Differential Prediction of Academic Success from Achievement Test Scores PDF Download
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Author: Dale Paul Scannell
Publisher:
ISBN:
Category : Prediction of scholastic success
Languages : en
Pages : 416
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Author: Dale Paul Scannell
Publisher:
ISBN:
Category : Prediction of scholastic success
Languages : en
Pages : 416
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Book Description
Author: David E. Lavin
Publisher: New York : Russell Sage Foundation
ISBN:
Category : Prediction of scholastic success
Languages : en
Pages : 192
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Book Description
Author: Johannes Schult
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Tests of cognitive ability play a major role in the selection of students. Still, data regarding the fairness of standardized tests in Germany is scarce. We use three samples (N)=)2,616; 58% women) from German universities to investigate the sex-specific differential prediction of college performance based on intelligence tests. The predictive bias we find is small and in line with US-American research. The direction of the effect depends on the cognitive ability domain investigated: Numeric test scores are prone to disadvantage women whereas verbal test scores are more likely to discriminate against men. Including high school grade point average in the prediction model can help to offset differential prediction that underestimates women's academic achievement.
Author: Clarence Hirum Bagley
Publisher:
ISBN:
Category : Prediction of scholastic success
Languages : en
Pages : 350
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Author: Betty Lee McLain
Publisher:
ISBN:
Category : Ability
Languages : en
Pages : 116
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Author: Rodney Ebon Anderson
Publisher:
ISBN:
Category :
Languages : en
Pages : 200
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Author: Chester Henry Ruedisili
Publisher:
ISBN:
Category :
Languages : en
Pages : 432
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Author: Anthony K. Kallingal
Publisher:
ISBN:
Category : Prediction of scholastic success
Languages : en
Pages : 322
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Author: Minji K. Lee
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Concerns over fairness permeates every aspect of the testing enterprise, and one characterization of fairness in testing defined by the Standards (AERA, APA, and NCME, 1999) is a fairness as lack of bias. One important way to study bias in college admission context concerns the degree to which prediction equations are equivalent for different groups. To the extent that the AP variables are used together with admission test scores and previous academic records to predict future academic achievement, it is important to know if members of one group are systematically predicted to obtain lower or higher grades than they actually achieve on the average (Linn, 1990, p. 309). Many studies have investigated differential predictive validity for different groups using high school performance and admission test scores as predictors (Linn, 1990). To this day, minimal research attention has been directed toward differential predictive validity using Advanced Placement (AP) variables as predictors, although policy makers have begun to treat the AP experience as an additional important prerequisite for success in college (Breland et al., 2002). By examining the differential predictive ability of AP variables and controlling for predictor unreliability, we can better understand the extent to which these predictors are biased against particular groups. With this understanding, test users can be informed of the extent to which the inferences drawn from these variables are supported by strong validity evidence regarding fairness in admission. Against this backdrop, the current study examines whether AP exam scores predict the first year GPA and second year retention differently for different groups of ethnicity, gender, parental education level, and language group, controlling for high-school-level variables using hierarchical linear modeling.
Author: James Najeeb Jacobs
Publisher:
ISBN:
Category : Ability
Languages : en
Pages : 420
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Book Description
