Predictors of Postsecondary Success: An Analysis of First Year College Remediation Page: 68
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percentage (p = 0.01) and SAT percentage (p = 0.04), significance was set at p < .05. Therefore,
regression results regarding Research Question 1 suggested high school campus-level variables
of incoming freshmen at a Central Texas two-year college provided a low level of prediction of a
student's enrollment into remedial education.
Research Question 2
To what extent do high school campus-level variables of incoming freshmen at a Central
Texas two-year college predict a student's enrollment into remedial education?
With Research Question 2, the researcher sought to expand Research Question 1 by
exploring the level of predictive impact each of the selected campus-level variables had on
college-level remedial coursework SCHs. Multiple regression analysis was used to determine
the strength of relationship between first-year college student enrollment into remedial education
coursework and the selected campus-level predictor variables: at-risk, economically
disadvantaged, limited English proficient, advanced course/dual credit courses, math college-
ready, English college-ready, ACT average, and SAT average. The overall regression model
was statistically significant, (F(8,286) =12.74, p = 0.05), and yielded an r2 = 0.263, indicating the
overall model explained 26.3% of the variance in first-year college student taking remedial
Campus-Level Non-Limited English Proficient
Campus-level non-LEP percentage was included in the regression model as a continuous
predictor variable to be studied. Campus-level non-LEP percentage was found to have a weak
correlation (r = -0.272) to first-year student remedial enrollment, thereby indicating a high
school campus' LEP percentage had a weak relationship to college remedial coursework.
However, regression analysis showed campus-level non-LEP percentage to have no statistical
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Baker, Emmett Andrew. Predictors of Postsecondary Success: An Analysis of First Year College Remediation, dissertation, August 2017; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc1011868/m1/76/: accessed April 19, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; .