ERIC review: attrition research at community colleges.

The problem of student attrition in community colleges has become more important as calls for accountability in the use of public resources intensify. In addition, amid a fiscal environment of decreasing state funding, student retention has become a matter of economic survival for some colleges. Whether student attrition is viewed as an institutional effectiveness issue, a financial issue, or an enrollment management issue, it continues to be a challenge for community colleges. Many institutions' primary strategy for reducing attrition is the early identification of students likely to drop out and the development and implementation of intervention services for those students. Despite these efforts to reduce attrition, however, it largely remains an unsolved problem for community colleges. A more in-depth understanding of the process and those participating in it is necessary to develop initiatives that can further reduce student attrition.

This ERIC review will discuss the scope and significance of community college student attrition. It will begin with an examination of the theoretical models that explain and attempt to predict the phenomenon and review common variables associated with student attrition that have been widely studied by researchers. In addition, this review will summarize the research literature on student enrollment and registration behaviors as predictors of attrition. These types of studies that are now beginning to receive attention have the potential to add to our understanding of and ability to predict student attrition. Finally, this review will suggest areas that researchers should study in the future and discuss the opportunities for research that will yield benefits for practitioners in the field.

Throughout this review, the research studies cited refer to the general concept of student attrition using different terminology. Student persistence and student retention are terms utilized by some researchers in referring to a student's continued enrollment at an institution from one semester to another. Student attrition and student dropout are terms utilized by other researchers in referring to a student's failure to enroll from one semester to another. All four terms are referenced throughout this review essentially to refer to the same condition: whether or not a student enrolls in courses for the following semester.

Those who study or are employed in community colleges generally realize that many students have educational goals other than graduation (i.e., they enroll for a course or set of courses for personal interest or job training, or they are focused on earning a specific number of credit hours in order to transfer to a senior institution). The research studies examined in this review consider student dropout as occurring prior to an educational goal being achieved by the student.

Nearly all studies of community college student attrition typically target students who indicate that they are pursuing a degree or certificate that requires several semesters of coursework. Then, if the student fails to register for a consecutive semester prior to earning the degree or certificate, the student can be considered a dropout. Obviously, studying students with goals that do not require several semesters of coursework makes it difficult or impossible to determine who actually drops out. Moreover, students who fail to register at a community college for a subsequent semester cannot be automatically labeled as dropouts because they may have enrolled in another community college or senior institution. Additionally, failure to enroll can be due to nonacademic reasons such as entry into the workforce or unexpected medical, family, or other personal or nonacademic reasons. Therefore, for the purposes of this review, student attrition can be considered as unplanned academic-related or nonacademic-related events that occur prior to the student completing his or her educational objective.

Significance and Scope of Student Attrition

Community college student attrition receives a great deal of attention from researchers, practitioners, and policymakers in higher education. Some attribute this interest to a nationwide demand for institutional assessment and accountability (Cohen & Brawer, 1996). Others assert that the high costs associated with recruiting new students motivate administrators to identify ways to retain more of the students who already have enrolled (Brooks-Leonard, 1991). Still others contend that in difficult financial times more community colleges become enrollment- or tuition-driven enterprises to survive in the marketplace (Grossett, 1991). To be sure, the high financial costs associated with student attrition justify and demand the continued search for methods of reducing the rate of attrition of community college students. The high social and political costs of attrition demand deeper understanding of student attrition in order to develop strategies to reduce it.

Across several decades of research, community college attrition rates have been consistently reported as very high. Early research by Clark (1960) and Thornton (1966) found that more than 40% of community college freshmen either did not complete their educational objectives or did not return for their second year. Later research yielded similar results, finding that only 45% of community college first-time, full-time freshmen who intended to earn a degree or certificate graduated in the period from 1998 to 2001 and that 32% of students failed to return for their second year at a community college or enroll at another institution of higher education (SREB, 2003). Overall, researchers and practitioners continue to find that community college student dropout rates are significantly higher than those of senior institutions (Mohammadi, 1994).

