alternativespecific variables to vary over the alternatives
7.3.1 The model
7.4 The sturctural covariance matrix
7.3.2 Informal explanation of estimation by simulation 7.3.3 Alternativebased data with uncorrelated errors
Options
7.3.4 Alternativebased data with correlated errors
Examples
7.4.1 Interpretation using probabilities
7.5 Rankordered logistic regression
Using predict
7.4.2 Identification, discrete change, and marginal effects Using asprvalue 7.4.3 Testing for IIA 7.4.4 Adding casespecific data
7.5.1 Fitting the rankordered logit model
7.6 Conclusions
Options
7.5.2 Interpreting results from rologit
Example of the rankordered logit model
Interpretation using odds ratios
Interpretation using predicted probabilties 8 Models for count outcomes
8.1 The Poisson distribution
8.1.1 Fitting the Poisson distribution with the poisson command
8.2 The Poisson regression model
8.1.2 Computing predicted probabilities with prcounts
Syntax
8.1.3 Comparing observed and predicted counts with prcounts
Options Variables generated
8.2.1 Estimating the PRM with poisson
8.3 The negative binomial regression model
Variable lists
8.2.2 Example of fitting the PRM Specifying the estimation sample Weights Options 8.2.3 Interpretation using the rate, μ
Factor change in E(yx)
8.2.4 Interpretation using predicted probabilities
Percent change in E(yx) Example of factor and percent change Marginal change in E(yx) Example of marginal change using prchange Example of marginal change using mfx Discrete change in E(yx) Example of discrete change using prchange Example of discrete change with confidence intervals
Example of predicted probabilities using prvalue
8.2.5 Exposure time*
Example of predicted probabilities using prgen Example of predicted probabilities using prcounts
8.3.1 Fitting the NBRM with nbreg
8.4 Models for truncated counts
NB1 and NB2 variance functions
8.3.2 Example of fitting the NBRM
Comparing the PRM and NBRM using estimates table
8.3.3 Testing for overdispersion 8.3.4 Interpretation using the rate μ 8.3.5 Interpretation using predicted probabilities
8.4.1 Fitting zerotruncated models
8.5 The hurdle regression model*
8.4.2 Example of fitting zerotruncated models 8.4.3 Interpretation of parameters 8.4.4 Interpretation using predicted probabilities and rates 8.4.5 Computing predicted rates and probabilities in the estimation sample
8.5.1 Insample predictions for the hurdle model
8.6 Zeroinflated count models
8.5.2 Predictions for userspecified values
8.6.1 Fitting zeroinflated models with zinb and zip
8.7 Comparisons among count models
Variable lists
8.6.2 Example of fitting the ZIP and ZINB models Options 8.6.3 Interpretation of coefficients 8.6.4 Interpretation of predicted probabilities
Predicted probabilities with prvalue
Confidence intervals with prvalue Predicted probabilities with prgen
8.7.1 Comparing mean probabilities
8.8 Using countfit to compare count models
8.7.2 Tests to compare count models
LR tests of α
Vuong test of nonnested models 9 More topics
9.1 Ordinal and nominal independent variables
9.1.1 Coding a categorical independent variable as a set of dummy variables
9.2 Interactions
9.1.2 Estimation and interpretation with categorical independent variables 9.1.3 Tests with categorical independent variables
Testing the effect of membership in one category versus the reference category
9.1.4 Discrete change for categorical independent variables
Testing the effect of membership in two nonreference categories Testing that a categorical independent variable has no effect Testing whether treating an ordinal variable as interval loses information
Computing discrete change with prchange
Computing discrete change with prvalue
9.2.1 Computing sex differences in predictions with interactions
9.3 Nonlinear nonlinear models
9.2.2 Computing sex differences in discrete change with interactions
9.3.1 Adding nonlinearities to linear predictors
9.4 Using praccum and forvalues to plot predictions
9.3.2 Discrete change in nonlinear models
Options
9.4.1 Example using age and agesquared
9.5 Extending SPost to other estimation commands 9.4.2 Using forvalues with praccum 9.4.3 Using praccum for graphing a transformed variable 9.4.4 Using praccum to graph interactions 9.4.5 Using forvalues with prvalue to create tables 9.4.6 A more advanced example* 9.4.7 Using forvalues to create tables with other commands 9.6 Using Stata more efficiently
9.6.1 profile.do
9.7 Conclusions
9.6.2 Changing screen fonts and window preferences 9.6.3 Using adofiles for changing directories 9.6.4 me.hlp file A Syntax for SPost Commands
A.1 asprvalue
Syntax
A.2 brant
Description Options Examples
Syntax
A.3 case2alt
Description Options Examples Saved results
Syntax
A.4 countfit
Description Options Examples
Syntax
A.5 fitstat
Description Options for specifying the model Options to select the models to fit Options to label and save results Options to control what is printed Example
Syntax
A.6 leastlikely
Description Options Examples Saved results
Syntax
Description
A.7 listcoef
Options Options for listing Examples
Syntax
A.8 misschk
Description Options Options for nominal outcomes Examples Saved results
Syntax
A.9 mlogplot
Options Example
Syntax
A.10 mlogtest
Description Options Examples
Syntax
A.11 mlogview
Description Options Examples Saved results Acknowledgment
Syntax
A.12 Overview of prchange, prgen, prtab, and prvalue
Description Dialog box controls
Syntax
A.13 praccum
Examples
Syntax
A.14 prchange
Description Options Examples Variables generated
Syntax
A.15 prcounts
Description Options Examples
Syntax
A.16 prgen
Description Options Variables generated Examples
Syntax
A.17 prtab
Description Options Options for confidence intervals and marginals Examples Variables generated
Syntax
A.18 prvalue
Description Options Examples
Syntax
A.19 spex
Description Options Options for confidence intervals Options used for bootstrapped confidence intervals Examples Saved results
Syntax
Description Options Examples 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 B.8 wlsrnk References
Author index (pdf)
Subject index (pdf)
