Identification of Multiple Nonreturner Profiles to Inform the ...

Article

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

Journal of College Student Retention: Research, Theory & Practice 2015, Vol. 17(1) 18–43 ! The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1521025115571091 csr.sagepub.com

Krista D. Mattern1, Jessica P. Marini2, and Emily J. Shaw2

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.

1 2

ACT, Inc., Iowa City, IA, USA The College Board, New York, NY, USA

Corresponding Author: Krista D. Mattern, ACT, Inc., 500 ACT Drive, PO Box 168, Iowa City, IA, 52243, USA. Email: [email protected]

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Keywords retention, cluster analysis, transfer

The issue of retention in higher education has been studied extensively by researchers. There have been retention studies focused on the predictors of the outcome, the evaluation of various interventions to improve retention, focused research on special populations, and theoretical works building comprehensive models of college retention. In addition to the dominant model of student persistence cited in the literature (Tinto, 1993), other prominent models of persistence (e.g., Astin, 1984; Bean, 1990, 2005; Cabrera, Nora, & Castaneda, 1993) tended to focus on the interdependent nature of individual, institutional, and environmental characteristics that affect college retention. Of the many individual- or student-level variables at play, most commonly retention research focuses on the role of academic preparation (Adelman, 1999, 2006; Allen, 1999; Allen, Robbins, Casillas, & Oh, 2008; Astin, 1997; Attewell, Heil, & Reisel, 2011; Mattern & Patterson, 2009; Pascarella & Terenzini, 1998; Tinto, 1993), gender (Leppel, 2002; Peltier, Laden, & Matranga, 1999; Woodard, Love, & Komives, 2000), race or ethnicity (Allen, 1999; Astin, 1975; Keller, 2001; Murtaugh, Burns, & Schuster, 1999; Pascarella & Terenzini, 1998; Woodard et al., 2000), socioeconomic status (SES; Allen, 1999; Attewell et al., 2011; Bowen & Bok, 1998; Cabrera, Nora, & Castaneda, 1992; Howard, 2001; Hoyt & Winn, 2004), age (Keller, 2001; Murdock & Nazrul Hoque, 1999; Tinto, 1993), and motivation (Allen, 1999; Allen et al., 2008; Robbins, Lauver, Le, Davis, & Langley, 2004). Of the many institutional variables of interest, research tends to highlight the role of the size and selectivity of an institution, whether the institution is publicly or privately controlled, and whether it is a 2-year versus a 4-year institution (Astin, Tsui, & Avalos, 1996; Attewell et al., 2011; Mattern & Patterson, 2009; Tinto, 1993). With regard to influential environmental variables, retention studies often focus on peer and faculty interactions affecting social integration and the student’s sense of belonging to an institution (Hausmann, Ward Schofield, & Woods, 2007; Hurtado & Carter, 1997; Tinto, 1993), characteristics of the community or neighborhood in which the institution is located (Peltier et al., 1999; Tinto, 1993), or the distance of the campus to the student’s home (Mattern, Wyatt, & Shaw, 2013; Mooney, Sherman, & Lo Presto, 1991). With so many student-level, institution-specific, and environmental variables influencing retention in unique and complex ways, it becomes difficult for colleges and universities to synthesize all research findings on the factors related to retention and use it to inform interventions and outreach strategies on campus. Compounding the complexity of identifying at-risk students is that the reason that one student drops out of an institution (e.g., financial issues) may be completely different than the reason that another student does (e.g., academic

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performance). Therefore, due to the heterogeneity in causal factors across students for predicting retention, traditional linear regression techniques may not adequately predict who will drop out of an institution. Even in his early work on college retention, Tinto (1975) stressed the use of appropriate methodology that would measure different reasons for departure such as voluntary or involuntary. As a proposed solution to this problem, the current study will explore the use of cluster analysis techniques to provide structure and meaning to these variables by empirically grouping nonreturning students based on relevant individual, institutional, and environment variables (Aldenderfer & Blashfield, 1984) and then describing these clusters based on the clusters’ associated characteristics.

Factors Related to College Retention Using the attitude-theory framework (Fishbein & Ajzen, 1975) in which behavior can be explained by the intention to perform that behavior with past behavior, attitudes, and norms influencing intentions, Bean (2005) identified nine themes of student retention: intentions, institutional fit and commitment, psychological and attitudinal factors, academic performance, social factors, bureaucratic factors, external environment, student’s background, and financial factors. Based on a review of the retention literature, research findings as they relate to these themes are described in the following sections and help inform the selection of variables to include in the current study.

Intention to Leave Based on the results of numerous retention studies that he and his colleagues conducted, Bean (2005) concluded that the best predictor of whether a student would depart is his or her intent to leave the institution. Because most students drop out between the first and second year of college, it is best to collect this information from students during the second semester of their first year. This information could help an institution target its retention efforts toward students most at risk for leaving. Bean acknowledged that a student’s intention to leave does little to explain why students leave. Specifically, other variables such as attitudes, academics, and finances influence student intentions (which then impact retention) and can better explain why the student is departing.