Models of Student Attrition

Theoretical models of college student attrition have been developed by several researchers to identify and analyze the numerous variables that impact a student's decision to remain in college or drop out (Bean & Metzner, 1985; Spady, 1970, 1971; Tinto, 1975). Three of these attrition models are representative of those typically used in studies of community college students.

Attributed with one of the earliest models of student attrition, Spady (1970/1971) applied the work of Durkheim (1951) to his development of a college student attrition conceptual model. Essentially, Durkheim found that suicidal tendencies increased in people who were not integrated socially and normatively into their existing social system. Spady (1970) perceived a parallel process occurring in college students who dropped out, albeit not as drastic as suicide. Students who did not share values and orientations similar to other students, did not interact socially with other students, and generally did not feel compatible with the social system of college were more likely to drop out.

Spady's (1970) initial model of college student attrition proposed five independent variables, four of which (grade performance, intellectual development, normative congruence, and friendship support) actually influenced the fifth variable (social integration). These five variables were then linked indirectly to the dependent variable (dropout decision) through two intervening variables (satisfaction and institutional commitment). Spady (1971) then applied his proposed model for college student attrition in a longitudinal study of 683 first-year undergraduates at the University of Chicago in 1965. His purpose was to operationalize the variables of the model and analyze how separate components and interrelationships explained the attrition process. Spady's (1971) revised model, based on his findings in this study, retained the elements in his original model but added two important improvements. The first was the inclusion of a separate component comprised of structural relations and friendship support. The second improvement was a revision of the relationships among the components in the model.

Tinto (1975) completed the next major development of a student attrition model. Also connecting his model to the theories proposed by Durkheim (1951) and building on the work of Spady (1970), Tinto' s model for college student attrition continues to be the most widely recognized and tested model. Tinto believed that a student's tendency to stay in college was related to the degree to which the student felt integrated into the social and academic life of the college. Basically, Tinto contended that the cumulative interaction over time of categories of variables that included backgrounds, initial commitments to college study, and interactions with peers and faculty contributed to both social integration and academic integration. Several studies have confirmed that the construct of integration proposed by Tinto is predictive of student attrition (e.g., Pascarella & Chapman, 1983; Terenzini, Lorang, & Pascarella, 1981). Additional research has indicated that for two-year and four-year commuter students, academic integration has more indirect effect on attrition than does social integration (e.g., Pascarella & Chapman, 1983; Pascarella & Wolfe, 1985).

Bean and Metzner (1985) developed the next major model of student attrition. They contended that the theoretical models of student attrition developed by Spady, Tinto, and Pascarella relied heavily on socialization to explain attrition. Since the nontraditional student did not have the opportunity to become socially integrated into the institution, a different theory was needed to link the variables that could help explain the attrition process for this group of students. Bean and Metzner's conceptual model was developed with this in mind and was based on a model originally developed by Bean (1980) and then modified to its current format. This conceptual model, specifically developed for nontraditional students, recognizes the smaller role that social integration plays in attrition for those students. Bean and Metzner (1985) explain that the elements that comprise their model are the result of a thorough review of the literature on nontraditional students. The linkages between the elements in their model are derived from other models of traditional student attrition and behavioral theories. Bean and Metzner's model posits that a student's dropout decision is primarily based on four sets of variables: (a) academic performance as measured by grade point average; (b) intent to leave, which is influenced primarily by psychological outcomes and academic variables; (c) background and defining variables, primarily high school performance and educational goals; and (d) environmental variables, which are expected to have substantial direct effects on dropout decisions.

