Assessment of Dimensionality in Dichotomously-Scored Data Using Multidimensional Scaling: Analysis of HSMB Data.

Autor/in	Jones, Patricia B.
Titel	Assessment of Dimensionality in Dichotomously-Scored Data Using Multidimensional Scaling: Analysis of HSMB Data.
Quelle	(1988), (33 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Discriminant Analysis; Factor Analysis; Language Tests; Mathematics Tests; Multidimensional Scaling; Preschool Children; Preschool Education; Preschool Tests; Social Development; Statistical Analysis + Suchen Sie Ihr Suchwort? Diskriminanzanalyse; Faktorenanalyse; Language test; Sprachtest; Mehrdimensionale Analysis; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Soziale Entwicklung; Statistische Analyse
Abstract	Data from three subtests (language, mathematics, and social) scales of the Head Start Measures Battery (HSMB) were analyzed using principal components analysis (PCA) and non-metric multidimensional scaling (MDS). The HSMB measures preschool development in language, mathematics, nature and science, perception, reading, and social development. The sample included 1,000 children (36-60 months old) who participated in the Head Start Program and received all language, mathematics, and social routing items in fall of 1985. Loadings on the first factor were high, and eigenvalues obtained from the PCA suggested that one (or possibly two) dimensions were present in the data. Loadings of rotated principal components suggested that at least three factors corresponding to the subtests were present. The structure was clarified by MDS plots, showing that items were located in distinct sectors corresponding to their subtest. However, items having the highest IRT discrimination parameters were clustered toward the center, suggesting that the measures have a strong common factor and unique variance related to each subtest. Practically, this justifies both a common scale for all subtests when an overall measure of achievement is needed and individual subtest scaling when information on particular skills is required. Agreement between PCA and MDS can be used to reinforce the validity of the principal components model. Seven tables and four figures are provided. (SLD)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC