Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered

• Published in: Educational psychologist Vol. 51; no. 3-4; pp. 317 - 330
• Main Authors: Stapleton, Laura M., McNeish, Daniel M., Yang, Ji Seung
• Format: Journal Article
• Language: English
• Published: Philadelphia Routledge 01.10.2016; Taylor & Francis Ltd
• Subjects: Cluster analysis; Cluster Grouping; Data Analysis; Educational psychology; Educational Research; Factor Analysis; Factor Structure; Hierarchical Linear Modeling; Hypothesis Testing; Multivariate Analysis; Probability; Randomized Controlled Trials; Research Methodology; Research Problems; Statistical Bias; Statistical Data; Statistical Inference
• ISSN: 0046-1520; 1532-6985
• DOI: 10.1080/00461520.2016.1207178

Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002 ), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003 ; Sterba, 2009 ). The overarching goal of this article is to suggest when it is appropriate and advantageous to analyze such nested data within a single-level framework and when utilization of multilevel models presents advantages. The decision hinges on the research questions to be addressed, the scope of the data, and the measurement structure of any constructs hypothesized at the cluster level (Kozolowski & Klein, 2000 ; Marsh et al., 2012 ). We demonstrate models using several different data sets, including single-level and multilevel hierarchical linear models and confirmatory factor models. For these demonstrations, observational data from students nested within schools are used, as well as data from a classroom-based cluster randomized trial.