Attitudes Institutional fit—whether a student fits in with others at a college—and institutional commitment—commitment to a specific institution—are viewed as two of the most important attitudes students hold in determining whether they will have intentions to leave or stay (Bean, 2005; Tinto, 1975, 1993). The difficulty of predicting whether a student will be committed to or fit in at a specific institution

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is related to differing antecedents across individuals. For example, a lack of demographic diversity could cause underrepresented minority students to feel as though they do not fit in to a college or university environment. Based on this reasoning, it is not surprising that research has shown that retention is related to demographic characteristics of race or ethnicity (Allen, 1999; Astin, 1975; Keller, 2001; Murtaugh et al., 1999; Pascarella & Terenzini, 1998; Woodard et al., 2000), SES (Allen, 1999; Attewell et al., 2011; Bowen & Bok, 1998; Cabrera et al., 1992; Howard, 2001; Hoyt & Winn, 2004), and gender (Leppel, 2002; Peltier et al., 1999; Woodard et al., 2000). Other attitudes such as self-efficacy, satisfaction, the value one places on education, and level of stress associated with being a student at a particular institution are also considered influential factors in the retention literature (Bean, 2005; Robbins et al., 2004; Zajacova, Lynch, & Espenshade, 2005).

Academic Performance Research has consistently shown a link between academic preparation and college retention (Adelman, 1999, 2006; Allen, 1999; Allen et al., 2008; Astin, 1997; Attewell et al., 2011; Burton & Ramist, 2001; Mattern & Patterson, 2009; Pascarella & Terenzini, 2005; Tinto, 1993). Specifically, there is a positive association between high school rank or high school grade point average (HSGPA), SAT scores, and grades earned in the first year of college with college retention (Allen, 1999; Astin et al., 1996; Burton & Ramist, 2001; Mattern & Patterson, 2009, 2012; Murtaugh et al., 1999). Other academic indicators have also been shown to correlate positively with college retention such as performance on advanced placement (AP) exams and the academic rigor of students’ high school coursework (Adelman, 1999, 2006; Mattern, Shaw, & Xiong, 2009). Additionally, research has shown that students who perform lower or higher in college than what would be expected based on their preentry academic preparation are more likely to leave an institution (Shaw & Mattern, 2012). Academic performance is likely to have not only direct but also indirect effects on college retention. Specifically, students who enter college more academically prepared are more likely to earn higher grades in college and thus face less risk of academic probation or failing out of college. In terms of direct effects, students who perform academically well are likely to have more positive attitudes toward the institution and are, therefore, more likely to feel like they belong and face a lower risk of departure. In particular, academic integration, which develops through relationships with faculty and staff and relates to a student’s academic involvement within an institution, is the psychological mechanism through which academic preparation is believed to influence retention by leading to positive academic experiences at the institution that reinforce the student’s intention to stay (Bean, 2005; Tinto, 1975, 1993).

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Social Factors As a complement to academic integration, the extent to which a student integrates socially is also considered an influential factor impacting a student’s decision to leave an institution or stay (Bean, 2005; Braxton, Hirschy, & McClendon, 2004; Tinto, 1975). Specifically, studies often focused on peer and faculty interactions affecting social integration and the student’s sense of belonging to an institution (Hausmann et al., 2007; Hurtado & Carter, 1997; Tinto, 1993). Having a parent who went to college is thought to influence college retention by means of social integration in that students have more “social capital” via their parents’ exposure and knowledge of college to navigate the college environment adeptly. Social support and close friendships also influence a student’s social integration. Not fitting in terms of academics, finances, and demographics might also limit the extent to which a student feels integrated socially at an institution (Bean, 2005).

Bureaucratic Factors Bean (2005) identified the institution’s ability to effectively manage large groups of students in terms of the associated paperwork, rules, and requirements for attending and remaining enrolled at an institution as influencing students’ perceptions of the institution. These perceptions, in turn, affect students’ intent to stay or depart from that institution. In a distinct but related research area, studies have examined characteristics of an institution that may be related to these bureaucratic factors and their relationship with college retention. In particular, the role of the size and selectivity of an institution, whether the institution is publicly or privately controlled, and whether it is a 2-year or a 4-year institution (Astin et al., 1996; Attewell et al., 2011; Mattern & Patterson, 2009; Tinto, 1993), as well as community or neighborhood characteristics where the institution is situated (Peltier et al., 1999; Tinto, 1993) have been shown to be related to student retention.

External Environment Bean (2005) refers to the external environment as influencing factors outside of the control of both the individual student and the institution, such as family responsibilities or health issues that may require a student to drop out of college. Even if such life events could be identified prior to the students’ departure, there may be little an institution can do to keep these students from leaving. That is, these students may be socially and academically integrated into the institution but still have to leave due to factors unrelated to the institution (e.g., going home to take care of a sick parent), but the utility of targeted interventions for such students appears limited.

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Student’s Background Bean (2005) described the student’s background as the human and social capital a student brings to college, including his or her academic preparation and SES. Research finding a positive link between retention and academic preparation and SES has already been reviewed in previous sections. Research has also shown that retention rates are maximized when highly capable students attend high-quality institutions (Bowen & Bok, 1998; Bowen, Chingos, & McPherson, 2009; Mattern, Shaw, & Kobrin, 2011). In other words, of students with the same ability, those attending more selective institutions are more likely to be retained than students attending less selective institutions. Additionally, within an institution, higher ability students are more likely to return than lower ability students.

