Identification of Multiple Nonreturner Profiles to Inform the Development of Targeted College Retention Interventions

Abstract

Throughout the college retention literature, there is a recurring theme that students leave college for a variety of reasons making retention a difficult phenomenon to model. In the current study, cluster analysis techniques were employed to investigate whether multiple empirically based profiles of nonreturning students existed to more fully understand the types of students with particular characteristics that are related to leaving college. Based on over 18,000 students who left their initial institution after the first year, analyses supported three clusters, which were labeled as Affordability Issues, Unexpected Underperformers, and Underprepared and Facing Hurdles. Follow-up analyses were then conducted to determine whether students from each cluster had different higher education trajectories. Students in the Underprepared and Facing Hurdles cluster were most likely to drop out of higher education completely or transfer to a 2-year institution. Those students in the Affordability Issues cluster were most likely to transfer to a less expensive 4-year institution. Finally, the Unexpected Underperformers behaved somewhere in between the other two clusters with regard to dropout and transfer behavior. The implications of these findings in terms of developing more thoughtful and targeted retention interventions for these different types of students are discussed.

A Model of Co-Curricular Support for Undergraduate Engineering Students

“…While previous studies have focused on the student perspective and have improved the understanding of student retention at the university level (e.g., Mattern, Marini, & Shaw, ; Slanger, Berg, Fisk, & Hanson, ), the engineering education community's understanding remains limited about the specific actions educators take to improve retention (Tinto, ). ESSCs offer examples of interventions that are offered to improve the retention of underrepresented students in engineering degree programs.…”

Explaining Gender Gaps in English Composition and College Algebra in College: The Mediating Role of Psychosocial Factors

“…Despite these seemingly inconsistent findings, a meta-analysis found that female students earned higher grades in English and mathematics courses (Voyer & Voyer, 2014); even though male students scored relatively higher in mathematics on standardized admission tests, like ACT and SAT (Bielinski & Davison, 2001; DeMars, 2000; Liu & Wilson, 2009). Hence, at least on the basis of scoring better on standardized admission tests (Combs et al, 2010; Mattern, Shaw, & Marini, 2013; Smithwick-Rodriguez, 2011), these findings suggest that male students begin college more academically ready to succeed in mathematics, whereas female students are better prepared to succeed in English courses. However, there is ample evidence that female students earn significantly better grades in first-year English Composition and College Algebra (e.g., Conger & Long, 2010; Ding et al, 2007; Lorah & Ndum, 2013; Voyer & Voyer, 2014).…”

How Final Is Leaving College While in Academic Jeopardy? Examining the Utility of Differentiating College Leavers by Academic Standing

“…Student departure from an institution due to academic dismissal tends to account for less than 25% of departures (Tinto, 1993), and there is very little retention research comparing characteristics and outcomes of students who have been academically dismissed from college to those leaving an institution voluntarily (Brost & Payne, 2011;Cherry & Coleman, 2010;Cogan, 2011;Ott, 1988). In other words, in the plethora of studies examining student predictors of attrition in college, the outcome of attrition is rarely parsed by voluntary and involuntary leavers (Cherry & Coleman, 2010;Mattern, Marini, & Shaw, 2015). This may be an artifact of student data availability in databases of college outcomes.…”

“…In other words, in the plethora of studies examining student predictors of attrition in college, the outcome of attrition is rarely parsed by voluntary and involuntary leavers (Cherry & Coleman, 2010;Mattern, Marini, & Shaw, 2015). This may be an artifact of student data availability in databases of college outcomes.…”