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资料来源: http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/
Regression Models for Categorical Dependent Variables Using Stata, Third Edition
| Authors: | J. Scott Long and Jeremy Freese |
| Publisher: | Stata Press |
| Copyright: | 2014 |
| ISBN-13: | 978-1-59718-111-2 |
| Pages: | 589; paperback |
| Price: | $64.00 |
与本书配套的spost13 ado, do 文件 可通过输入如下命令查看和安装
findit spost13
Comment from the Stata technical groupRegression Models for Categorical Dependent Variables Using Stata,Third Edition, by J. Scott Long and Jeremy Freese, is an essentialreference for those who use Stata to fit and interpret regression modelsfor categorical data. Although regression models for categoricaldependent variables are common, few texts explain how to interpret suchmodels; this text decisively fills the void.
The third edition is divided into two parts. Part I begins with anexcellent introduction to Stata and follows with general treatments ofthe estimation, testing, fitting, and interpretation of models forcategorical dependent variables. The book is thus accessible to newusers of Stata and those who are new to categorical dataanalysis. Part II is devoted to a comprehensive treatment of estimationand interpretation for binary, ordinal, nominal, and count outcomes.
Readers familiar with previous editions will find many changes in thethird edition. An entire chapter is now devoted to interpretation ofregression models using predictions. This concept is explored ingreater depth in Part II. The authors also discuss how manyimprovements made to Stata in recent years—factor variables,marginal effects with margins, plotting predictions usingmarginsplot—facilitate analysis of categorical data.
The authors advocate a variety of new methods that usepredictions to interpret the effect of variables in regression models.Readers will find all discussion of statistical concepts firmly groundedin concrete examples. All the examples, datasets, and author-writtencommands are available on the authors' website, so readers can easilyreplicate the examples with Stata.
Examples in the new edition also illustrate changes to the authors'popular SPost commands after a recent rewrite inspired by the authors'evolving views on interpretation. Readers will note that SPost nowtakes full advantage of the power of the margins command and theflexibility of factor-variable notation. Long and Freese also provide asuite of new commands, including mchange, mtable, and mgen.These commands complement margins, aiding model interpretation,hypothesis testing, and model diagnostics. They offer the same syntacticalconvenience features that users of Stata expect, for example including powersor interactions of covariates in regression models and seamlessly working withcomplex survey data. The authors also discuss how to use these commands toestimate marginal effects, either averaged over the sample or evaluated atfixed values of the regressors.
The third edition of Regression Models for Categorical DependentVariables Using Stata continues to provide the same high-quality, practicaltutorials of previous editions. It also offers significant improvementsover previous editions—new content, updated information aboutStata, and updates to the authors' own commands. This book should be on thebookshelf of every applied researcher analyzing categorical data and isan invaluable learning resource for students and others who are new tocategorical data analysis.
Table of contents
View table of contents >>
Table of contents (pdf)List of figures
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 The SPost software 1.5.1 Updating Stata
1.5.2 Installing SPost13 Uninstalling SPost9
Installing SPost13 using search
Installing SPost13 using net install
1.5.3 Uninstalling SPost13
1.6 Sample do-files and datasets 1.6.1 Installing the spost13_do package
1.6.2 Using spex to load data and run examples
1.7 Getting help with SPost 1.7.1 What if an SPost command does not work?
1.7.2 Getting help from the authors What we need to help you
1.8 Where can I learn more about the models?
2 Introduction to Stata
2.1 The Stata interface
2.2 Abbreviations
2.3 Getting help 2.3.1 Online help
2.3.2 PDF manuals
2.3.3 Error messages
2.3.4 Asking for help
2.3.5 Other resources
2.4 The working directory
2.5 Stata file types
2.6 Saving output to log files
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 Recommended structure for do-files
2.10 Using Stata for serious data analysis
2.11 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 The generate command
2.13.2 The replace command
2.13.3 The recode command
2.14 Labeling variables and values 2.14.1 Variable labels
2.14.2 Value labels
2.14.3 The notes command
2.15 Global and local macros
2.16 Loops using foreach and forvalues
2.17 Graphics 2.17.1 The graph command
2.18 A brief tutorial
2.19 A do-file template
2.20 Conclusion
3 Estimation, testing, and fit
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 Syntax of estimation commands
3.1.5 Variable lists
Using factor-variable notation in the variable list
Specifying interaction and polynomials
More on factor-variable notation
3.1.6 Specifying the estimation sample Missing data
Information about missing values
Postestimation commands and the estimation sample
3.1.7 Weights and survey data Complex survey designs
3.1.8 Options for regression models
3.1.9 Robust standard errors
3.1.10 Reading the estimation output
3.1.11 Storing estimation results
(Advanced) Saving estimates to a file
3.1.12 Reformatting output with estimates table
3.2 Testing 3.2.1 One-tailed and two-tailed tests
3.2.2 Wald and likelihood-ratio tests
3.2.3 Wald tests with test and testparm
3.2.4 LR tests with lrtest Avoiding invalid LR tests
3.3 Measures of fit 3.3.1 Syntax of fitstat
3.3.2 Methods and formulas used by fitstat
3.3.3 Example of fitstat
3.4 estat postestimation commands
3.5 Conclusion
4 Methods of interpretation
4.1 Comparing linear and nonlinear models
4.2 Approaches to interpretation 4.2.1 Method of interpretation based on predictions
4.2.2 Method of interpretation using parameters
4.2.3 Stata and SPost commands for interpretation
4.3 Predictions for each observation
4.4 Predictions at specified values 4.4.1 Why use the m* commands instead of margins?
4.4.2 Using margins for predictions Predictions using interaction and polynomial terms
Making multiple predictions
Predictions for groups defined by levels of categorical variables
4.4.3 (Advanced) Nondefault predictions using margins The predict() option
The expression() option
4.4.4 Tables of predictions using mtable mtable with categorical and count outcomes
(Advanced) Combining and formatting tables using mtable
4.5 Marginal effects: Changes in predictions 4.5.1 Marginal effects using margins
4.5.2 Marginal effects using mtable
4.5.3 Posting predictions and using mlincom
4.5.4 Marginal effects using mchange
4.6 Plotting predictions 4.6.1 Plotting predictions with marginsplot
4.6.2 Plotting predictions using mgen
4.7 Interpretation of parameters 4.7.1 The listcoef command
4.7.2 Standardized coefficients
4.7.3 Factor and percentage change coefficients
4.8 Next steps
Part II Models for specific kinds of outcomes
5 Models for binary outcomes: Estimation, testing, and fit
5.1 The statistical model 5.1.1 A latent-variable model
5.1.2 A nonlinear probability model
5.2 Estimation using logit and probit commands 5.2.1 Example of logit model
5.2.2 Comparing logit and probit
5.2.3 (Advanced) Observations predicted perfectly
5.3 Hypothesis testing 5.3.1 Testing individual coefficients
5.3.2 Testing multiple coefficients
5.3.3 Comparing LR and Wald tests
5.4 Predicted probabilities, residuals, and influential observations 5.4.1 Predicted probabilities using predict
5.4.2 Residuals and influential observations using predict
5.4.3 Least likely observations
5.5 Measures of fit 5.5.1 Information criteria
5.5.2 Pseudo-R²'s
5.5.3 (Advanced) Hosmer–Lemeshow statistic
5.6 Other commands for binary outcomes
5.7 Conclusion
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