Financial Factors Finally, money and finances play a large role in student retention (Bean, 2005; Bowen et al., 2009; Cabrera et al., 1992). Bean highlighted many difficulties associated with studying the effects of finances on college retention. For example, students from higher SES families also tend to have higher academic preparation; therefore, the driving factor in a student’s departure or whether there is a compounded effect may be unclear. Additionally, research on the financialretention link may be limited due to nonreporting, misreporting, and privacy issues surrounding financial data. Research generally demonstrated a positive relationship between financial factors and college retention with increases in SES and financial aid resulting in higher retention rates. In sum, student retention is a complex phenomenon with multiple psychological mechanisms and constructs influencing the departure process. With that in mind, the goal of the current study is to provide clarity and simplicity to the study of retention by employing cluster analysis techniques. That is, rather than examining the relationship between these influential factors and college retention, our intention is to identify clusters of students who do not return to college. By identifying students who share a similar profile, the goal is to identify types of nonreturning students to study and then to develop targeted interventions for assistance for the specific clusters. The benefits of using cluster analysis to study college retention are expounded on in the following section.

Cluster Analysis Cluster analysis is a method designed to organize objects into different groups, or clusters, where those objects within a cluster are more similar to one another than they are to objects in different clusters (Aldenderfer & Blashfield, 1984). By using theoretically meaningful measures, the “distance” between each object (or student, in this study) is calculated and then the students closest together are grouped together in clusters. Essentially, cluster analysis is a type of exploratory

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data mining procedure which aims to find patterns and groupings in data that might not be available by classical techniques. There are two broad approaches to cluster analysis—hierarchical methods and partitioning methods. Hierarchical methods build the clusters step by step either by starting with many individual clusters (the original objects) and combining them together, known as agglomerative clustering or by starting with one large group and breaking it into smaller groups, known as divisive clustering. This is done until an optimal balance is found between the similarities within clusters and dissimilarities between clusters. Partitioning methods start with the number of clusters and then the objects are divided until an optimal organization in the set number of clusters is reached. The capacity for cluster analysis to place observations that differ on a multitude of dimensions into similar and unique groups makes it ideal for analyzing a phenomenon such as college retention. As described earlier, the multitude of student-level, institution-specific, and environmental variables that can affect and influence an individual’s decision to return for the second year of college can be very difficult for a practitioner to synthesize in a meaningful way. That, coupled with the fact that each student could have a unique reason for not returning for the second year of college, makes understanding the reasons students leave college a difficult task. A data mining procedure, like cluster analysis, will place students into groups based on the unobservable patterns found in their unique college-going situations, based on observed measures.

Current Study The current study utilized national archival databases to examine many of the student, institution, and environmental factors shown to be related to college persistence among students who did not return after the first year of college. The databases used in the current study provide many opportunities such as large sample size and the inclusion of various institutions across the United States, but it also limited the variables available. For example, direct measures of student attitudes such as institution fit or self-efficacy were not available; however, reasonable proxies included a self-measure of writing ability (as a measure of self-efficacy), desiring help in developing study skills (as a measure of help seeking behavior and internal locus of control), and degree goal aspirations and the selection of an intended major (as a proxy of the value one places on education or the association between one’s education and ultimate employment). Likewise, direct measures of social integration were not available; however, information on whether a student planned to live off campus and whether they planned to work, both of which could limit their interactions with peers and faculty and thus potentially reduce their social integration, was available. Furthermore, the distance between a student’s home and their attending institution was calculated, which has been shown to be negatively related to

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college retention, potentially due to a lack of social integration into the foreign environment or due to impeding feelings of homesickness (Mattern et al., 2013; Mooney et al., 1991). In sum, every database has both benefits and limitations (Adelman, 2006). Information on the sample and the measures used in the current study is provided below.

Methods Sample The sample examined in the study is from one cohort of first-time, first-year students who entered college in the fall of 2009. These data were collected for the purposes of conducting national college readiness, success, and validity research on the SAT and other College Board tests across 4-year institutions (Patterson & Mattern, 2012). The original sample consisted of 262,949 students from 131 colleges and universities. The institutions were diverse with regard to control (public or private), selectivity, size, and location. Of this sample, 14.6% or 38,364 students did not return for their second year of college, which is the focus of the current study. Students with missing values for any of the measures used in the clustering procedure were excluded from the final cluster sample, resulting in a final sample size of 18,678 students. The breakdown of missing data by measures in the cluster procedure is as follows: race or ethnicity (missing for 11,574), parental education level (missing for 12,970), degree goal (missing for 13,376), help desired in developing study skills (missing for 10,796), part-time job in college (missing for 13,322), freshman housing plans (missing for 13,298), self-measure of writing ability (missing for 15,450), Academic Rigor Index (ARI; missing for 4,943), SAT scores (missing for 10,982), first-year grade point average (missing for 1,615), HSGPA (missing for11,814), distance to school (missing for 3,999), tuition (missing for 49), and under or over performance (missing for 12,965). The majority of missing data was due to students who had not taken the SAT and therefore had neither SAT scores nor responses to the SAT Questionnaire (SAT-Q) items.

Measures Demographic characteristics Gender. Self-reported gender was obtained from the SAT-Q that students completed during registration for the SAT. The sample of students was 53.4% female. Race or ethnicity. Students indicated their race or ethnicity on the SAT-Q. The categories include (a) Native American or Alaska Native, (b) Asian, Asian American, or Pacific Islander, (c) Black or African American, (d) Mexican or

