Justificación del Proyecto

The research literature has long held that where one starts out in higher education has enormous consequences, particularly with reference to completing degrees (Velez 1985). The vast

majority (78.7 percent; s.e. = 1.15) of the universe under study here started out in a four-year college. Do any characteristics of that first institution stand out in multivariate analysis?

Only selectivity of the first institution was admitted to the Step 2 logistic model (the fact that the institution was a four-year college was not admitted). To be sure, there are other characteristics of the first institution of attendance. But if the dichotomous “selective” variable turns out to have but modest, if any, significance in the account, it is unlikely that other stock institutional characteristics—size, control, residential/commuter ratio—will have any influence, either. The best of the institutional characteristics variables in the literature is probably Stoecker, Pascarella and Wolfle’s (1988) “size.” This is not a simple measure, rather a factorial scale that includes total enrollment, student/faculty ratio, and public control. However attractive the concept, institutional size rarely breaks through as a stand-alone factor in literature with large national samples because of student taste—some like it large, some like it small—and taste is too variant. All this does not mean that where one starts out is irrelevant to completion, rather it directs our attention to other features of student academic history. More to the point, with 64.8 percent (s.e. = 1.06) of NELS:88/2000 students who attended a four-year college at some time attending more than one institution, and 26 percent (s.e. = 1.01) attending more than two, the task of ascribing influence to institutional characteristics is daunting (for a discussion of this issue, see pp. 81–84). In addition to the selectivity variable (SELECT), Step 2 includes NODELAY, a marker for direct entry to postsecondary education following high school graduation. It also includes a new

variable, ACCELCRD, made possible by the construction of the NELS:88/2000 postsecondary transcript file to reflect the practice of dual-enrollment that expanded during the period the High School Class of 1992 was attending high school and that has become much more visible since then. ACCELCRD sums all college credits earned by course work prior to high school graduation, along with credits earned by examination—including AP, the College Level Examination Program (CLEP), and institutional challenge exams. Most of these credits were earned either prior to matriculation or during the first term of enrollment, though some were earned at later points in the student’s undergraduate career. A previous brief analysis of this phenomenon (Adelman 2004a, pp. 55–56) suggested that acceleration might have a bearing on degree completion since the descriptive data indicated a positive relationship between the number of "acceleration" credits earned and both (a) high school academic curriculum intensity quintile and (b) selectivity of the first institution attended.

Table 13 sets forth the relationships of the variables in play at the postsecondary matriculation stage to bachelor’s degree completion for students who attended a four-year college at any time. Academic Resources is still in a commanding position, and the Delta-p statistic indicates that with each step up the quintiles of Academic Resources the probability of completion increases by 12.8 percent. Socioeconomic status quintile is still significant, though again, marginally so. Of all the new variables, no delay of entry alone is statistically significant, and its Delta-p says that students who enter college directly from high school increase the probability of bachelor’s degree attainment by 21.2 percent, a very persuasive marker.

Table 13. Logistic account of factors associated with earning a bachelor’s degree in the history of 1992 12th-graders who attended a four-year college at any time: Postsecondary entry phase

Adjusted Parameter standard

Variable estimate error t p Delta-p

Intercept -4.2124 0.6588 2.02 0.01

Academic Resources quintile 0.5541 0.0715 3.54 0.01 0.1283

Socioeconomic status quintile 0.2859 0.0643 2.03 0.10 0.0662

Education expectations 0.3462 0.2032 0.78 † †

No delay of entry 0.9161 0.2224 1.88 0.10 0.2121

Selectivity of first institution 0.4470 0.2301 0.89 † †

Acceleration credits 0.1904 0.1196 0.73 † †

Race -0.4709 0.2130 1.01 † †

Gender -0.4627 0.1540 1.37 † †

Parenthood -0.9639 0.4597 0.96 † †

a_{Variables did not meet threshold criterion for statistical significance}

NOTES: Statistically significant variables are highlighted in bold. Standard errors adjusted by root design effect = 2.19. G2 _{= 5060.17; df =4913; G}2_{/df = 1.030; X}2 _{(df) = 1101.0 (9); pseudo R}2 _{= 0.2127; percent concordant}

predicted probabilities = 78.5.

SOURCE: National Center for Education Statistics: NELS:88/2000 Postsecondary Transcript Files (NCES 2003- 402 and Supplement). The selectivity of the first institution of attendance, while yielding a positive parameter estimate, does not reach the threshold of significance, and the t value for acceleration credits, at 0.73, falls just below the threshold for retention in the overall statistical model. The case of acceleration credits is one for which the author hoped for a better outcome, but once a rule is set, it is observed: The variable is dropped from subsequent steps. The demographic variables are

30_{The “true” first institution of attendance excludes (1) colleges and community colleges in which the}

student was enrolled prior to high school graduation; (2) institutions in which the student was enrolled during the summer immediately following high school graduation and prior to fall term postsecondary entry (unless the institution was the same in both periods); and (3) “false starts,” that is, cases in which the student enrolled, but then withdrew during the first term of attendance, only to enroll and complete course work in a different institution at a later point in time (in these cases, the second institution is the “true first institution”). The true first date of retained, but don’t tell us much. And education expectations barely stays under consideration with a t value of 0.78. Beattie’s human capital analysis (2002) downplayed education

expectations as a central feature of explaining outcomes, particularly in consideration of group differences. It turns out that, even with a more sophisticated variable (our “anticipations”) than the customary way of marking “aspirations,” Beattie is right.