Multilevel Modeling with Stat-JR SAAs: A Software Review

Autor/inn/en	Kim, Minjung; Hsu, Hsien-Yuan
Titel	Multilevel Modeling with Stat-JR SAAs: A Software Review
Quelle	In: Journal of Educational and Behavioral Statistics, 44 (2019) 1, S.103-121 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998618811383
Schlagwörter	Hierarchical Linear Modeling; Statistical Analysis; Computer Software; Computer Software Evaluation; Academic Achievement; High School Students; Educational Research + Suchen Sie Ihr Suchwort? Statistische Analyse; Softwareanalyse; Schulleistung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Bildungsforschung; Pädagogische Forschung
Abstract	Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5), with regard to their use for MLM. In this article, we review the features of Stat-JR's SAAs and illustrate how to implement SAAs, using one of the Stat-JR interfaces to analyze multilevel models for the 1982 High School and Beyond data set. Results from Stat-JR SAA are compared with the results using HLM7.01 software. We also discuss recommendations and implications for future users of SAAs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01