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Mexican American, (e) Puerto Rican, (f) other Hispanic, Latino, or Latin American, (g) white, and (h) other. In this study, categories 4, 5, and 6 were combined into a single category titled Hispanic. Less than 1% of the sample were Native American or Alaska Native, 7.1% were Asian, 11.1% were Black or African American, 11.3% were Hispanic, 67.2% were white, and 2.6% had an unknown ethnicity. Highest parental education. Parental education was also derived from selfreported data obtained from responses on the SAT-Q. Student responses were provided for both mother’s and father’s highest educational level. The highest degree of either parent was used to create this variable. The response options for this item included: “grade school,” “some high school,” “high school diploma or equivalent,” “business or trade school,” “some college,” “associate or 2-year degree,” “bachelor’s or 4-year degree,” “some graduate or professional school,” and “graduate or professional degree.” This variable was collapsed into three groups: (a) less than a bachelor’s degree, (b) bachelor’s degree, and (c) higher than a bachelor’s degree; 43.7% of our sample had parental education levels less than a bachelor’s degree, 31.9% had a bachelor’s degree, and 24.4% had higher than a bachelor’s degree. Preenrollment academic predictors High school GPA. Self-reported HSGPA was obtained from the SAT-Q and is constructed on a 12-point interval scale, ranging from 0.00 (F) to 4.33 point (A+) (M ¼ 3.41; SD ¼ 0.54). SAT scores. Official SAT scores were obtained from the College Board’s College-Bound Seniors database. A student’s most recent SAT mathematical (M ¼ 534; SD ¼ 93), critical reading (M ¼ 523; SD ¼ 92), and writing (M ¼ 511; SD ¼ 91) scores—each on a score scale range of 200 to 800—were used in the current analyses. Advanced placement exam count. Obtained from official College Board records, AP exam count is the number of AP exams that a student completed (M ¼ 1.6; SD ¼ 2.1). Academic rigor. Academic rigor was calculated from student responses to the SAT-Q, which records information on English, mathematics, science, social science or history, and foreign and classical language courses completed during high school. In addition, students indicated the academic level of each course completed, such as honors, dual enrolment, and AP. Within each subject area, students are awarded between 0 and 5 points depending on the rigor of the student’s course work. Each of the scores from these five subscales is summed yielding a total score on a 0 to 25 scale (M ¼ 11.5; SD ¼ 5.1). For more

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information on the development of the Academic Rigor Index and the complete algorithm, refer to Wyatt, Wiley, Camara, and Proestler (2011). Academic self-beliefs Degree goal. One item on the SAT-Q asks students to indicate their educational degree aspiration (“What is the highest level of education you plan to complete beyond high school?”) with the following response options: “specialized training or certificate program,” ”2-year associate of arts or sciences degree,” “bachelor’s degree, “master’s degree,” “doctoral or related degree,” “other,” and “undecided.” This variable was collapsed into four groups: (a) less than a bachelor’s degree, (b) bachelor’s degree, (c) higher than a bachelor’s degree, and (d) undecided. Students who indicated “other” were excluded from analyses. The sample contained 1% that aspired to less than a bachelor’s degree, 26.9% indicating a bachelor’s degree, 55.6% indicating higher than a bachelor’s degree, and 16.4% undecided. Help desired in developing study skills. Another item on the SAT-Q asked whether students may want help in college to improve their study skills, in addition to other areas. The items were scored dichotomously with a 1 indicating that they did want help and a 0 indicating that they did not want help. Approximately 35% of the sample indicated that they desired help in developing their study skills. Self-measure of writing ability. On the SAT-Q, students are asked to rate themselves in terms of perceived writing ability relative to other people their age (“How do you think you compare with other people your own age in writing ability?”) with the following response options: (A) “among the highest 10 percent in this area of ability,” (B) “above average in this area,” (C) “average in this area,” and (D) “below average in this area.” The majority of the sample fell into the above average (44.3%) and average (33.4%) categories, with only 1.5% indicating they felt they were below average. Intended major. On the SAT-Q, students could indicate their intended major. For those who did not know what they wanted to major in, they could select undecided. A dichotomous variable distinguishing between those with an intended major and those that were undecided was created. Only 3.0% of the sample indicated they were undecided. Postenrollment academic predictors First-year GPA. Each participating institution supplied FYGPA (first-year grade point average) values for their 2009 first-time, first-year students (M ¼ 2.20; SD ¼ 1.10).

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Over- or under-performance (residual). The extent to which students over- or underperformed in their first-year of college was determined based on the difference between a student’s predicted FYGPA (based on running a regression within each institution with HSGPA and SAT scores included as predictors) and observed FYGPA (M ¼ 0.55; SD ¼ 0.99). Postenrollment interfering factors Part-time job in college. On the SAT-Q, students indicated whether or not they would be working part-time in college. Response options included “yes,” “no,” or “don’t know.” and 68.6% indicated they would be working part-time, while 25.0% were undecided. Freshman housing plans. On the SAT-Q, students indicated their freshman housing plans. Response options included “at home,” “on campus,” “off campus,” and “don’t know.” The majority of our sample indicated they would be living on-campus (66.4%), 6.2% indicated at home, 3.8% off-campus, and 23.6% were undecided. Institutional characteristics Size. Institution size was determined by the reported number of full-time undergraduate students, obtained from institutional responses to the Annual Survey of Colleges (ASC), a yearly survey of colleges, universities, and vocational or technical schools by the College Board with the objective of obtaining information that is important for potential students or applicants. Institutions were categorized into four sizes: (a) small: 750 to 1,999 undergraduates, (b) medium to large: 2,000 to 7,499 undergraduates, (c) large: 7,500 to 14,999 undergraduates, and (d) very large: 15,000 or more undergraduates; 52.5% of our sample attended very large sized institutions, 26.4% attended large institutions, 16.9% medium to large, and the remaining 4.2% small institutions. Selectivity. Based on ASC data, institutional selectivity was measured as the ratio of admitted students to applied students. Institutions were categorized into three levels of selectivity: (a) admittance rate under 50%, (b) admittance rate between 50 and 75%, and (c) admittance rate of above 75%. The majority of the students in our sample (69.0%) attended schools with an admittance rate between 50 and 75%, followed by 21.5% attending schools with an admittance rate of above 75%, and the remaining 9.4% attending the most selective schools. Control. Whether an institution was private or public was determined by ASC data. The majority of our sample (78.8%) attended a public institution.

