bu retention report summer 2010

1

Baylor University Retention Study

Summer 2010

Authors:

Phil Davignon, PhD Candidate in Sociology Kathleen Morley, Office of Institutional Research and Testing

Stephanie Simon, PhD Candidate in Statistical Science Tracey Sulak, PhD Candidate in Educational Psychology

Sinda Vanderpool, Office of the Provost

Consultants:

Lucy Barnard-Brak, Educational Psychology Jack Tubbs, Statistical Science

2

Table of Contents

I. Introduction A. Overview ..............................................................................................6 B. A Brief Background on National Retention Research .................................6 C. Project Outline and Description of Analysis Performed..............................9 D. Explanation of Analyses ..............................................................................9 II. Institutional Retention Analysis Results A. Overall Logistic Regression for 2007 and 2008 Cohorts ...........................11 B. Cluster analysis for 2008 Cohort ...............................................................16 1. Cluster Analysis for Institutional Variables ...................................16 2. Cluster Analysis for Individual Variables ......................................16

3. Institutional by Individual Cluster Cross-analysis .........................17 C. Overall Institutional Recommendations ...................................................18 III. Retention Analysis on Groups of Interest and Major Groupings A. Logistic Regression Using All Variables (groups of interest) ...................20 1. Biology Majors ..............................................................................20 a. 2007 Biology b. 2008 Biology c. Recommendations for Biology Majors 2. Engineering and Computer Science Majors ..................................21 a. 2007 ECS b. 2008 ECS c. Recommendations for ECS Majors 3. Premed Students.............................................................................23 a. 2007 Premed b. 2008 Premed c. Recommendations for Premed Students 4. STEM Majors.................................................................................24 a. 2007 STEM b. 2008 STEM c. Recommendations for STEM Majors 5. Undecided Students .......................................................................26 a. 2007 Undecided b. 2008 Undecided c. Recommendations for Undecided Students B. Logistic Regression Using Admission Variables (major groupings) ........27 1. Biology ...........................................................................................27 a. Findings for Biology 2008

b. Recommendations for Biology (admissions variables only) 2. Chemistry, Hard Sciences and Math ..............................................28 a. Findings for Chemistry, Hard Sciences and Math 2008

b. Recommendations for Chemistry, Hard Sciences and Math

3

3. Communications ............................................................................28 a. Findings for Communications 2008

b. Recommendations for Communications 4. Education .......................................................................................29 a. Findings for Education 2008 b. Recommendations for Education 5. Engineering and Computer Science ...............................................29 a. Findings for ECS 2008 b. Recommendations for ECS 6. Family Consumer Sciences and Fine Arts .....................................30 a. Findings for FCS and Fine Arts 2008 b. Recommendations for FCS and Fine Arts 7. Health-related .................................................................................30 a. Findings for Health-related 2008 b. Recommendations for Health-related 8. Honors College ..............................................................................31 a. Findings for Honors College 2008 b. Recommendations for Honors College 9. Humanities .....................................................................................31 a. Findings for Humanities 2008 b. Recommendations for Humanities 10. Music ............................................................................................32 a. Findings for Music 2008 b. Recommendations for Music 11. Nursing ...........................................................................................32 a. Findings for Nursing 2008 b. Recommendations for Nursing 12. Political science and International/Area ........................................33 a. Findings for Political Science and International/Area2008

b. Recommendations for Political Science and International/Area

13. Pre-business ...................................................................................34 a. Findings for Pre-business 2008 b. Recommendations for Pre-business 14. Psychology and Social Work .........................................................34 a. Findings for Psychology and Social Work 2008 b. Recommendations for Psychology and Social Work 15. Undecided ......................................................................................35 a. Findings for Undecided 2008 b. Recommendations for Undecided IV. Freshman Gateway Courses A. Logistic Regression Using All Variables (selected gateway courses) .......36 1. Biology 1305 ..................................................................................36 a. Findings for BIO 1305 2007 b. Findings for BIO 1305 2008