There are also two important compensatory interaction effects that are included in the model. The first is between the element, "Academic Variables" and the element, "Environmental Variables." Bean and Metzner consider the environmental variables important enough to cause a nontraditional student who even has low values for the academic variables to stay in college if the values for the environmental variables are in a positive direction. Conversely, in a situation where a student has very high values for academic variable but values for environmental variables in a negative direction, that student is likely to drop out. The second compensatory interaction is between the element, "Academic Outcomes" and the element, "Psychological Outcomes." Bean and Metzner consider the psychological outcomes to be important enough to cause a nontraditional student who even has poor academic outcomes to stay in college if the psychological outcomes are positive. Conversely, in a situation where a student has very positive academic outcomes but negative psychological outcomes, that student is likely to drop out.

Many of the studies examined in this review focus on a single variable or single sets of variables and the relationship with student attrition. While this univariate approach does provide valuable information on specific variables, it fails to identify the complex relationships that exist among many variables, and it does not identify which variables may be more important and which ones may not contribute uniquely to student attrition. Multivariate studies are necessary for understanding the complexity of student academic outcomes. Moving to a multivariate approach requires a theoretical model for anticipating and explaining the relationships among variables studied.

The theoretical models of college student attrition discussed above have been developed to identify and analyze the numerous variables that impact a student's decision to remain in college or drop out. Three of these attrition models are representative of those typically used in studies of community college students. Early models were developed with a focus on traditional students attending four-year residential colleges. More recent models have focused on nontraditional students typically found attending community colleges. Basically these models posit that the dropout decision is the result of the student's interaction with the institution academically and socially mediated by myriad "input" factors. Overall, these models provide a conceptual framework for anticipating and understanding the relationships, associations, and linkages between the variables investigated.

The following sections describe the variables that researchers have studied within various conceptual models.

Common Variables

A wide variety of variables have been studied as they relate to student attrition. For example, researchers have examined student characteristics (age, gender, ethnicity, socioeconomic status), other student variables (parental education level, employment status, marital status), academic ability (high school grade point average, class rank, admission test scores, first-semester college grades), noncognitive factors (motivation, social integration, intent to return, and career aspirations) and availability and use of student services. In addition, institution-related factors, curriculum, faculty-student interactions, and campus climate have been researched in the efforts to understand and predict student attrition. Collectively, researchers have determined that student attrition is a complex phenomenon that cannot be understood or predicted with single variables. However, a large amount of research supports the general observation that community college students are more likely to drop out if they have no specific educational goals, work full time, and attend college part time. Nevertheless, a more thorough understanding of multiple variables and their interactions is required for the formulation of strategies to reduce attrition. An overview of attrition research on several categories of variables follows.

Student Characteristics

Research conducted on the characteristics of community college students and how these characteristics relate to attrition is extensive. Some research has determined that age can be a predictor of attrition by itself and in combination with other variables (Greer, 1980; Hunter & Sheldon, 1980; Lanni, 1997). In fact, several studies have found that older students are more likely to drop out than are younger students (e.g., Brooks-Leonard, 1991; Gorter, 1978; Windham, 1995). However, exceptions to this finding were DeVecchio (1972) and Mohammadi (1994) who did not find any significant relationship between age and persistence.

Attrition studies that have examined student gender have found a relationship when gender is examined by itself but not when other factors are taken into account (Mohammadi, 1994). Other studies have indicated no significant relationship at all between gender and attrition (Aquino, 1990; Fischbach, 1990).

Ethnicity has been found to be related to student persistence in several studies. Mohammadi (1994) and Zhao (1999) found that Black students were more likely to drop out but other research has found no significant relationship between ethnicity and dropout (Aquino, 1990; Wall, Lessie & Brown, 1996). Finally, the study of student socioeconomic status and its relationship to attrition is unclear since several studies do not report consistent findings (Grosset, 1991; Rendon, 1995; Wetzel, 1977).