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Metropolitan status. The institution’s location (i.e., rural, suburban, and urban) was based on ASC data. Most of our sample attended a suburban (46.3%) or urban (36.2%) institution. Tuition. The cost of a year of tuition was obtained from ASC data (M ¼ $11,830; SD ¼ $9,918). Distance from home. The distance between a student’s home address and college address was calculated in SAS based on an algorithm that measured the number of miles between the center points of the two zip code areas (Hadden & Zdeb, 2006; M ¼ 238; SD ¼ 484).

Analysis This study used hierarchical cluster analysis implemented using the two-step procedure in SPSS. The two-step procedure is an agglomerative procedure which goes through two passes of the data to form the clusters. In the first step, individual observations are tentatively grouped together by similarity. Then these groups are hierarchically clustered using a distance measure determined by the type of variables in the dataset. The variables in this study’s data set are both continuous and categorical and the two-step procedure is able to handle variables of mixed types. Since the variables are of a mixed type, the twostep procedure uses a likelihood distance to determine the distance between observations in the space and form the clusters. The two-step procedure also standardizes continuous variables, which is a necessary step to ensure that clusters are not being formed based on the scale of one or two variables, but using all of the information available. Also, the number of different groups existing within nonreturning students is unknown. The two-step procedure does not require the user to input an initial number of clusters. It will determine the optimal number of clusters from the relationship between the variables alone, which lends itself well to this particular research question. Finally, this procedure is built to handle large data sets, which this qualifies as, and this is an important point to take into consideration since many clustering procedures that work for small data sets do not extend to large scale data.

Results Cluster Analysis Based on the 18,678 nonreturning students, the two-step procedure evaluated possible numbers of clusters ranging from 1 to 15 and determined based on the maximum ratio of distance between the clusters that three distinct profiles emerged from the cluster analysis. This ratio is calculated by taking the ratio

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of the minimum intercluster distance from the current model and the next largest model as well as the ratio between the current model and the next smallest model and finally selecting the larger of the two ratios as the final measurement. A large distance ratio means that the clusters are well separated and that the members of the cluster are alike each other yet sufficiently different from the other clusters. As expected, there were certain characteristics of each cluster formed, and the clusters were named based on these characteristics: Affordability Issues (N ¼ 3,958), Unexpected Underperformers (N ¼ 6,229), and Underprepared and Facing Hurdles (N ¼ 8,491). Table 1 provides descriptive information for the three clusters in terms of the continuous variables included in the analysis, whereas Table 2 provides a summary of the categorical predictors. The characteristic that set the Affordability Issues cluster apart from the other clusters was the tuition of the initial institution. As seen in Table 1, this cluster had, on average, the highest cost of tuition of all three clusters (M ¼ $29,741; SD ¼ $5,269). The cost was over four times more than for each of the other two clusters. The Affordability Issues students were more academically prepared than the Underprepared and Facing Hurdles students but less prepared than the Unexpected Underperformers students; however, they had the highest FYGPA despite still performing lower than what their prematriculation credentials would predict (underperformance ¼ 0.40). They attended institutions fairly far away from their home (M ¼ 331). They were also the most likely to attend institutions that were most selective (22.7%), small (19.7%) and medium to large (47.4%), and private (100%) as compared to the other two clusters (refer to Table 2). Students in the Unexpected Underperformers cluster had the highest performance on the prematriculation academic measures but were not the top performers in college. As shown in Table 1, the students in this cluster had the most rigorous high school course load as shown by the ARI (M ¼ 14.5, SD ¼ 5.6), highest SAT critical reading (M ¼ 580, SD ¼ 74), SAT mathematics ( M ¼ 591, SD ¼ 76), and SAT writing (M ¼ 567, SD ¼ 74) scores, highest HSGPA (M ¼ 3.63, SD ¼ 0.45), and the highest AP exam count (M ¼ 2.7, SD ¼ 2.5) of the three clusters. As displayed in Table 2, 84.2% rated their writing ability as above average or higher, with 36% indicating that they are in the top 10% of writing ability. Yet, they did not have the highest FYGPA of the three clusters (M ¼ 2.49, SD ¼ 1.09) and also under-performed to a larger extent than the Affordability Issues cluster (M ¼ 0.51). Additionally, there was a larger percentage of males than females in this cluster; however, the opposite was true for the other two clusters. This cluster also contained the highest percentage of white students (74.8%) and came from the most advantaged families in terms of parental education, with 37% having at least one parent with an advance degree. Similarly, these students also had the highest degree goal aspirations, with 66% indicating a desire to earn more than a bachelor’s degree. They were the most likely to attend institutions that were very

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M

11.5 523 534 511 3.41 1.6 238 $11,830 2.20 0.55

Measures

Academic Rigor Index SAT critical reading SAT mathematics SAT writing HSGPA Number of AP exams Distance from home (miles) Tuition FYGPA Under- or overperformance

Total sample

5.1 92 93 91 0.54 2.1 484 $9,918 1.10 0.99

SD 12.4 547 554 538 3.49 1.8 331 $29,724 2.53 0.40

M 5.2 93 95 94 0.53 2.3 582 $5,269 0.99 0.88

SD

Affordability Issues

14.5 580 591 567 3.63 2.7 336 $7,432 2.49 0.51

M 4.6 74 76 74 0.45 2.5 646 $2,495 1.09 1.03

SD

Unexpected Underperformers

9.0 470 484 457 3.21 0.6 123 $6,715 1.85 0.65

M

3.9 72 75 69 0.52 1.1 169 $3,020 1.06 1.01

SD

Underprepared and Facing Hurdles

Table 1. Means and Standard Deviations for the Continuous Variables for the Overall Sample and by Nonreturner Cluster.

