Literaturnachweis - Detailanzeige
| Autor/inn/en | Gross, Alan L.; und weitere |
|---|---|
| Titel | A Model for Investigating Predictive Validity at Highly Selective Institutions. |
| Quelle | (1993), (33 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Admission (School); Algorithms; Competitive Selection; Higher Education; Mathematical Models; Predictive Validity; Secondary Education; Selective Admission |
| Abstract | A statistical model for investigating predictive validity at highly selective institutions is described. When the selection ratio is small, one must typically deal with a data set containing relatively large amounts of missing data on both criterion and predictor variables. Standard statistical approaches are based on the strong assumption that the missing data are missing at random (MAR) (i.e., the missing data can be accounted for in terms of the observed measures), and there are no unmeasured variables that underlie the missing data process. The proposed model represents an attempt to account for any unmeasured selection variables by assuming that applicants are first placed into admission categories by the institution and then selected within each category in terms of the observed predictor variables. Thus, although the MAR assumption may not hold for the set of all applicants, it may very well hold within each admission category. The model uses the EM algorithm to obtain estimates of validity separately within each category. The model is quite general and can be used when there are missing data on the predictor and criterion variables, and even if the admission category is not known for each applicant. The proposed model is illustrated in terms of a real life data set for a selective secondary school with over 2,000 applicants. Four tables present analysis data. (Author/SLD) |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
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