Hierarchical Linear Models: Applications and Data Analysis Methods

By Marie Davidian. 2003

719 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c7fb2665cc8e2d6041432c78b7ae76dc3d72b7e4

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Citations in the corpus

Connections between attitudes, group norms, and behaviour in bullying situations (2004, background): “The classroom means show a reliability coefficient (Raudenbush & Bryk, 2002, p. 46) for anti-bullying norms equal to .78 for a typical classroom of 25 students, and for normative indifference this reliability equals .61.”
Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change (2008, background): “…one to simultaneously model change in the means (fixed effects) and in the variance and covariance of initial level and change (random effects; Bryk & Raudenbush, 2001; Duncan, Duncan, Strycker, Li, & Alpert, 1999; Laird & Ware, 1982; McArdle & Epstein, 1987; Raykov, 1993; Rogosa & Willett,…”
Longitudinal Outcomes for Mathematics Achievement for Students with Learning Disabilities (2011, background): “The error terms (e) were assumed to be independent and normally distributed (Raudenbush & Bryk, 2002).”
A Multilevel Mixture IRT Model With an Application to DIF (2010, methodology): “Intraclass correlations (ICCs, Raudenbush & Bryk, 2002) were used to determine the hierarchical structure of the data.”
Changes in Self-Esteem during the Middle School Years: A Latent Growth Curve Study of Individual and Contextual Influences. (2004, methodology): “Hierarchical linear modeling (HLM; Bryk & Raudenbush, 1987, 1992; Raudenbush & Bryk, 2002) was used for the analyses.”
Predicting Teacher Commitment Using Principal and Teacher Efficacy Variables: An HLM Approach (2011, background): “That would prompt setting collective efficacy with a fixed slope (Raudenbush & Bryk, 2002).”
Dimensions of Student Engagement in American Community Colleges: Using the Community College Student Report in Research and Practice (2008, background): “The nestings make the data a typical multilevel structure in which respondents within an institution are expected to have correlated responses as a result of attending the same institution that are accounted for through random effects (Raudenbush & Bryk, 2002).”
Exploring pro-environmental food purchasing behaviour: An empirical analysis of Italian consumers (2018, methodology): “…specification in which a region-specific (level-2) random intercept is included in the linear predictor thus enabling us to explicitly model the 𝑏01 hierarchical structure of the data and the unobserved heterogeneity (Goldstein, 2011; Skrondal and Rabe-Hesketh, 2004; Raudenbush and Bryk, 2002).”