A Comparison of Hierarchical and Nonhierarchical Logistic Regression for Estimating Cutoff Scores in Course Placement.

Autor/inn/en	Schulz, E. Matthew; Betebenner, Damian; Ahn, Meeyeon
Titel	A Comparison of Hierarchical and Nonhierarchical Logistic Regression for Estimating Cutoff Scores in Course Placement.
Quelle	(2002), (36 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Algebra; College Students; Comparative Analysis; Cutting Scores; Estimation (Mathematics); Higher Education; Regression (Statistics); Sample Size + Suchen Sie Ihr Suchwort? Collegestudent; Estimation; Mathematics; Schätzung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Regression; Regressionsanalyse
Abstract	This study was performed to determine whether hierarchical logistic regression models could reduce the sample size requirements of ordinary (nonhierarchical) logistic regression models. Data from courses with varying class size were randomly partitioned into two halves per course. Grades of students in college algebra courses were obtained from 40 colleges. The largest sample size group, Group 4, contained 11 colleges with half counts ranging from 171 to 563 (average 307). Nonhierarchical and hierarchical analyses were performed on each half. Compared to their nonhierarchical counterparts, hierarchically estimated cutoff scores from different halves were closer together in value and predicted course outcomes in the other half more accurately. These differences were most pronounced with small samples. It is concluded that the sample size requirements could be substantially reduced if hierarchical logistic regression were used to estimate cutoff scores. (Contains 2 tables, 7 figures, and 28 references.) (SLD)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC