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Do intervention programs assist students to succeed in college? A multilevel longitudinal study.

This study, using hierarchical linear models, examined effects of the intervention programs on first-time-full-time students' retention and college cumulative GPA, interacting with students' characteristics, e.g., demographics and college preparedness. Program effects on a three-year trend were also explored. The study results show that the intervention programs had significant effects on retention and college cumulative GPA, and worked better for the first year. Interactions between program type and students' characteristics were also discussed.

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It is reported that about one fourth of students dropped college after their first year (Mallinckrodt & Sedlacek, 1987; Tinto, 1993; Tinto, Russo, & Kadel, 1994), and about 25% of college graduates got their bachelor degrees from the first college they attended (National Center for Education Statistics, 2004). Retention as well as academic performance is critical to students' success in college; therefore, they are important issues in college administration.

Retention is a complex issue involving many different factors. Whether a student departs from an institution is largely a result of the extent to which the student becomes academically and socially connected with the institution. As Tinto's (1975) model indicates, as students are integrated into and become more interdependent with both academic and social elements of a university, the probability that the student will leave the university declines. Astin (1975) also found that involvement was critical to a student's decision to persist or drop out school. In other words, involvement with faculty and student peer groups encourages participation in social and intellectual life of a college and, therefore, helps learning and persistence in college (Astin, 1993; Berger, 1999; Campbell & Campbell, 1997; DeBerard, Spielmans, & Julka, 2004; Nagda, Gregerman, Jonides, von Hippel, & Lerner, 1998; Tinto, 1993).

Other factors that may also affect retention and academic performance include institutional type (Chapman & Pascarella, 1983), motivations for attending college (Allen, 1999; Stage, 1989), financial aid (Cabrera, Nora, & Castaneda, 1992; Glynn, Saner, & Miller, 2003; Sandler, 2000; Wetzel, O'Toole, & Peterson, 1999), fulfillment of expectations for college (Braxton, Vesper, & Hosler, 1995; Glynn et al., 2003), sense of community in residence halls (Brower, 1992), self-efficacy (Peterson, 1993), attitudes (Fishbein & Ajzen, 1975; Glynn et al., 2003), and maladaptive coping strategies (DeBerard et al., 2004). In addition, previous research results show that interactions of those factors with students' characteristics, e.g., demographics and college preparedness, play an important role on their success in college.

In the literature, most research on students' success in college has used student enrollment data to explore factors affecting students' success in college. Such research has provided much valuable information to college administrators, faculty and staff. As a result, many universities have setup programs based on the theories in the field and the results of research trying to improve students' academic achievement and prevent students from attrition. However, research on intervention program effects somewhat falls behind. Such research would make an important contribute to the literature.

This present study is an effort to examine the intervention programs conducted in a Midwest urban university. In an effort to manage the attrition problem and improve students' academic performance, the university has initiated nearly 100 intervention programs, from 2001 to 2003, with the Success Challenge grant funded by the state. The Success Challenge grant has two components: 1) challenging university campuses to enable at-risk students successfully to earn baccalaureate degrees; and 2) challenging university campuses to enable baccalaureate seeking students to complete their degrees in a timely fashion, typically four years. In theory, these programs were set up based on Tinto's (1975) Student Integration Model and Astin's (1975) Theory of Involvement. Most of the interventions programs were designed to promote student-to-student interaction, faculty-to-student interaction, student involvement, academic engagement, and academic assistance. They can be roughly categorized into six different program types based on the types of services they provide. The categories consist of advising, academic help, first-year experience (FYE), social integration, general orientation, and financial aid. The Financial Aid category was dropped in the present study due to the small number of students in that group.

Murtaugh, Burns, & Schuster, (1999) stated that freshman orientation may be effective to reduce the risk of dropping out. It is not clear whether the other types of intervention are effective or not, and how long the effect lasts. It is also a great concern to the stakeholders and the administrators about which programs work, how they work, and which one works better, and to whom. The purpose of this present study is to try to answer these questions by examining the effects of the intervention programs on retention and students' academic performance which is measured by college cumulative GPA across 3 years, interacting with students' characteristics, using multilevel longitudinal modeling.

Methods

Setting and Participants

1305 First-Time-Full-Time (FTFT) students who voluntarily participated in one of 20 Success Challenge intervention programs at the beginning of the fall quarter of 2000 at a large Midwest urban university were included in the study. The mean age was 18.62, with SD = .56. The sample is made up 46.7% female. The sample has 83.2% Caucasian, 11.3% African-American, and 5.5% other race. The average high school GPA was 2.91 (SD = .64). In addition, 18% of the participants were from high selective colleges, 11% moderate selective, 39% liberal selective and 32% not selective. Table 1 shows the descriptive statistics by program type.

Success Challenge Programs

Advising programs. Advising programs included programs such as Central Advising, the Pre-Professional Advising Center, and Career Navigator. Central Advising consists of satellite advising centers staffed by a team of professional academic advisors. The Pre-Professional Advising Center provides general advising to students who have an interest in exploring pre-professional majors. The Career Navigator series is a six-stage program designed to help freshmen and sophomores to explore potential majors and careers.

Academic help programs. Academic help programs included Tutorial Services, Co-op Calculus, and the Engineering Aerospace Collaborative. Tutorial services serve to improve students' grades in specific courses and empower the students as active, independent learners. Co-op calculus is designed to help freshmen succeed in the introductory calculus sequence. The Engineering Aerospace Collaborative selected underrepresented pre-engineering students to take part in activities such as a summer bridge program, introductory engineering workshops, and cooperative learning courses in math and physics.

First Year Experience programs. FYE courses are designed to incorporate a core set of common classroom experiences focusing on college survival skills, transition issues, and career and personal development. Some of the FYE courses include FYE Librarian and Orientation to Learning. The purpose of FYE Librarian is to instruct first year students on how to use the many research resources and services available at the university to further develop their research skills. Orientation to Learning is a course requirement for all incoming freshmen. It is designed to help first year students understand the many challenges of college life and to introduce them to the many resources available to them on campus.

Social integration programs. Social integration programs served to increase both student-to-student and faculty-to-student interactions. The programs under this category included Learning Communities and Faculty/Student Activities. Learning Communities is a course structure created by students registering for a block of classes together. Students in learning communities form supportive peer groups, which help them to become both academically and socially connected. The Faculty/Student Activities program hosts a number of events which bring students and faculty together outside of the classroom.

General orientation programs. Orientation programs provide students with a description of program offerings, college expectations, information about assistance and services for examining interests and abilities, encouragement to establish working relationships with faculty, information about services that help with adjustment to college, and financial aid information.

Data Source and Measures

The data were retrieved from the university enrollment database and the Success Challenge Program records. The current study examined two outcome measures: (a) the retention rates for the three fall quarters of the academic years of 2001-2002, 2002-2003, and 2003-2004; and (b) the college cumulative GPA for the three academic years of 2000-2001, 2001-2002, and 2002-2003. The program categories of Advising, First-year experience, Social Integration, Academic Help, and General Orientation served as the characteristics of the programs based on their service type. The students' characteristics included in the present study were gender, ethnicity, high school GPA, and college selectivity. Students' age was not modeled since it was not a significant factor in this present study.

Data Analysis

Since the data were nested in nature, that is, the repeated observations (three-year retention rates and college cumulative GPA) were nested within the individuals and the individuals in turn were nested within the programs, three-level hierarchical modeling (HLM; Raudenbush & Bryk, 2001) was conducted for examining the program effects on the retention and the college cumulative GPA. For the retention, a 3-level logistic model was used because the outcome variable was dichotomous. The first level model was about the three-year retention rates, the second level model was about the students, and the third level model was about the programs. The students' characteristics (e.g., gender, ethnicity, and high school GPA) and the program characteristics (e.g., program types and percent female students in the program) were modeled at levels 2 and 3, respectively. For academic performance, we treated the college cumulative GPA as a continuous variable, thus a 3-level regression model was conducted. The first level model was about the three-year college cumulative GPA, the second level model was about the students, and the third level model was about the programs. The time-varying variables (e.g., residence and course load), the students' characteristics (e.g., gender, ethnicity, and high school GPA) and the program characteristics (e.g., program types) were modeled at levels 1, 2 and 3, respectively.

Results

The retention rates across the fall quarters of the three academic years were .67, .54, and .49, with a mean of .57. The three-year cumulative university GPA were 2.33 (SD = .97), 2.75 (SD = .62), and 2.58 (SD = .73), respectively, with a mean of 2.52 (SD = .84).

Program Effects on Retention

An unconditional hierarchical logistic linear model revealed that 22.54% of total variation in the logit of retention was among the programs, which justified the use of a hierarchical linear modeling. This result also indicates that different Success Challenge Programs had different significant effects on retention. In order to find which intervention program and what student characteristics explained a 3-year retention trend, a three-level logistic HLM longitudinal model was conducted.

Table 2 displays the parameter estimates from the multilevel longitudinal analysis for retention. From Table 2, we can see that the major findings are: (a) The academic-help programs significantly ([[gamma].sub.001] = .997, p < .010) increased the retention rates for the first year; (b) The advising ([[gamm].sub.011] = .537, p < .001) and social integration ([[gamma].sub.012] = .487, p < .001) programs significantly helped students who were in higher selective colleges return to school after the first year; (c) Female students more likely ([[gamma].sub.020] = .268, p< .046) returned to school after first year; and (d) Students with higher high school GPA more likely returned to school after first year ([[gamma].sub.030] = .412, p < .005) and also more likely returned to school for the following two years ([[gamma].sub.110] = .171, p < .009).

Program Effects on Academic Performance (GPA)

An unconditional HLM revealed that 17.93% of total variation in the academic performance measured by the college cumulative GPA was among the programs. In other words, for students who participated in different programs would have different GPAs, which may also show that the Success Challenge Programs had significant different effects on the college cumulative GPA. An HLM longitudinal model, controlling for two significant time-varying covariates, cumulative credit hours and residence (dormitory vs. other place), was conducted for exploring what students' characteristics and programs helped the students' GPA.

Table 3 shows the parameter estimates from multilevel longitudinal analysis for GPA.

The main findings are: (a) The general orientation programs significantly ([[gamma].sub.001] = .374, p < .001) helped all students increase GPA for the first year; (b) The social integration programs significantly ([[gamma].sub.011] = .175, p < .001) helped students who were in selective colleges increase their GPAs for the first year; (c) The GPA increasing trend was significantly ([[gamma].sub.101] = -.061, p < .001) slower for students who participated in the general orientation programs than students who participated in the other programs; (d) Female students ([[gamma].sub.020] = .090, p < .006), White students ([[gamma].sub.030] = .306, p < .001), or students with higher high school GPA performed significantly higher in GPA ([[gamma].sub.040] = .532, p< .001) than other students for the first year; and (e) For White students ([[gamma].sub.110] = -.052, p < .001), African-American students ([[gamma].sub.120] = -.078, p < .001), or students with higher high school GPA ([[gamma].sub.130] = -.024, p < .036), the increasing GPA trend across the three years was significantly slower than other students.

Discussion

This study utilized multilevel longitudinal modeling to explore the effectiveness of the intervention programs on FTFT students' retention and academic achievement measured by college cumulative GPA. The findings of the study imply: 1) early intervention programs assist to retain first year students in college; 2) academic-help programs help participants to return; 3) for those who are better prepared for college, social interactions with faculty, staff, and their peers enhance their returning to school; 4) social integration programs increase GPA for students who are in more selective colleges; and 5) general orientation helps students increase GPA at early stage, but the effect did not necessarily last.

This study confirmed Tinto's (1993) statement that involvement in social and intellectual life of a college helps learning and persistence in college. Other studies (Alikonis, Guo, & Miller, 2005) about the Success Challenge programs show that participation in more than one Success challenge program greatly helped student both in retention and increase of GPA, not only in the first year, but also second and third year.

The results of this study show that general orientation is necessary at the beginning of the college life. School administrators may also need to focus more on social- and academic-help-related specific programs other than just general orientation to help student return to school and increase GPA. For better college prepared students, universities may need programs that promote student social interactions with faculty, staff and their peers to help them return to their schools. For the under prepared students, academic help is more needed. A combination of academic help and social interaction may work better.

It would give us a deeper insight if student's psychological factors and family background were added into the models. If more program characteristics were available, the program effects could be explored more thoroughly. For further study, more program information, such as who ran the program, budgetary information, the length of the program may explain more clearly about the program effects. The students in the current study participated in only one of the programs, but there were almost equal number of students who participated in multiple programs. Thus, the program effects on students' retention rates and academic performance were under estimated for the all FTFT students. Moreover, this study did not intent to draw a causal effect of the intervention programs on students' retention and academic performance for all FTFT students in the population, because the data were retrieved by convenience from the database rather than randomly selected from the population. Nonetheless, this study at least provided an empirical evidence of the effect of the intervention programs on students' retention and academic performance.

References

Alikonis, C., Guo, S., & Miller, C. (2005, May). Success challenge: Retention efforts at a Midwest urban university. Paper presented at the 45th Forum of the Association of Institutional Research, San Diego, CA.

Allen, D. (1999). Desire to finish college: An empirical link between motivation and persistence. Research in Higher Education, 40, 461-485.

Astin, A. W. (1975). Preventing students from dropping out. San Francisco: Jossey-Bass Publishers.

Astin, A. W. (1993). What matters in college?: Four critical years revisited. San Francisco: Josey-Bass Publishers.

Berger, J., & Milem, J. (1999). The role of student involvement and perceptions of integration in a causal model of student persistence. Research in Higher Education, 40, 641-664.

Braxton, J. M., Vesper, N., & Hosler, D. (1995). Expectations for college and student persistence. Research in Higher Education, 13, 595-612.

Brower, A. (1992). The "Second Half" of student integration: The effects of life task predominance on student persistence. Journal of Higher Education, 63, 441-462.

Cabrera, A. F., Nora, A., & Castaneda, M. B. (1992). The role of finances in the persistence process: A structural model. Research in High Education, 33, 571-593.

Campbell, T. A., & Campbell, D. E. (1997). Faculty/student mentor program: Effects on academic performance and retention. Research in Higher Education. 38, 727-742.

Chapman, D., & Pascarella, E. (1983). Predictors of academic and social integration of college students. Research in Higher Education, 19, 295-322.

DeBerard, M. S., Spielmans, G. I., & Julka, D. L. (2004). Predictors of academic achievement and retention among college freshmen: A longitudinal study. College Student Journal, 38, 66-80.

Fishbein, M., & Ajzen, 1. (1975). Belief attitude, intention and behavior. Reading, MA: Addison-Wesley.

Glynn, J. G., Sauer, P. L. & Miller, T. E. (2003). Signaling student retention with prematriculation data. NASPA Journal, 41, 41-67.

Mallinckrodt, B., & Sedlacek, W. E. (1987). Student retention and the use of campus facilities by race. NASPA Journal, 24, 28-32.

Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40, 355-371.

Nagda, B. A., Gregerman, S. R., Jonides, J., von Hippel, W., & Lerner, J. S. (1998). Undergraduate student-faculty research partnerships affect student retention. The Review of Higher Education, 22, 55-72.

National Center for Education Statistics (2004). Student effort and educational progress. Retrieved May 25, 2005, from http://nces.ed.gov/programs/coe/2004/section 3/table.asp?tableID=61.

Peterson, S. L. (1993). Career decision-making self-efficacy and institutional integration of underprepared college students. Research in Higher Education, 34(6), 659-675.

Raudenbush, S. W., & Bryk, A. S. (2001). Hierarchical linear models: Applications and data analysis methods (2nd Ed.). Thousand Oaks, CA: Sage.

Sandier, M. E. (2000). Career decision-making self-efficacy, perceived stress, and an integrated model of student persistence: A structural model of finances, attitudes, behavior, and career development. Research in Higher Education, 41, 537-580.

Stage, F. K. (1989). Motivation, academic and social integration, and the early dropout. American Educational Research Journal, 26, 385-402.

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89-125.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd Ed.). Chicago, IL: University of Chicago Press.

Tinto, V., Russo, P., & Kadel, S. (1994). Constructing educational communities: Increasing retention in challenging circumstances. Community College Journal, 64, 26-30.

Wetzel, J. N., O'Toole, D., & Peterson, S. (1999). Factors affecting student retention probabilities: A case study. Journal of Economics and Finance, 23(1), 44-55.

WEI PAN

Division of Educational Studies, University of Cincinnati

SHUQIN GUO

Policy and Evaluation Division, California Department of Education

CAROLINE ALIKONIS

Office of Institutional Research, University of Cincinnati

HAIYAN BAI

Department of Educational Research, Technology, and Leadership

University of Central Florida
Table 1
Descriptive Statistics by Program Type

                                    Total
Variable              Statistic  (n = 1305)

Age                      M          18.62
                         Min        16.68
                         Max        24.86
                         SD           .56

Gender
  Female                 %            .47
  Male                   %            .53

Ethnicity
  White                  %            .83
  Black                  %            .11
  Other                  %            .06