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Size

Ultimate degree goal

Intended major

Highest parental education

Ethnicity

Gender

Measures

Female Male American Indian Asian Black Hispanic White Other > Bachelor’s degree Bachelor’s degree > Bachelor’s degree Undecided Decided > Bachelor’s degree Bachelor’s degree > Bachelor’s degree Undecided Small Medium to large Large Very large

53.4 46.6 0.7 7.1 11.1 11.3 67.2 2.6 43.7 31.9 24.4 3.0 97.0 1.0 26.9 55.6 16.4 4.2 16.9 26.4 52.5

Total sample 55.0 45.0 0.6 7.4 8.5 11.0 70.0 2.6 36.5 33.0 30.4 3.2 96.8 0.6 22.3 59.1 17.9 19.7 47.4 27.0 5.9

Affordability Issues 47.8 52.2 0.6 10.0 2.8 8.9 74.8 2.8 26.2 36.8 37.0 3.6 96.4 0.4 16.2 66.0 17.4 0.0 2.1 13.1 84.8

Unexpected Underperformers

Table 2. Distribution (%) of Categorical Variables for the Entire Sample and by Nonreturner Cluster.

56.8 43.2 0.9 4.9 18.4 13.2 60.2 2.4 59.9 27.8 12.3 2.4 97.6 1.7 37.0 46.3 15.0 0.0 13.5 35.9 50.6 (continued)

Underprepared and Facing Hurdles

33

under 50% admitted 50% to 75% admitted Over 75% admitted Private Public Urban Suburban Rural No Yes Yes No Undecided At home On-campus Off-campus Don’t know Below average Average Above average Highest 10%

9.4 69.0 21.5 21.2 78.8 36.2 46.3 17.5 64.5 35.5 68.6 6.4 25.0 6.2 66.4 3.8 23.6 1.5 33.4 44.3 20.9

Total sample 22.7 61.8 15.5 100.0 0.0 41.8 53.8 4.4 68.2 31.8 62.4 8.3 29.3 4.3 72.5 2.1 21.1 1.1 28.7 46.0 24.3

Affordability Issues 11.0 70.9 18.1 0.0 100.0 47.0 34.5 18.5 71.5 28.5 62.1 7.9 30.0 2.7 70.9 2.7 23.7 0.6 15.2 48.2 36.0

Unexpected Underperformers 2.1 71.0 26.9 0.0 100.0 25.7 51.5 22.8 57.7 42.3 76.3 4.4 19.3 9.6 60.3 5.4 24.6 2.3 48.9 40.7 8.1

Underprepared and Facing Hurdles

very large: 15,000 or more

Note. Institution sizes were categorized by the number of undergraduates as follows: small: 750 to 1,999; medium: 2,000 to 7,499; large: 7,500 to 14,999; and

Self-measure of writing ability

Freshman housing plans

Part-time job

Study skills help

Metropolitan status

Control

Selectivity or admittance rate

Measures

Table 2. Continued

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Journal of College Student Retention: Research, Theory & Practice 17(1)

large (84.8%) and located in an urban setting (47.0%) as compared to the other two clusters. Underprepared and Facing Hurdles represented the largest cluster, (N ¼ 8,491) including nearly half of all nonreturners. Their profile reflects what many likely think of as the prototypical nonreturner. Specifically, this cluster was composed of students who were less prepared academically than their peers entering college. They had the lowest HSGPA (M ¼ 3.21, SD ¼ 0.52) and SAT critical reading (M ¼ 470, SD ¼ 72), SAT mathematics (M ¼ 484, SD ¼ 75), and SAT writing (M ¼ 457, SD ¼ 69) scores. They also took the least rigorous courses in high school as measured by ARI (M ¼ 9.0, SD ¼ 3.9) and took the fewest AP exams (M ¼ 0.6, SD ¼ 1.1). Most rated their writing skills as average (48.9%). This cluster included a larger percentage of underrepresented minorities with Black and Hispanic students comprising over 30% of the cluster. Additionally, the majority (59.9%) of students in this cluster came from a family where neither parent earned a bachelor’s degree. In sum, the students in this cluster arrived on campus with less academic and social capital (Bean, 2005). They were also the most likely to attend institutions that were large (35.9%) and rurally located (22.8%) as compared to the other two clusters. Finally, these students tended to stay closer to home than the other clusters (M ¼ 123, SD ¼ 169) and had the highest proportion of students saying they would work part-time while in college (76.3%) and not live on-campus (9.6% at home and 5.4% off-campus), which may have limited their social integration into campus life.

