Multivariate Models for Normal and Binary Responses in Intervention Studies

Autor/inn/en	Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen
Titel	Multivariate Models for Normal and Binary Responses in Intervention Studies
Quelle	In: American Journal of Evaluation, 37 (2016) 2, S.270-286 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1098-2140
DOI	10.1177/1098214015626297
Schlagwörter	Intervention; Multivariate Analysis; Mixed Methods Research; Models; Data Interpretation; At Risk Persons; Research Design; Experiments; Randomized Controlled Trials; Hierarchical Linear Modeling; Correlation; Comparative Analysis; Statistics; Equations (Mathematics); Scores; Experimental Groups; Control Groups; Federal Programs; Early Childhood Education; Preschool Children; Disadvantaged Youth; Low Income; School Readiness; Cognitive Development; Child Development; Infants; Bayley Mental Development Index; Bayley Scales of Infant Development + Suchen Sie Ihr Suchwort? Multivariate Analyse; Analogiemodell; Data evaluation; Datenauswertung; Risikogruppe; Forschungsdesign; Erprobung; Korrelation; Statistik; Equations; Mathematics; Gleichungslehre; Early childhood; Education; Frühkindliche Bildung; Frühpädagogik; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Benachteiligter Jugendlicher; Niedriglohn; Readiness for school; School ability; Schulreife; Kognitive Entwicklung; Kindesentwicklung; Infant; Toddler; Toddlers; Kleinkind
Abstract	Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in intervention studies and analysis models that can simultaneously include such outcomes are available, we found very limited use of these models in intervention research. To encourage greater use of multivariate analysis for mixed outcomes, this article highlights the benefits and describes important features of models that can incorporate a mix of normal and binary outcomes. Models for intervention research are then fit using Mplus and results interpreted using data from an evaluation of the Early Head Start program, a randomized trial designed to improve child outcomes for an at-risk population. The models illustrated estimate treatment effects for mixed responses in standard and multilevel experimental designs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01