Advantages of Hierarchical Linear Modeling.

By Jason W. Osborne. 2000

322 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bd1e03b3d7567a1454dcda91aba3173f88e4c449

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Citations in the corpus

Academic and educational outcomes of children with ADHD. (2007, methodology): “and that use statistical methods such as hierarchical linear modeling.(57) In this approach, individual students are nested in hierarchies that are defined by grade and”
A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data (2020, background): “For a detailed description and decription of HLM, readers are referred to Osborne (2000), Garson (2012), and Woltman et al. (2012).”
An introduction to hierarchical linear modeling (2012, methodology): “Effect size estimates and standard errors remain undistorted and the potentially meaningful variance overlooked using disaggregation or aggregation is retained (Beaubien, Hamman, Holt & Boehm-Davis, 2001; Gill, 2003; Osborne, 2000).”
A Reliability Generalization Study of the Marlowe-Crowne Social Desirability Scale (2002, background): “It is sometimes possible to detect significant relationships under amixed-effectsmodel that can be masked when using fixed-effects models (Osborne, 2000).”
Understanding the importance of sport infrastructure for participation in different sports – findings from multi-level modeling (2012, methodology): “As a result, multi-level analyses must be applied when using hierarchical data, as only this type of procedure measures and analyzes the data on the appropriate level (Osborne, 2000). An alphalevel of 0.1 is used for all statistical tests. The evaluation of the multi-level models is undertaken in th”