Follow-Up Analyses As a follow-up analysis to the cluster results, whether these clusters of nonreturning students followed hypothesized higher education trajectories was examined. Specifically, based on their cluster profile, it was expected that the Affordability Issues students would transfer to less expensive institutions, the Unexpected Underperformers would transfer to less selective institutions, and the Underprepared and Facing Hurdles students would drop out of higher education all together at a higher rate than the other two clusters. To test these hypotheses, data from the National Student Clearinghouse (NSC), which tracks student enrollment data for the majority of institutions of higher education, were used to identify either the institution to which the student transferred or if the student dropped out of higher education completely. If a transfer school was identified, the corresponding institutional characteristics of type (2-year, 4-year), size, selectivity, control, metropolitan status, tuition, and distance from home were examined. The majority of students (99.1%, N ¼ 18,059) from the cluster analysis could be accurately matched back to NSC records, which was used for the follow-up analysis. Data on institutional size was missing for a large percentage of students in each cluster (32.1–43.1%); therefore, no conclusions are drawn with regard to this institutional characteristic.

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Table 3 summarizes the educational trajectories for each cluster of nonreturners. It is of interest to note that the Underprepared and Facing Hurdles cluster had the highest rate of dropouts (35.2%) of all the clusters. Of students that transferred, this cluster also had the highest percentage of students transferring to a 2-year institution (64.3%). Additionally, these students were most likely to transfer to institutions that were least selective or open admissions (74%) and public (88.9%). These students also transferred to an institution that was closer to home (154 miles versus 331 miles). Students in the Affordability Issues cluster were the least likely to drop out of higher education completely (18.3%). Of the transfer students, 59.9% transferred to another 4-year institution. As expected, the largest decrease in the average tuition cost of the second institution as compared to the first institution occurred for this cluster—$10,740 as compared to $29,724—with the new tuition costs equaling roughly a third of the original. The average tuition cost for the other clusters also decreased but to a much smaller extent. Similarly, 72.5% of students in the Affordability Issues cluster transferred to a public institution, whereas all had originally attended a private institution. Despite being academically prepared for college, over a quarter of the students in the Unexpected Underperformers cluster dropped out of higher education completely. Of the students who did transfer, it was nearly an even split between 2-year and 4-year institutions. As was the case with the other clusters, students tended to transfer to less selective institutions; however, there was only a slight decrement in the associated tuition costs. Like the Affordability Issues cluster, the average distance from home of the institution was cut in half from over 300 miles to around 150 miles.

Discussion This study examined whether distinct clusters or profiles of nonreturners could be identified among students who did not return for their second year of college at their initial institution. Three distinct clusters of nonreturning students emerged from our analysis and were labeled as follows: Affordability Issues, Unexpected Underperformers, and Underprepared and Facing Hurdles. Not only did these students vary in terms of their precollege attributes, but they also performed differently in college as well as had different higher education trajectories. The current findings highlight what many already believe intuitively—that students leave college for various reasons. Therefore, a one-size-fits-all approach to developing an intervention does not appear to be the answer to an institution’s retention problem. Rather targeted interventions that focus specifically on the needs of the individual student are more likely to effectively increase overall retention rates.

36

Control

Selectivity

Size

Transfer students Type

2-year 4-year Very small Small Medium to large Large Very large Missing Under 50% 50 to 75% Over 75% Missing Private Public Proprietary

18.3 81.7 N ¼ 3,113 (%) 40.1 59.9 0.4 5.2 23.3 16.6 22.4 32.1 14.7 27.4 56.4 1.5 25.2 72.5 0.4

Departure decision

Drop out Transfer

N ¼ 3,810 (%)

Measures

Affordability Issues

Table 3. Higher Education Trajectories by Nonreturner Cluster.

26.0 74.0 N ¼ 4,440 (%) 46.2 53.8 0.2 2.7 13.3 17.5 29.1 37.2 11.5 26.0 59.5 3.0 12.5 84.7 0.4

N ¼ 6,002 (%)

Unexpected Underperformers

35.2 64.7 N ¼ 5,341 (%) 64.3 35.7 0.2 2.0 18.3 17.8 18.6 43.1 3.8 19.2 74.0 2.9 6.2 88.9 1.1

N ¼ 8,247 (%)

(continued)

Underprepared and Facing Hurdles

37

Missing Urban Suburban Rural Missing

N ¼ 6,002 (%)

Unexpected Underperformers

1.9 2.3 32.9 38.6 51.8 46.1 13.4 12.9 1.9 2.3 Mean (standard deviation) 154 (419) 166 (451) $10,740 ($11,721) $6,852 ($8,732)

N ¼ 3,810 (%)

Affordability Issues

93 (296) $4,417 ($5,067)

3.8 34.9 46.4 14.9 3.8

N ¼ 8,247 (%)

Underprepared and Facing Hurdles

accurately matched back to NSC records, which was used for the follow-up analysis. Institution sizes were categorized by the number of undergraduates as follows: very small: less than 750; small: 750 to 1,999; medium: 2,000 to 7,499; large: 7,500 to 14,999; and very large: 15,000 or more. 794 students had missing tuition data. 391 students had missing distance data.