Other Student Variables

Studies by Lanni (1997), Windham (1995), and Swager, Sarah, Campbell, and Orlowski (1995) all found that students who worked full time were more likely to drop out of college when compared to those who worked part time or not at all. Also, Gerardi (1996) found that parents' educational background, along with other factors, was found to contribute to graduation. Studies at Northern Virginia Community College (2000a, 2000b) found that among the factors reported by students as reasons for dropping out, competing demands from their family was mentioned frequently. In addition, Bers and Smith (1991) suggested that home responsibilities of older community college students may affect persistence.

Academic Variables

Academic factors have long been studied as a predictor of student attrition. Recently, studies conducted at community colleges have been consistent with the results from senior institutions. Boughan (1998) and Lanni (1997) found that students unprepared for college coursework were more likely to drop out; Zhao (1999) found that low cumulative grade point average and academic standing in the community college were associated with attrition; and Hagedorn, Maxwell, and Hampton (2002) found that high school grades were predictors of college attrition. That is, the higher the students' grades in high school, the more likely they would persist to meet their educational goals in college.

Commitment Variables

Variables related to a student's commitment to the institution have been studied by several researchers. Boughan (1998) and Clagett (1998) found that students' intentions, desire to become a student, and academic commitment all were associated with their likelihood to persist. Similarly, Strauss and Volkwein (2001) found that a student's amount of reported institutional commitment was associated with a student's decision to reenroll at the same institution. Finally, Hagedorn, Maxwell, and Hampton (2002) reported that students with a positive viewpoint and high goals were likely to persist and not drop out of college. Each of these studies found that if a student was able to identify his or her enrollment goal more clearly, indicate a high level of commitment to that goal, and generally report a positive outlook on his or her educational experience, that student was less likely to drop out.

Student Services Variables

Several researchers looked at a variety of student services that are made available to students and sought to determine if the use of these services would have an impact on attrition. For example, an institutional study looked at matriculation services such as assessment, orientation, and counseling at the City College of San Francisco and found that students enrolled for more than nine credit hours who received these services persisted (Wolfe, 1998). However, Wolfe also found that if students who enrolled for fewer than nine credit hours received matriculation services, there was no impact on retention. Willett (2001) found similar results, reporting that students who were contacted by and visited with a counselor were more likely to persist than other students. Finally, Barr and Rasor (1999) found that freshmen who participated in a student service offered by American River College, such as athletics, student clubs, or tutoring, persisted longer and earned higher grade point averages and course completions than other freshmen who did not. These results suggest that greater student involvement on campus can lead to more positive academic outcomes for students.

Environmental Variables

A less often studied set of variables can be referred to as environmental factors. For example, Heverly (1999) found that student experiences with some key college administrative and instructional processes were associated with attrition. Tinto (1997) looked at learning communities and found that the enhanced classroom experiences influenced student persistence. These students worked more in groups, collaborated on projects and learning, and tended to develop improved communications with students and faculty as compared to more traditional lecture-passive listening educational experiences. Wortman and Napoli (1996) conducted a meta-analysis of six studies at community colleges and reported that academic and social integration significantly impacted persistence in a positive manner. Rendon (1995) reported that restructuring community colleges to become less Eurocentric and more racially accepting can reduce the attrition of many nontraditional students.

Less Studied Variables

A few studies have addressed student enrollment and registration behaviors either by focusing on college students as late registrants for the purpose of determining their success in relation to regular registrants or on class schedule changes (adds or drops) made by students. Belcher & Patterson (1990), Diekhoff (1992), Peterson (1986), and Sova (1986) studied late registrants, and Broadbent (1975) and Morris (1986) focused on class schedule changes. The studies of student registration behaviors provide some information on the variables used in the studies and comparable methods and analysis.

Late Registration Research

Research on late registration in American colleges and universities is extremely limited, and the published studies vary a great deal in terms of research quality. The small number of studies is somewhat surprising considering that most public community colleges today advertise themselves as being open door and have fairly liberal registration policies in the spirit of customer service. Nevertheless, the few studies that have been conducted and published typically investigated GPA, course completion, or college attrition by comparing late registrants with regular registrants. In addition, some of the studies also examined the characteristics of late registrants.

