or if a lower score in one measure were compensated by higher performance in

Or if a lower score in one measure were compensated

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or if a lower score in one measure were compensated by higher performance in the other measure. Details of this particular standard are described below. Performance data from the fall, 1998, class were used to evaluate the potential impact of increasing admission standards at UHH. First semester performance at UHH (F98GPA) was positively and statistically significantly correlated with each of the three high school performance measures, HSGPA (r=0.50, p<0.001), SATV (r=0.29, p<0.001), and SATM (r=0.22, p<0.001). Scatter plots for these pairs of variables are shown in Figures 1-3. When run in a multiple regression analysis, an equation can be generated using high school performance variables to predict college GPA. This predicted grade point index (PGI) equation has the form: PGI= a(HSGPA) + b(SATV) + c(SATM) + d, where a, b, c, and d are constants. The multiple regression equation for these fall, 1998 data was PGI = 1.036(HSGPA) + 0.00126(SATV) + 0.00101(SATM) - 1.966. In this analysis, HSGPA was the most powerful predictor (p<0.001) followed, although not statistically significantly, by SATV (p<0.06), and SATM (p<0.16). The SAT scores lose apparent importance in the multiple regression analysis, because SAT scores are already highly correlated with HSGPA and with each other; thus they add little additional explanation of variation in UHH GPA. For example, Figure 4 shows a scatter plot of SATV vs SATM for which the correlation coefficient, r = 0.61, P<0.001. It is also possible that the first semester course load at UHH may not draw as much on mathematics skills as later semesters with the result that SATM has a diminished predictive power for early college GPA. These results are essentially the same as the results of Susan Brown's earlier analysis of student performance data from the class of fall, 1996. Once established from an earlier base of data, the predicted grade point index could potentially be used to evaluate and determine admission status of future UHH applicants. To examine the impacts of this possibility, Figure 5
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7 shows actual GPA performance of students plotted against predicted GPA index from the multiple regression on high school performance measures. Two points must be recognized at the onset of interpreting these data. 1) The data set is necessarily restricted to students who were admitted; there is no
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