4

c. Recommendations for Biology 1305 2. English 1302 ..................................................................................37 a. Findings for ENG 1302 2007 b. Findings for ENG 1302 2008 c. Recommendations for English 1302 3. Math 1304 ......................................................................................38 a. Findings for MTH 1304 2007 b. Findings for MTH 1304 2008 c. Recommendations for Math 1304 4. Math 1321 ......................................................................................39 a. Findings for MTH 1321 2007 b. Findings for MTH 1321 2008 c. Recommendations for Math 1321 5. Religion 1310 .................................................................................40 a. Findings for REL 1310 2007 b. Findings for REL 1310 2008 c. Recommendations for Religion 1310 B. Linear Regression Applied to Grade Received in Course (selected gateway courses) ............................................................................................41

1. Biology 1305 ..................................................................................41 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

2. Chemistry 1301 ..............................................................................43 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

3. English 1302 ..................................................................................44 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

4. Math 1304 ......................................................................................45 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

5. Math 1321 ......................................................................................46 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

6. Religion 1310 .................................................................................47 a. Significant Factors on Course Grade b. Descriptive Information by Grade in Course c. Recommendations

5

Technical notes ............................................................................................48 Appendices

A. Overall Institutional Output 1. Master Variable List ............................................................................49 2. Logistic Regression Output .................................................................57 3. Cluster Analysis Output (institutional, individual) ..............................58

B. Logistic Regression Using All Variables (Groups of Interest) ..................62 C. Logistic Regression Using Admission Variables (Major Groupings) .......82

D. Studies of Freshman Gateway Courses Applied to Retention and Grade Received .........................................................................................112

References ..........................................................................................138

6

I. Introduction

A. Overview

The purpose of this retention data-mining project, conducted during the summer of 2010, was to perform a comprehensive analysis of retention rates within the 2007 and 2008 freshman cohorts at Baylor University. The two cohorts were chosen because they represented the most recent cohorts with a full set of retention data. Second year retention data is typically collected on the 12th day of the semester and this information was not yet available for the 2009 cohort. This report focused only on retention at the institution, not retention within programs. At Baylor, overall freshman to sophomore retention rates currently range from 83 to 86%, and this has a profound negative impact on graduation rates. When you break down the freshmen cohorts into major groupings, retention rates range from 75.2% to 93%. The current analysis may help the university establish policies and data-driven strategies that will have a positive impact on student retention, and, in turn, graduation rates.

B. A Brief Background on National Retention Research

College retention research has focused on three general issues: demographic variables, individual characteristics, and institutional programs. Different theories have been proposed about the relationships between the three issues, with the most popular and widely-tested relying on the sociological perspective. With the advent of more advanced statistical procedures, it has become possible to test empirically interactionalist theories, such as Tinto’s Interactionalist Theory (1993), Bean and Metzer’s Non-traditional Student Attrition Model (Bean, 1980) and Astin’s Theory of Involvement (1984). The three theories referenced above share four common attributes that make each applicable to Baylor University’s retention research project:

• Each theory is data-driven. • The theories are applicable to student departure in residential colleges or

universities. • Student retention is framed by each theory as an ill-structured problem. • Each theory is testable and parsimonious, or uses the least number of

variables to explain a phenomenon. Vincent Tinto’s theory views student departure as a longitudinal process dependent upon student integration. The model helps differentiate between student leaving behaviors, such that voluntary withdrawals, dropouts, stop outs, academic failure, and transfers may all be viewed separately. With dropouts, Tinto hypothesized that a lack of social and academic integration would result in a higher likelihood of student dropout. Student integration may be measured at the individual level and at the institutional level, but the greater the student integration at each level, the lower the student departure rate. Tinto also suggested that individual pre-enrollment characteristics may affect these interactions. Institutional commitment and goal commitment encompass many of the individual student characteristics, like high school educational preparation, generally