Stein (1984) investigated GPA and attrition rates of late registrants who were new students at Minneapolis Community College. This study defined late registrants as those students enrolling for classes from three days prior to the start of the academic quarter to eight days after the term had started. The study identified new students from those who were late registrants for the 1984 winter quarter. Data collected on these new students included their GPA, number of credit hours taken, and persistence to the next academic quarter (spring 1984) as compared to the total student body. Analysis of the data indicated that 25.1% of late registrants earned no grade points (withdrew from the class or received an F) as compared to 21.4% of the whole student population for the 1983 fall quarter. However, the percentage of late registrants that earned an A was 28% as compared to 17.6% of the whole student body for 1983 fall quarter. The study also found that of the new student late registrants, only 23.4% registered for the following quarter. A cross-sectional comparison to data collected on the whole student body in 1973, 1976 and 1979 indicated that persistence rate for the whole student body was approximately 62.5%.

Peterson (1986) also focused on GPA and attrition rates when she conducted a study of late registrants at Honolulu Community College. Her analysis of the students who registered late was that their completion rate was unusually high, with only eight students dropping out of school during the semester. The late registrants attempted a total of 214 courses and completed a total of 152. Peterson concluded that the late registrant is dropping one or two classes but is not usually dropping completely out of school. In terms of course grades, Peterson found that late registrants who took only three to nine credit hours had a much higher rate of successful completion than those who took more than 12 credit hours. Transcripts of the students indicated that those who enrolled for more than 12 credit hours almost always dropped at least one course or received an "N," "W," or "F." In addition, Peterson found that the rate of success rose when students enrolled in vocational courses as compared to liberal arts courses. All eight of the students who dropped out of school completely were enrolled in liberal arts courses.

Other studies on late registration at community colleges have been conducted by examining GPA and attrition in a particular course or set of courses. Sova (1986) studied students who enrolled in either (English) ENG 090 or ENG 110 at Broome Community College in the fall 1985 semester. Sova found that fewer late registrants passed the course than regular registrants and that the attrition rate for late registrants was much higher compared to regular registrants.

Another large-scale study published on community college students relating to late registration was conducted by Belcher and Patterson (1990) who examined students attending Miami-Dade Community College to determine the number and characteristics of students registering late. This study is the only one found in the literature that identifies some characteristics of the late registrants, although only a single semester of behavior is studied. The researchers found that late registrants were: (a) not pursuing a degree; (b) tended to be former students; and (c) were more likely to be part-time students, males, Black non-Hispanics, and older than recent high school graduates. This study, however, did not examine the relationship between late registration and any academic outcomes, including attrition.

The only other study found that looked at community college students, late registration, and attrition was conducted by Street, Smith, and Olivarez (2001). This smaller study examined 251 new and returning students to determine whether students who registered late were less likely to persist. The study found that for new students, 80% of those who registered during the regular period persisted to the next semester as compared to only 35% who registered late. For returning students, 64% of those who registered during the regular period persisted, as compared to 42% of late registrants.

Student Class Schedule Changes

Other studies have focused upon the registration behavior of students in terms of when adjustments are made to their schedule of classes, what kinds of students are most likely to make changes, and the reasons students add and drop courses. Only one study was found that looked at community college students, and it did not attempt to relate the student behavior to attrition (Broadbent, 1975).

Summers (2000), however, conducted a comprehensive study that provided new information on community college student attrition. The purpose of his research was to investigate the relationships between community college student enrollment and registration behaviors (when a student initially enrolls for classes, number of changes made to schedule, when changes are made to the schedule, and what kind of changes are made) and student characteristics and academic outcomes (grade point average, course completion, and persistence to the next semester). The study also investigated whether there were student characteristics that were related to specific enrollment and registration behaviors and examined whether there were interrelationships among the enrollment and registration behaviors studied and interrelationships among the academic outcomes studied. Finally, the study also investigated if enrollment and registration behaviors could predict student academic outcomes.

