Multilevel and Longitudinal Modeling Using Stata

By Nicholas J. Horton. 2006

934 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/da2d54bbd0acee86f12e3553cc1a7c3d890ac0de

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Citations in the corpus

Bias in Factor Score Regression and a Simple Solution (2011, background): “Thus “estimation can be quite slow, especially if there are several random effects” (Rabe-Hesketh & Skrondal, 2005, p. 128).”
Review of Multilevel and Longitudinal Modeling Using Stata, Second Edition, by Sophia Rabe-Hesketh and Anders Skrondal (2008, background): “…317 pages plus front matter) did an excellent job describing and illustrating multilevel, longitudinal, and clustered regression methods in the context of real-world examples and implementations within Stata, but it fell somewhat short as a comprehensive introduction to this material (Horton 2006).”
A Multilevel Analysis of Statewide Disproportionality in Exclusionary Discipline and the Identification of Emotional Disturbance (2019, background): “Although the approximation does carry some risk of biased estimates when the clusters are small (Rabe-Hesketh & Skrondal, 2012), our school-level clusters of individual data are quite large.”
On Group Comparisons With Logistic Regression Models (2020, background): “Some of these points have previously been made, although in somewhat di↵erent language, by Rohwer (2012) and Buis (2016).”
Public support for redistribution: what explains cross-national differences? (2010, background): “Furthermore, a multilevel regression facilitates an assessment of the contribution of the different levels in explaining the demand for equality (Rabe-Hesketh and Skrondal, 2005; Hans, 2006).”
The influence of the family, the school, and the group on the environmental attitudes of European students (2017, unknown): “Nevertheless, a hierarchical linear model including two random intercepts, at the school and country levels, has also been estimated (Rabe-Hesketh and Skrondal 2005).”