Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model

Autor/inn/en	Akaeze, Hope O.; Lawrence, Frank R.; Wu, Jamie Heng-Chieh
Titel	Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model
Quelle	In: Educational and Psychological Measurement, 83 (2023) 1, S.93-115 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Akaeze, Hope O.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221082688
Schlagwörter	Measures (Individuals); Multidimensional Scaling; Tests; Hierarchical Linear Modeling; Measurement Techniques; Statistical Analysis; Models; Preschool Children; Child Development; Michigan + Suchen Sie Ihr Suchwort? Messdaten; Mehrdimensionale Analysis; Examination; Prüfung; Examen; Messtechnik; Statistische Analyse; Analogiemodell; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Kindesentwicklung
Abstract	Multidimensionality and hierarchical data structure are common in assessment data. These design features, if not accounted for, can threaten the validity of the results and inferences generated from factor analysis, a method frequently employed to assess test dimensionality. In this article, we describe and demonstrate the application of the multilevel bifactor model to address these features in examining test dimensionality. The tool for this exposition is the Child Observation Record Advantage 1.5 (COR-Adv1.5), a child assessment instrument widely used in Head Start programs. Previous studies on this assessment tool reported highly correlated factors and did not account for the nesting of children in classrooms. Results from this study show how the flexibility of the multilevel bifactor model, together with useful model-based statistics, can be harnessed to judge the dimensionality of a test instrument and inform the interpretability of the associated factor scores. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01