Note. Not all students from the cluster analysis could be accurately matched to a second institution. Specifically, 99.1% of the sample (N ¼ 18,059 students) was

Distance from home Tuition

Metropolitan status

Measures

Table 3. Continued

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Journal of College Student Retention: Research, Theory & Practice 17(1)

Targeted Interventions Based on their academic performance in high school and college, students in the Affordability Issues cluster would not appear to be at risk for departure. They have above average test scores and HSGPA, took nearly two AP exams, and completed a rigorous high school course load. Their performance in the first year was not stellar, averaging roughly a B, but not at risk for academic probation. Students in the Affordability Issues cluster did perform lower than expected in college (underperformance ¼ .40), which could signal to those working in Student Affairs that something may be amiss. Additionally, these students and their families are burdened with extremely high tuition costs. Perhaps, these students would benefit from learning more about different sources of financial aid or scholarships that are available. Research has found that many students and parents are uninformed about the availability of financial aid (e.g., Horn, Chen, & Chapman, 2003; Perna, 2004, 2006). The problem is more pronounced for students from low SES backgrounds, who have less social capital to navigate the college admission process (Hossler, Schmitt, & Bouse, 1991). Given that over a third of students in the Affordability Issues cluster came from a family where neither parent earned a college degree, it seems reasonable that more information and access to various sources of financial aid may be useful for these students. In addition, the promotion of the availability of work–study opportunities on campus and resources and support related to balancing work commitments with academic commitments may be helpful for these students. Students from the Unexpected Underperformers cluster may also be a challenging group to identify as at-risk based on their level of academic preparation entering college. Similar to the Affordability Issues cluster, these students had above average test scores and high school grades. On average, they took 2.7 AP exams and had a high school rigor score of 14.5. In college, they also earned roughly a B but underperformed to an even larger extent than the Affordability Issue cluster (underperformance ¼ .51). As compared to the other clusters, students in this cluster were more likely to be white males from high SES families. That is, these students appear to have both the academic and social capital to persist, yet they do not. Their strong academic high school performance and affluent background coupled with their underperformance in college could serve as a signal to institutions that extra precautions may be needed for an individual student with this profile. A recent study by Shaw & Mattern (2012) found that there is promise in identifying students at risk for leaving an institution by storing their predicted FYGPAs in a database and comparing these values to their actual FYGPAs in college and potentially even first-semester GPAs. This routine examination of students’ residuals can help institutions to easily locate those students who are academically behaving much differently (larger residuals) in college than expected and appropriately reach out to them to offer support.

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The Unexpected Underperformers cluster conjures up images of students with a new sense of independence (average distance from home was 336 miles) who may be more concerned with the social aspects of college than the academic aspects. Perhaps these students would benefit from time management and study skill training, which have been shown to be positively linked to academic success (Crede´ & Kuncel, 2008; Robbins et al., 2004). Furthermore, institutions should follow up with these students to determine why they are underperforming, given their academic background. These students appear to be capable of succeeding, and institutions would benefit by intervening and hopefully remedying the situation before departure occurs. Finally, the Underprepared and Facing Hurdles cluster includes students that are the most easily identifiable as at-risk for leaving an institution given their lower academic performance when entering college and their lack of social capital; they are more likely to be from an underrepresented minority group and from a lower SES family. In terms of college grades, they also averaged less than a C for their first year. These students may be the easiest to identify and probably require the most resources in order to be successful. They would probably benefit most from individualized course placement guidance, including remedial course offerings, to remedy any academic needs. Placing students in courses that are of appropriate difficulty would help reduce poor performance and subsequent departure that may ensue from inappropriate course placement (Sawyer, 2007). Likely less equipped to do so on their own, these students may also benefit from extra tutoring and academic advising or counseling in order to navigate the complex college process successfully. In sum, the current study found that the same outcome—departure from a 4-year institution—occurred for students with three distinct profiles. Therefore, identifying students that are most likely not to return for their second year is not a straightforward endeavor. Research methodologies, such as cluster analysis, may better serve our understanding of the departure process and help institutions to develop meaningful interventions for the various student profiles. Future research should attempt to replicate these findings with other datasets, especially given the exploratory nature of cluster analysis, to determine the robustness of the current findings. Likewise, individual institutions should conduct their own analyses given the varied environmental, bureaucratic, and political factors influencing its campus environment, which may result in the identification of new, alternative institution-specific nonreturner profiles.

Study Limitations and Future Research Despite having access to data on a large, diverse set of students attending colleges across the United States, the reliance on archival data limits the variables that could be examined. For example, direct measures of students’ attitudes and integration once on campus, which play prominent roles in models of student

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Journal of College Student Retention: Research, Theory & Practice 17(1)

departure (Tinto, 1975, 1993), were not available. Likewise, the study included many characteristics of the attending institution; however, these served as indirect or proxy measures to a more meaningful construct that Bean (2005) outlined as bureaucratic factors. Additionally, the driving force behind a student’s decision to leave was unknown; rather, inferences were made based on the data. Though not an option in the current study, retention research would benefit from a study design that contacts students after their departure to inquire about the reasons why they left. Many institutions administer exit surveys to their college graduates. It might be a useful practice to also survey nongraduates, if an institution is not already doing so, which could serve as a rich source of information for determining the root of departure for various types of students (Porter, 2004). Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies Krista D. Mattern, Ph.D. is a Principal Research Scientist in the Statistical Research Department at ACT, Inc. Her research focuses on evaluating the validity and fairness of both cognitive and non-cognitive measures for predicting student success as well as higher education issues, in general, such as college choice, college major selection, retention and transfer behavior, and college completion. Jessica P. Marini, Ph.D., is an Assistant Research Scientist for the Validity Research & Services team at the College Board. Her research interests include causal inference, test validity, and students’ transition from high school to college. She is currently working on validity research related to the SATÕ and Advanced PlacementÕ Exams. Emily J. Shaw, Ph.D. is the Senior Director of Validity Research & Services at the College Board. Her research focuses on test validity as well as higher education issues related to college admission, preparation, and success. She also manages the development and maintenance of a national database of higher education outcomes for use in efficacy and validation research.