Summers examined all first-time, full-time college level students seeking a certificate or associate degree who first matriculated at one small rural community college in the Midwest in fall 1994, fall 1995, and fall 1996. The sample (n = 1,365) for this study was comprised of 473 students who first enrolled in the fall semester of 1994, 461 students who first enrolled in the fall semester of 1995, and 431 students who first enrolled in the fall semester of 1996. Utilizing a variety of statistical tests and both multiple linear regression and logistic regression, his study found some interesting relationships and predictions.

Overall, Summers found that significant differences existed between the characteristics of student who persisted and enrolled for spring semester courses as compared to students who dropped out. In this study he found that students who were of traditional age (less than 25 years), White, transfer majors, and not eligible for financial aid were more likely to persist. The only characteristic that did not have any statistically significant difference for attrition was the gender of the student.

In addition, Summers found that students who persisted averaged initial enrollment for fall semester classes nearly 29 days earlier than students who dropped out. Students enrolled in the spring semester averaged making significantly fewer changes to their fall semester course schedule (2.47) as compared to students not enrolled for the spring semester (4.18). Students enrolled in the spring semester averaged making significantly fewer course drops (1.32) than students not enrolled for the spring semester (3.03). Finally, students enrolled in the spring semester averaged making a larger proportion (54.4%) of their schedule changes early in the add-drop period (the first week of the semester) than students not enrolled for the spring semester (31.5%).

Summers also found that fall semester grade point average (GPA) was related to attrition. His study found that there was a statistically significant difference in the mean fall semester GPA of students who did not enroll in classes for the spring semester as compared to those that did enroll (M = 1.71 compared to M = 3.50).

Summers found that the logistic regression model that predicted student attrition from enrollment and registration behaviors, while holding student characteristics constant, included all five of the student characteristic variables and three of the six enrollment and registration variables: number of course drops, when a student initially enrolled for the fall semester, and number of course adds. He found that for each additional course dropped during the fall semester, the odds of enrolling for the spring semester decreased by 50.2%; for each additional day earlier initially enrolled for the fall semester, the odds of enrolling for the spring semester increased by 1.3%; and for each additional course added during the fall semester, the odds of enrolling for the spring semester increased by 52.8%.

Finally, Summers found that the best logistic regression model that predicted attrition from enrollment and registration behaviors and academic outcomes, while holding student characteristics constant, included three of the six enrollment and registration variables: number of course drops, when a student initially enrolled, and number of course adds, plus fall semester GPA and fall semester course completion. His study found that for each additional one-point increase in fall semester GPA, the odds of enrolling for the spring semester increased by 45.9%; for each additional day earlier that a student initially enrolled for the fall semester, the odds of enrolling for the spring semester increased by 1.2%; for each additional course dropped by the student in the fall semester, the odds of enrolling for the spring semester decreased by 24.5%; and for each additional one-unit increase in fall semester course completion, the odds of enrolling for the spring semester increased by 5.25 times.

Future Research Opportunities

Researchers have typically investigated community college student attrition by focusing on student characteristics, environmental factors, and academic variables. Overall, the research suggests that factors leading to higher community college student attrition include working full-time, registering late, not having clear educational goals, performing poorly in classes, and not engaging in a variety of student support services. However, many of these studies have resulted in mixed findings, so more research is needed to understand better the influences and predictors of student attrition and, subsequently, the strategies that may affect its reduction.

Previous research on community college student enrollment and registration behaviors is limited and has either focused on students as late registrants or on course schedule changes (adds and drops) made by students. Further investigation of community college student enrollment and registration behaviors has the potential to provide valuable additional data for understanding and identifying students likely to drop out.

