Hierarchical Data Structures, Institutional Research, and Multilevel Modeling

Autor/inn/en	O'Connell, Ann A.; Reed, Sandra J.
Titel	Hierarchical Data Structures, Institutional Research, and Multilevel Modeling
Quelle	In: New Directions for Institutional Research, (2012) 154, S.5-22 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0271-0579
DOI	10.1002/ir.20011
Schlagwörter	Institutional Research; Fundamental Concepts; Statistical Analysis; Models; Multivariate Analysis; Comparative Analysis; Heterogeneous Grouping; Researchers; Disproportionate Representation; Student Diversity; Sample Size + Suchen Sie Ihr Suchwort? Institutionelle Forschung; Grundlagenplan; Konzept; Statistische Analyse; Analogiemodell; Multivariate Analyse; Researcher; Forscher
Abstract	Multilevel modeling (MLM), also referred to as hierarchical linear modeling (HLM) or mixed models, provides a powerful analytical framework through which to study colleges and universities and their impact on students. Due to the natural hierarchical structure of data obtained from students or faculty in colleges and universities, MLM offers many advantages to analysts and policy makers involved in institutional research (IR). This article introduces fundamental concepts of hierarchy and its statistical treatment specifically for data structures occurring in IR settings. It provides an introduction to multilevel modeling, including the impact of clustering and the intraclass correlation coefficient. Prototypical research questions in institutional research are examined, and an example is provided to illustrate the application and interpretation of multilevel models. (Contains 1 figure.) (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10