1 Linear variance-components models

1.1 Introduction
1.2 How reliable are expiratory flow measurements?
1.3 The variance-components model 1.4 Modeling the Mini Wright measurements 1.5 Estimation methods
1.6 Assigning values to the random intercepts 1.7 Summary and further reading
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 2.6 Different kinds of effects in panel models 2.7 Endogeneity and between-taxpayer effects
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 3.5 How do children grow?
3.6 Growth-curve modeling 3.7 Two-stage model formulation 3.8 Prediction of trajectories for individual children
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 4.2 Which treatment is best for toenail infection?
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 4.8 Empirical Bayes (EB) predictions 4.9 Other approaches to clustered dichotomous data 4.10 Summary and further reading
4.11 Exercises

5 Ordinal responses

5.1 Introduction
5.2 Cumulative models for ordinal responses 5.3 Are antipsychotic drugs effective for patients with schizophrenia?
5.4 Longitudinal data structure and graphs 5.5 A proportional-odds model 5.6 A random-intercept proportional-odds model 5.7 A random-coefficient proportional-odds model 5.8 Marginal and patient-specific probabilities 5.9 Do experts differ in their grading of student essays?
5.10 A random-intercept model with grader bias 5.11 Including grader-specific measurement error variances 5.12 Including grader-specific thresholds 5.13 Summary and further reading
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 6.7 Poisson regression with overdispersion but ignoring clustering 6.8 Random-intercept Poisson regression 6.9 Random-coefficient Poisson regression 6.10 Other approaches to clustered counts 6.11 Which Scottish countries have a high risk of lip cancer?
6.12 Standardized mortality ratios
6.13 Random-intercept Poisson regression 6.14 Nonparametric maximum likelihood estimation 6.15 Summary and further reading
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 7.4 Three-level variance-components models 7.5 Did the Guatemalan immunization campaign work?
7.6 A three-level logistic random-intercept model 7.7 Summary and further reading
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 8.4 How much do primary and secondary schools affect attainment at age 16?
8.5 An additive crossed random-effects model 8.6 Including a random interaction 8.7 A trick requiring fewer random effects
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

Author index
  (pdf)

Subject index
  (pdf)