Addressing Gaps in Research on First-Year Success: Gauging the Influence of High School Environment, Part-Time Instructors, and Diversity on Preparation and Persistence of First-Year University Students
November 4, 2008
Although research on factors that promote or hinder academic success of college students abound, much of it focusing on learning gains and institutional retention of first-year students (St. John, 2006; Reason, Terenzini and Domingo, 2006; Seidman, 2005; Kuh et al. 2005; Pascarella and Terenzini, 2005; Braxton, 2000; Astin, 1993; Tinto, 1987), there is little empirical evidence on how characteristics of high schools influence preparation and success of students that go on to college. Similarly, there is a paucity of insight on how the growing use of part-time (adjunct) university instructors affects the learning and academic growth of students (Pascarella and Terenzini, 2005, pp. 110-119; AAUP, 2008; Jacoby, 2006). A third area of inquiry where findings to date remain inconclusive is how changes in the ethnic/racial composition of students relate to academic success and enrollment persistence. Over the past twenty years a substantial body of research has accumulated that suggests ethnic/racial diversity among college students yields significant educational benefits, including “steeper learning curves” and enhanced cognitive skills, and improved persistence (Brown, 2006, p. 334; Shaw 2005, p. 3-6; Milem, Chang, and Antonio 2005, pp. 6, 13, 18; ACE and AAUP 2000, pp. 4, 8; Chang, 1999). However, reflecting on three decades of studies on the connection between diversity and student learning, Pascarella and Terenzini (2005, p. 130) point out that “all the findings are based solely on student self-reports.” Moreover, the accumulated research fails to specifically measure educational benefits associated with ethnic/racial diversity in the classroom (Terenzini, Cabrera, Colbeck, Bjorklund, and Parente, 2001).
DATA SOURCES, STATISTICAL METHOD, AND LIMITATIONS
Yij = y00 + yp0Xpij + y0qZqj + ypqZqjXpij + u0j + eij for (1) and (2)
The logistic regression model to estimate the probability of persistence with interaction terms is expressed as:
Logn(pi / [1- pi]) = y0 + y1Xi + y2 Zj + y3 XZij + ei
where pi is the probability of a persistence; Xi is a vector of student characteristics and first-year academic and campus experiences; and Zj is a vector representing exposure to part-time instructors and classroom diversity, including share of classmates from ethnic/racial groups and enrollment in a diversity course; and XZij is the interaction term that estimates the slope y3 as the effect of student background and first-year experience on persistence to be a linear function of the exposure to part-time instructors and classroom diversity (Jaccard, 2001). Variables other than those measuring exposure to part-time instructors and classroom diversity are entered as moderators to test their level of significance.
Tables and Figures
 A translation of the tabulated standardized coefficients in Greenwald, Hedges, and Laine (1996) shows that a 10% rise in per-pupil-expenditure may lead to a 1.7% increase in student achievement, using post-1970 studies that are deemed more appropriate by the authors; similarly, an 18% rise in teacher salary may effect a mere 0.15% rise in achievement, using post-1980 studies. (calculations by the author)
 Results listed in Table 4 from Elliott’s 1998 study show that an approximately 30% increase in the per-pupil core expenditure (including teachers’ salaries), which equates to $1000, may yield a 0.38% rise in math achievement. Only modestly significant (alpha p = 0.26), a 10% rise in math achievement would be associated with a 794% increase in per-pupil funding—well beyond a conceivable change in expenditures! (calculations by the author)
 Heavy TV viewing (over 3 hours per day) in adolescents correlated strongly with a decline in reading ability (Reinking and Wu, 1990), while the average amount of TV viewing during childhood and adolescence was associated with school dropout and failure to complete a university education (Hancox, Milne, and Poulton, 2005). Gentzkow and Shapiro (2006) found little negative impact on educational achievement due to TV viewing. But, in contrast to aforementioned studies, they based their analysis on students growing up during the 1950s and ‘60s, when programming content was on average decidedly more educational than today. The harmful effect of TV on cognitive development has been argued by a noted German neurologist in Plüss and Scheytt (2006). Lillydahl (1990) and D’Amico (1984) showed that working more than part-time interferes with a student’s academic progress.
 On limitations of self-reported data, see Clayson and Sheffet, 2006; Gonyea, 2005; Feeley, 2002; Pike, 1999; Coren, 1998; Pohlmann and Beggs, 1974.
 The national average is adjusted up to account for the student-teacher ratio metric used in Rooney, Hussar, and Planty (2006), as their number includes teachers with non-instructional assignments.
 Calculation and interpretation of interaction terms follows Jaccard (2001, pp. 18-37).
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