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Multilevel and longitudinal modeling using stata

Author: Sophia Rabe-Hesketh; Anders Skrondal
Publisher: College Station, Tex. : Stata Press, 2008.
Edition/Format:   Print book : English : 2. edView all editions and formats
Summary:
Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Sophia Rabe-Hesketh; Anders Skrondal
ISBN: 1597180408 9781597180405
OCLC Number: 254653596
Notes: Literaturverz. S. [535] - 547.
Description: XXXIII, 562 Seiten : Diagramme
Contents: 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 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 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 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 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 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 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 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 Appendix A: Syntax for gllamm, eq, and gllapred Appendix B: Syntax for gllamm Appendix C: Syntax for gllapred Appendix D: Syntax for gllasim References Index A Summary, Further Reading, and Exercises appear at the end of each chapter.
Responsibility: Sophia Rabe-Hesketh ; Anders Skrondal.

Abstract:

Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels. Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. The applications and exercises span a wide range of disciplines, making the book suitable for courses on multilevel and longitudinal modeling in the medical, social, and behavioral sciences and in applied statistics.

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"... a considerably expanded version, nearly double the size of the original. Much of the added material serves to delineate more clearly between statistics and software. ... throughout the book, Read more...

 
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