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

Autor/inn/en	Stapleton, Laura M.; McNeish, Daniel M.; Yang, Ji Seung
Titel	Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered
Quelle	In: Educational Psychologist, 51 (2016) 3-4, S.317-330 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0046-1520
DOI	10.1080/00461520.2016.1207178
Schlagwörter	Hierarchical Linear Modeling; Data Analysis; Statistical Data; Multivariate Analysis; Factor Analysis; Factor Structure; Randomized Controlled Trials; Cluster Grouping; Educational Research; Hypothesis Testing; Statistical Bias; Statistical Inference; Research Problems; Research Methodology; Probability + Suchen Sie Ihr Suchwort? Auswertung; Multivariate Analyse; Faktorenanalyse; Faktorenstruktur; Eingruppierung; Bildungsforschung; Pädagogische Forschung; Hypothesenprüfung; Hypothesentest; Inferential statistics; Schließende Statistik; Forschungskritik; Research method; Forschungsmethode; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01