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书名：multilevel and longitudinal modeling using stata
版本：第二版
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Table of contents
1 Linear variance-components models
1.1 Introduction
1.2 How reliable are expiratory flow measurements?
1.3 The variance-components model
1.6 Assigning values to the random intercepts
1.8 Exercises
1.2 How reliable are expiratory flow measurements?
1.3 The variance-components model
1.3.1 Model specification and path diagram
1.3.2 Error components, variance components, and reliability
1.3.3 Intraclass correlation
1.4 Modeling the Mini Wright measurements 1.3.2 Error components, variance components, and reliability
1.3.3 Intraclass correlation
1.4.1 Estimation using xtreg
1.4.2 Estimation using xtmixed
1.4.3 Estimation using gllamm
1.4.4 Relative and absolute agreement
1.5 Estimation methods 1.4.2 Estimation using xtmixed
1.4.3 Estimation using gllamm
1.4.4 Relative and absolute agreement
1.6 Assigning values to the random intercepts
1.6.1 Maximum likelihood estimation
1.6.3 Empirical Bayes variances
1.7 Summary and further reading Implementation via OLS regression
Implementation via the mean total residual
1.6.2 Empirical Bayes prediction Implementation via the mean total residual
1.6.3 Empirical Bayes variances
1.8 Exercises
2 Linear random-intercept models
2.1 Introduction
2.2 Are tax preparers useful?
2.3 The longitudinal data structure
2.4 Panel data and correlated residuals
2.5 The random-intercept model
2.8 Residual diagnostics
2.9 Summary and further reading
2.10 Exercises
2.2 Are tax preparers useful?
2.3 The longitudinal data structure
2.4 Panel data and correlated residuals
2.5 The random-intercept model
2.5.1 Estimation using xtreg
2.5.2 Estimation using xtmixed
2.6 Different kinds of effects in panel models 2.5.2 Estimation using xtmixed
2.6.1 Between-taxpayer effects
2.6.2 Within-taxpayer effects
2.6.3 Relations among the estimators
2.7 Endogeneity and between-taxpayer effects 2.6.2 Within-taxpayer effects
2.6.3 Relations among the estimators
2.8 Residual diagnostics
2.9 Summary and further reading
2.10 Exercises
3 Linear random-coefficient and growth-curve models
3.1 Introduction
3.2 How effective are different schools?
3.3 Separate linear regressions for each school
3.4 The random-coefficient model
3.6 Growth-curve modeling
3.9 Complex level-1 variation or heteroskedasticity
3.10 Summary and further reading
3.11 Exercises
3.2 How effective are different schools?
3.3 Separate linear regressions for each school
3.4 The random-coefficient model
3.4.1 Specification and interpretation of a random-coefficient model
3.4.2 Estimation and prediction using xtmixed
3.5 How do children grow? 3.4.2 Estimation and prediction using xtmixed
Estimation of random-intercept model
Estimation of random-coefficient model
Empirical Bayes prediction using xtmixed
3.4.3 Estimation and prediction using gllamm Estimation of random-coefficient model
Empirical Bayes prediction using xtmixed
Estimation of random-intercept model
Estimation of random-coefficient model
Empirical Bayes prediction
Estimation of random-coefficient model
Empirical Bayes prediction
3.6 Growth-curve modeling
3.6.1 Observed growth trajectories
3.6.2 Estimation using xtmixed
3.7 Two-stage model formulation 3.6.2 Estimation using xtmixed
Quadratic growth model with random intercept
Quadratic growth model with random intercept and random slope
Including a child-level covariate
3.6.3 Estimation using gllamm Quadratic growth model with random intercept and random slope
Including a child-level covariate
Quadratic growth model with random intercept
Quadratic growth model with random intercept and random slope
