Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

Autor/inn/en	Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.
Titel	Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses
Quelle	39 (2015) 8, S.627-642 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1177/0146621615591094
Schlagwörter	Structural Equation Models; Hierarchical Linear Modeling; Intervention; Program Effectiveness; Pretests Posttests; Scores; Differences; Error of Measurement; Comparative Analysis; Middle School Students; Computation; Mathematics Tests; Fractions; Statistical Analysis + Suchen Sie Ihr Suchwort? Unterscheiden; Messfehler; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Bruchrechnung; Statistische Analyse
Abstract	Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01