Multilevel and Longitudinal Modeling Using Stata Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Multilevel and Longitudinal Modeling Using Stata By Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Summary

Explains the models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. This text develops the mixed model from first principles, familiarizing with terminology, summarizing and relating the widely used estimating strategies, and providing historical perspective.

Multilevel and Longitudinal Modeling Using Stata by Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Presenting a thorough and accessible treatment of generalized linear mixed models, also known as multilevel or hierarchical models, Multilevel and Longitudinal Modeling Using Stata explains the models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results.

Beginning with the comparatively simple random-intercept linear model without covariates, the text develops the mixed model from first principles, familiarizing the reader with terminology, summarizing and relating the widely used estimating strategies, and providing historical perspective. Once this mixed-model foundation has been established, the text smoothly transitions to random-intercept models with covariates and then to random-coefficient models. The middle chapters apply the concepts defined earlier for Gaussian models to models for binary responses (e.g., logit and probit), ordinal responses (e.g., ordered logit and ordered probit), and count responses (e.g., Poisson). Models with multiple levels of random variation are then considered, as well as models with crossed (nonnested) random effects.

The most complete and up-to-date depiction of Statas capacity for fitting generalized linear mixed models, Multilevel and Longitudinal Modeling Using Stata serves as an ideal introduction for Stata users wishing to learn about this powerful data-analysis tool.

Preface
LINEAR VARIANCE-COMPONENTS MODELS
Introduction
How reliable are expiratory flow measurements?
The variance-components model
Modeling the Mini Wright measurements
Estimation methods
Assigning values to the random intercepts
Summary and further reading
Exercises
LINEAR RANDOM-INTERCEPT MODELS
Introduction
Are tax preparers useful?
The longitudinal data structure
Panel data and correlated residuals
The random-intercept model
Different kinds of effects in panel models
Endogeneity and between-taxpayer effects
Residual diagnostics
Summary and further reading
Exercises
LINEAR RANDOM-COEFFICIENT AND GROWTH-CURVE MODELS
Introduction
How effective are different schools?
Separate linear regressions for each school
The random-coefficient model
How do children grow?
Growth-curve modeling
Two-stage model formulation
Prediction of trajectories for individual children
Complex level-1 variation or heteroskedasticity
Summary and further reading
Exercises
DICHOTOMOUS OR BINARY RESPONSES
Models for dichotomous responses
Which treatment is best for toenail infection?
The longitudinal data structure
Population-averaged or marginal probabilities
Random-intercept logistic regression
Subject-specific vs. population-averaged relationships
Maximum likelihood estimation using adaptive quadrature
Empirical Bayes (EB) predictions
Other approaches to clustered dichotomous data
Summary and further reading
Exercises
ORDINAL RESPONSES
Introduction
Cumulative models for ordinal responses
Are antipsychotic drugs effective for patients with schizophrenia?
Longitudinal data structure and graphs
A proportional-odds model
A random-intercept proportional-odds model
A random-coefficient proportional-odds model
Marginal and patient-specific probabilities
Do experts differ in their grading of student essays?
A random-intercept model with grader bias
Including grader-specific measurement error variances
Including grader-specific thresholds
Summary and further reading
Exercises
COUNTS
Introduction
Types of counts
Poisson model for counts
Did the German health-care reform reduce the number of doctor visits?
Longitudinal data structure
Poisson regression ignoring overdispersion and clustering
Poisson regression with overdispersion but ignoring clustering
Random-intercept Poisson regression
Random-coefficient Poisson regression
Other approaches to clustered counts
Which Scottish countries have a high risk of lip cancer?
Standardized mortality ratios
Random-intercept Poisson regression
Nonparametric maximum likelihood estimation
Summary and further reading
Exercises
HIGHER LEVEL MODELS AND NESTED RANDOM EFFECTS
Introduction
Which method is best for measuring expiratory flow?
Two-level variance-components models
Three-level variance-components models
Did the Guatemalan immunization campaign work?
A three-level logistic random-intercept model
Summary and further reading
Exercises
CROSSED RANDOM EFFECTS
Introduction
How does investment depend on expected profit and capital stock?
A two-way error-components model
How much do primary and secondary schools affect attainment at age 16?
An additive crossed random-effects model
Including a random interaction
A trick requiring fewer random effects
Summary and further reading
Exercises
APPENDIX A: Syntax for gllamm, eq, and gllapred
APPENDIX B: Syntax for gllamm
APPENDIX C: Syntax for gllapred
APPENDIX D: Syntax for gllasim
References
Author Index
Subject Index

Multilevel and Longitudinal Modeling Using Stata Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Multilevel and Longitudinal Modeling Using Stata by Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Summary

Multilevel and Longitudinal Modeling Using Stata Summary

Multilevel and Longitudinal Modeling Using Stata by Sophia Rabe-Hesketh (University of California, Berkeley, USA)

Table of Contents

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