Including a child-level covariate
Quadratic growth model with random intercept and random slope
Including a child-level covariate
3.7.1 Model specification
3.7.2 Estimation
3.8 Prediction of trajectories for individual children 3.7.2 Estimation
3.9 Complex level-1 variation or heteroskedasticity
3.10 Summary and further reading
3.11 Exercises
4 Dichotomous or binary responses
4.1 Models for dichotomous responses
4.3 The longitudinal data structure
4.4 Population-averaged or marginal probabilities
4.5 Random-intercept logistic regression
4.6 Subject-specific vs. population-averaged relationships
4.7 Maximum likelihood estimation using adaptive quadrature
4.11 Exercises
4.1.1 Generalized linear model formulation
4.1.2 Latent-response formulation
4.2 Which treatment is best for toenail infection? 4.1.2 Latent-response formulation
Logistic regression
Probit regression
Probit regression
4.3 The longitudinal data structure
4.4 Population-averaged or marginal probabilities
4.5 Random-intercept logistic regression
4.6 Subject-specific vs. population-averaged relationships
4.7 Maximum likelihood estimation using adaptive quadrature
4.7.1 Some practical considerations
4.8 Empirical Bayes (EB) predictions 4.8.1 EB prediction of random effects
4.8.2 EB prediction of response probabilities
4.9 Other approaches to clustered dichotomous data 4.8.2 EB prediction of response probabilities
4.9.1 Conditional logistic regression
4.9.2 Generalized estimating equations (GEE)
4.10 Summary and further reading 4.9.2 Generalized estimating equations (GEE)
4.11 Exercises
5 Ordinal responses
5.1 Introduction
5.2 Cumulative models for ordinal responses
5.4 Longitudinal data structure and graphs
5.10 A random-intercept model with grader bias
5.14 Exercises
5.2 Cumulative models for ordinal responses
5.2.1 Generalized linear model formulation
5.2.2 Latent-response formulation
5.2.3 Proportional odds
5.2.4 Identification
5.3 Are antipsychotic drugs effective for patients with schizophrenia? 5.2.2 Latent-response formulation
5.2.3 Proportional odds
5.2.4 Identification
5.4 Longitudinal data structure and graphs
5.4.1 The longitudinal data structure
5.4.2 Plotting cumulative proportions
5.4.3 Plotting cumulative logits and transforming the time scale
5.5 A proportional-odds model 5.4.2 Plotting cumulative proportions
5.4.3 Plotting cumulative logits and transforming the time scale
5.5.1 Model specification
5.5.2 Estimation
5.6 A random-intercept proportional-odds model 5.5.2 Estimation
5.6.1 Model specification
5.6.2 Estimation
5.7 A random-coefficient proportional-odds model 5.6.2 Estimation
5.7.1 Model specification
5.7.2 Estimation
5.8 Marginal and patient-specific probabilities 5.7.2 Estimation
5.8.1 Marginal probabilities
5.8.2 Patient-specific cumulative response probabilities
5.9 Do experts differ in their grading of student essays? 5.8.2 Patient-specific cumulative response probabilities
5.10 A random-intercept model with grader bias
5.10.1 Model specification
5.10.2 Estimation
5.11 Including grader-specific measurement error variances 5.10.2 Estimation
5.11.1 Model specification
5.11.2 Estimation
5.12 Including grader-specific thresholds 5.11.2 Estimation
5.12.1 Model specification
5.12.2 Estimation
5.13 Summary and further reading 5.12.2 Estimation
5.14 Exercises
6 Counts
6.1 Introduction
6.2 Types of counts
6.3 Poisson model for counts
6.4 Did the German health-care reform reduce the number of doctor visits?
6.5 Longitudinal data structure
6.6 Poisson regression ignoring overdispersion and clustering
6.12 Standardized mortality ratios
6.13 Random-intercept Poisson regression
6.16 Exercises
6.2 Types of counts
6.3 Poisson model for counts