HS GPA                   M           2.91
                         Min         1.20
                         Max         4.50
                         SD           .64

College Selectivity:
  Not Selective          %            .32
  Liberal Selective      %            .39
  Moderate Selective     %            .11
  High Selective         %            .18

                                      Program Type

                                  Advising       FYE
Variable              Statistic   (n = 285)   (n = 284)

Age                      M          18.56       18.60
                         Min        16.68       17.92
                         Max        19.70       22.83
                         SD           .42         .52

Gender
  Female                 %            .53         .32
  Male                   %            .47         .68

Ethnicity
  White                  %            .77         .90
  Black                  %            .16         .06
  Other                  %            .07         .04

HS GPA                   M           3.22        3.06
                         Min         1.62        1.87
                         Max         4.00        4.45
                         SD           .47         .50

College Selectivity:
  Not Selective          %            .02         .03
  Liberal Selective      %            .64         .38
  Moderate Selective     %            .00         .51
  High Selective         %            .35         .08

                                            Program Type

                                   Social     Academic     General
                                 Integration    Help     Orientation
Variable              Statistic   (n = 238)   (n = 71)    (n = 427)

Age                      M          18.58       18.52       18.70
                         Min        17.72       17.44       17.68
                         Max        24.86       20.85       23.56
                         SD           .65         .45         .63

Gender
  Female                 %            .63         .24         .47
  Male                   %            .37         .76         .53

Ethnicity
  White                  %            .88         .92         .79
  Black                  %            .05         .04         .16
  Other                  %            .07         .04         .05

HS GPA                   M           3.22        3.39        2.34
                         Min         1.97        2.26        1.20
                         Max         4.00        4.50        4.00
                         SD           .46         .45         .54

College Selectivity:
  Not Selective          %            .01         .00         .94
  Liberal Selective      %            .82         .18         .03
  Moderate Selective     %            .00         .00         .00
  High Selective         %            .17         .82         .03

Table 2
Fixed Effect Estimates from Multilevel Longitudinal Analysis
for Retention

Fixed Effect                              Coefficient     SE

For Intercept, [[pi].sub.0]
  For Intercept, [[beta].sub.00]
    Intercept, [[gamma].sub.000]             -.135       .418
    ACADEMIC HELP, [[gamma].sub.001]          .997       .341
    % of FEMALE, [[gamma].sub.002]          -1.387       .422
  For SELECTIVE, [[beta].sub.01]
    Intercept, [[gamma].sub.010]             -.094       .103
    ADVISING, [[gamma].sub.011]               .537       .110
    SOCIAL, [[gamma].sub.012]                 .487       .135
  For FEMALE, [[beta].sub.02]
    Intercept, [[gamma].sub.020]              .268       .135
  For HIGH SCHOOL GPA, [[beta].sub.03]
    Intercept, [[gamma].sub.030]              .412       .143
For ACADEMIC YEAR slope, [[pi].sub.1]
  For Intercept, [[beta].sub.10]
    Intercept, [[gamma].sub.100]             -.869       .193
  For HIGH SCHOOL GPA, [[beta].sub.11]
    Intercept, [[gamma].sub.110]              .171       .065

Fixed Effect                                   t          df      p

For Intercept, [[pi].sub.0]
  For Intercept, [[beta].sub.00]
    Intercept, [[gamma].sub.000]             -.323         17    .750
    ACADEMIC HELP, [[gamma].sub.001]         2.924         17    .010
    % of FEMALE, [[gamma].sub.002]          -3.289         17    .005
  For SELECTIVE, [[beta].sub.01]
    Intercept, [[gamma].sub.010]             -.914       1301    .361
    ADVISING, [[gamma].sub.011]              4.865       1301    .000
    SOCIAL, [[gamma].sub.012]                3.601       1301    .001
  For FEMALE, [[beta].sub.02]
    Intercept, [[gamma].sub.020]             1.988       1301    .046
  For HIGH SCHOOL GPA, [[beta].sub.03]
    Intercept, [[gamma].sub.030]             2.873       1301    .005
For ACADEMIC YEAR slope, [[pi].sub.1]
  For Intercept, [[beta].sub.10]
    Intercept, [[gamma].sub.100]            -4.498         19    .000
  For HIGH SCHOOL GPA, [[beta].sub.11]
    Intercept, [[gamma].sub.110]             2.612       1303    .009

Note. SELECTIVE means that the FTFT student was in a selective
college, and FEMALE and HIGH SCHOOL GPA are self-explanatory, %
of FEMALE represents the percent of female participants in the
intervention program, and ACADEMIC HELP, ADVISING, and SOCIAL
are the types of intervention programs.