Overall, Summers' study provides empirical evidence that enrollment and registration behaviors can predict a significant amount of the variation in student academic outcomes. This finding tends to validate the importance of enrollment and registration behaviors as a significant set of variables that should be recorded, analyzed, and included in specific decision-making processes at community colleges.

With the ubiquitous use of sophisticated computer systems on college campuses and appropriate database construction techniques, management information systems can be developed automatically to collect enrollment and registration behavior data for later analyses. Computer hardware and software, coupled with automated enrollment procedures, can result in a data collection system that is transparent to the students, efficient for college staff, and provides a wealth of data for institutional researchers. Results from further study might indicate that enrollment and registration behaviors investigated at other institutions can predict a significant amount of the variation in semester GPA, semester course completion, and attrition for the students. These data can then be collected and utilized to improve the students' education at those particular institutions.

To that end, areas for future study include the following suggestions:

* In-depth profiles of students could provide a clearer understanding of the reasons, intentions, and motivations behind the overt behaviors. Future qualitative research might investigate the reasons behind the enrollment and registration behaviors to determine the nature of their frequency and variation for different characteristics of students. Further, the examination of other student characteristics such as high school academic performance, parental educational level, first generation college student status, frequency for changing academic major, and specific academic majors as they relate to registration behaviors, would be valuable. Finally, research needs to be conducted that includes part-time students and students who have previous matriculation histories at the community college being studied.

* More baseline research needs to be conducted exploring the enrollment and registration behaviors of community college students to identify relationships, patterns, and associations between these behaviors and other areas of community college attrition research. For example, research on student characteristics and academic outcomes needs to be examined in relation to enrollment and registration behaviors. In addition, the investigation of these same behaviors over a longer period of time could identify how the behaviors change or remain the same for students. Longitudinal study of enrollment and registration behaviors could provide additional information about the association of these behaviors with academic outcomes.

* Research needs to be conducted at other community colleges that are similar to those that have already been studied to determine if the results can be replicated (i.e., urban institutions or institutions with significantly different student characteristics). Research should also be conducted at institutions with both different student registration options and registration policies to determine how these factors impact registration and enrollment behaviors.

* Research that identifies interaction variables and determines their relationship and association with academic outcomes is needed to understand enrollment and registration behaviors better.

Future Implications for Practitioners

The identification of students likely to drop out allows for the delivery of intervention services prior to withdrawal with the expectation of reversing the dropout decision. Some researchers contend "it is far better to counter the paths of attrition with the early recognition of dropout-prone individuals. The behavior patterns of at-risk freshmen are observable, once we know what to look for" (Levitz & Noel, 1989, p. 70). Further, Kowalski (1977) referred to intervention as serving "potential dropouts before they become dropouts" (p. 78).

In several cases, early intervention has proven to be successful at helping to influence students to remain in college (Bishop & Brenneman, 1986; Bishop & Walker, 1990; Noel, Levitz, & Saluri, 1985). Several researchers found that a large majority of the students that were considered likely candidates for attrition chose to continue in college after receiving counseling (Bishop & Brenneman, 1986). Others found that there was significant evidence that "students who are identified as retention risks tend to persist in their academic careers after receiving counseling" (Bishop & Walker, 1990, p. 89). However, despite many investigations conducted on student characteristics and academic variables such as study time, credit hour load, and grade point average, research has yielded mixed results in terms of the relationship of these variables to attrition (Aquino, 1990).

Within an environment where greater numbers of students enrolling in community colleges are in need of increased levels of support, institutions need to find solutions and strategies for reducing student attrition and increasing student success. More research on community college student behavior is needed to understand better the influences and predictors of student attrition and, subsequently, the strategies that may influence its reduction. The results of this research--when applied as part of a comprehensive institutional system of identification, intervention, and implementation--has the potential for contributing to the reduction of the attrition rate of community college students.

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Michael D. Summers is the Vice President for Education and Student Affairs at Greenville Technical College in Greenville, South Carolina. [e-mail]