6.4 Did the German health-care reform reduce the number of doctor visits?
6.5 Longitudinal data structure
6.6 Poisson regression ignoring overdispersion and clustering
6.6.1 Model specification
6.6.2 Estimation
6.7 Poisson regression with overdispersion but ignoring clustering 6.6.2 Estimation
6.7.1 Using a level-1 random intercept
6.8 Random-intercept Poisson regression Model specification
Estimation
6.7.2 Quasilikelihood Estimation
Specification
Estimation
Estimation
6.8.1 Model specification
6.8.2 Estimation
6.9 Random-coefficient Poisson regression 6.8.2 Estimation
6.9.1 Model specification
6.9.2 Estimation
6.10 Other approaches to clustered counts 6.9.2 Estimation
6.10.1 Conditional Poisson regression
6.10.2 Generalized estimating equations (GEE)
6.11 Which Scottish countries have a high risk of lip cancer? 6.10.2 Generalized estimating equations (GEE)
6.12 Standardized mortality ratios
6.13 Random-intercept Poisson regression
6.13.1 Model specification
6.13.2 Estimation
6.13.3 Introducing a county-level covariate
6.13.4 Prediction
6.14 Nonparametric maximum likelihood estimation 6.13.2 Estimation
6.13.3 Introducing a county-level covariate
6.13.4 Prediction
6.14.1 Specification
6.14.2 Estimation
6.14.3 Prediction
6.15 Summary and further reading 6.14.2 Estimation
6.14.3 Prediction
6.16 Exercises
7 Higher level models and nested random effects
7.1 Introduction
7.2 Which method is best for measuring expiratory flow?
7.3 Two-level variance-components models
7.6 A three-level logistic random-intercept model
7.8 Exercises
7.2 Which method is best for measuring expiratory flow?
7.3 Two-level variance-components models
7.3.1 Model specification
7.3.2 Estimation
7.4 Three-level variance-components models 7.3.2 Estimation
7.4.1 Model specification
7.4.2 Different types of intraclass correlation
7.4.3 Three-stage formulation
7.4.4 Estimation using xtmixed
7.4.5 Prediction using xtmixed
7.5 Did the Guatemalan immunization campaign work? 7.4.2 Different types of intraclass correlation
7.4.3 Three-stage formulation
7.4.4 Estimation using xtmixed
7.4.5 Prediction using xtmixed
7.6 A three-level logistic random-intercept model
7.6.1 Model specification
7.6.2 Different types of intraclass correlations for the latent responses
7.6.3 Three-stage formulation
7.6.4 Estimation
7.6.5 Introducing a random coefficient at level 3
7.6.6 Prediction
7.7 Summary and further reading 7.6.2 Different types of intraclass correlations for the latent responses
7.6.3 Three-stage formulation
7.6.4 Estimation
7.6.5 Introducing a random coefficient at level 3
7.6.6 Prediction
7.8 Exercises
8 Crossed random effects
8.1 Introduction
8.2 How does investment depend on expected profit and capital stock?
8.3 A two-way error-components model
8.5 An additive crossed random-effects model
8.8 Summary and further reading
8.9 Exercises
8.2 How does investment depend on expected profit and capital stock?
8.3 A two-way error-components model
8.3.1 Model specification
8.3.2 Intraclass correlations
8.3.3 Estimation
8.3.4 Prediction
8.4 How much do primary and secondary schools affect attainment at age 16? 8.3.2 Intraclass correlations
8.3.3 Estimation
8.3.4 Prediction
8.5 An additive crossed random-effects model
8.5.1 Specification
8.5.2 Estimation
8.6 Including a random interaction 8.5.2 Estimation
8.6.1 Model specification
8.6.2 Intraclass correlations
8.6.3 Estimation
8.6.4 Some diagnostics
8.7 A trick requiring fewer random effects8.6.2 Intraclass correlations
8.6.3 Estimation
8.6.4 Some diagnostics
8.8 Summary and further reading
8.9 Exercises
A Syntax for gllamm, eq, and gllapred
B Syntax for gllamm
C Syntax for gllapred
D Syntax for gllasim
References
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