Table 3
Fixed Effect Estimates from Multilevel Longitudinal Analysis for GPA

Fixed Effect                             Coefficient    SE

For Intercept, [[pi].sub.0]
  For Intercept, [[beta].sub.00]
    Intercept, [[gamma].sub.000]             .227      .087
    ORIENTATION, [[gamma].sub.001]           .374      .070
  For SELECTIVE, [[beta].sub.01]
    Intercept, [[gamma].sub.010]             .043      .029
    SOCIAL, [[gamma].sub.011]                .175      .026
  For FEMALE, [[beta].sub.02]
    Intercept, [[gamma].sub.020]             .090      .032
  For WHITE, [[beta].sub.03]
    Intercept, [[gamma].sub.030]             .306      .041
  For HIGH SCHOOL GPA, [[beta].sub.04]
    Intercept, [[gamma].sub.040]             .532      .033
For ACADEMIC YEAR slope, [[pi].sub.1]
  For Intercept, [[beta].sub.10]
    Intercept, [[gamma].sub.100]             .119      .033
    ORIENTATION, [[gamma].sub.101]          -.061      .014
  For WHITE, [[beta].sub.11]
    Intercept, [[gamma].sub.110]            -.052      .015
  For BLACK, [[beta].sub.12]
    Intercept, [[gamma].sub.120]            -.078      .017
  For HIGHSCHOOL GPA, [[beta].sub.13]
    Intercept, [[gamma].sub.130]            -.024      .011
For RESIDENCE slope, [[pi].sub.2]
  For Intercept, [[beta].sub.20]
    Intercept, [[gamma].sub.200]             .060      .013
For CREDIT HOURS slope, [[pi].sub.3]
  For Intercept, [[beta].sub.30]
    Intercept, [[gamma].sub.300]             .002      .000

Fixed Effect                                  t         df     p

For Intercept, [[pi].sub.0]
  For Intercept, [[beta].sub.00]
    Intercept, [[gamma].sub.000]            2.609        18   .018
    ORIENTATION, [[gamma].sub.001]          5.330        18   .000
  For SELECTIVE, [[beta].sub.01]
    Intercept, [[gamma].sub.010]            1.497      1300   .134
    SOCIAL, [[gamma].sub.011]               6.690      1300   .000
  For FEMALE, [[beta].sub.02]
    Intercept, [[gamma].sub.020]            2.789      1300   .006
  For WHITE, [[beta].sub.03]
    Intercept, [[gamma].sub.030]            7.498      1300   .000
  For HIGH SCHOOL GPA, [[beta].sub.04]
    Intercept, [[gamma].sub.040]           16.005      1300   .000
For ACADEMIC YEAR slope, [[pi].sub.1]
  For Intercept, [[beta].sub.10]
    Intercept, [[gamma].sub.100]            3.610        18   .002
    ORIENTATION, [[gamma].sub.101]         -4.357        18   .000
  For WHITE, [[beta].sub.11]
    Intercept, [[gamma].sub.110]           -3.474      1301   .001
  For BLACK, [[beta].sub.12]
    Intercept, [[gamma].sub.120]           -4.522      1301   .000
  For HIGHSCHOOL GPA, [[beta].sub.13]
    Intercept, [[gamma].sub.130]           -2.098      1301   .036
For RESIDENCE slope, [[pi].sub.2]
  For Intercept, [[beta].sub.20]
    Intercept, [[gamma].sub.200]            4.556      3014   .000
For CREDIT HOURS slope, [[pi].sub.3]
  For Intercept, [[beta].sub.30]
    Intercept, [[gamma].sub.300]           14.882      3014   .000

Note. RESIDENCE means whether the student lived in a dormitory, and
CREDIT HOURS is the cumulative credit hours. SELECTIVE means that
the FTFT student was in a selective college, and other predictors
are self-explanatory. ORIENTATION and SOCIAL are the types of
intervention programs.
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Author:Pan, Wei; Guo, Shuqin; Alikonis, Caroline; Bai, Haiyan
Publication:College Student Journal
Article Type:Report
Geographic Code:1USA
Date:Mar 1, 2008
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