7

measured by university retention offices. The level of institutional commitment and goal commitment varies for each student and has a direct effect on subsequent social and academic integration. Of all the theories for university retention, Tinto’s theory has received the most empirical testing. The model has rarely been tested as a whole, but the individual parts have been studied intensely. Tinto’s theory has been criticized for its limited applicability to non-traditional students, but despite the criticisms, it remains the most widely used and tested models of university retention. The Non-traditional Student Attrition Model extends Tinto’s theory by accounting for the unique characteristics of non-traditional students. Bean and Metzner’s model suggests that for students who live off-campus, commute, are over age 25, or attend part-time, social integration may be far less important than academic integration. For this population, environmental factors, like financial contribution of family, may also directly impact student persistence and attrition. While this model has not been as popular as Tinto’s original model, empirical testing has shown that it may be more applicable to the non-traditional population. As with Tinto’s model, individual parts of the Non-traditional Student Attrition Model appear to work better than the model as a whole. The environmental factors portion of the model is particularly variable among students and appears to be the weakest factor in empirical testing. Astin’s Theory of Involvement uses the same constructs as the two previous theories, academic and social integration. Individual student characteristics play an important role in determining the level of social and academic integration, but Astin also argues that the quality of resources available at the institution may affect student integration. For example, if a university values student/faculty interactions and creates high quality programs that encourage the interactions inside and outside the classroom, then it is likely that students will have a high level of academic and social integration with the faculty members participating in such a program. Student development and learning are contingent upon the quality and quantity of student involvement at the university, according the Astin’s model. Student time is viewed as a limited resource and how a student chooses to use his/her time is directly related to student involvement. Student development is restricted by the amount of time and energy a student is willing to dedicate to activities at the university. The most important element of the Astin model is the addition of faculty as a catalyst for student integration. Specific pedagogical techniques utilized in compensatory education are encouraged at the university level. An example is the student-centered classroom where the focus of a course is on development of the student as opposed to the more traditional classroom method that focuses on content delivery through lecture. Astin’s model has received the least empirical testing of the three models discussed at the university level, but the same constructs have shown strong applicability at the compensatory education level.

Meta analysis research utilizing the interactionalist approach has isolated several factors related to student retention in ACT’s Third National Survey What Works in Student Retention? (2004). Academic factors related to retention include high school grade point average and assessment scores. Non-academic factors related to retention include: academic self-confidence, academic related skills, academic goals, institutional

8

commitment, social support, institutional selectivity and social involvement. The factors are listed in order of strength.

One of the primary recommendations that arose from ACT’s 2004 study was to take into consideration both academic and non-academic factors as the institution designs its integrated approach to student success. Another important recommendation was to develop and implement an early alert system based on factors including high school GPA, socioeconomic information, placement exams, first semester college GPA, attendance records, and non-academic information gleaned from surveys. All of the schools surveyed were asked to identify three campus retention practices that had the greatest impact on student retention. All of the respondents identified at least one of the following:

• Freshman seminar/university 101 for credit • Tutoring program • Advising interventions with selected student populations • Mandated course placement testing • Comprehensive learning assistance center/lab

In the Fourth National Survey for private four-year colleges and universities by ACT published in 2010, the above findings were reinforced. The top factors that rose to the top as significant in improving student retention were: the creation of an advising center, advising interventions on selected populations, integration of advising with the First Year Experience course, faculty mentoring of students, and required on campus housing for freshmen. Building upon Astin’s theory of integration, recent research also focuses on the direct correlation between student engagement and retention. Many institutions use the results of the National Survey of Student Engagement on their campuses to drive strategic choices. While it may seem intuitive that bringing in brighter and more motivated students would yield positive results in retention and student success, we must also figure institutional factors into the equation. Effective educational practices are essential for creating the right environment; universities that wish to improve must examine “ways the institution allocates resources and organizes learning opportunities and services to induce students to participate in and benefit from such activities” (Kuh, 2005, p. 9). Faculty members and administrators must be committed to arranging the curriculum and programming so that students become more engaged in learning and the university community. Reviews of current research in college retention indicate directions for future study. As the student body composition at most universities becomes increasingly diverse, it will become important to investigate the interaction of demographic variables with institutional variables in order to design programs that meet the needs of current students (Reason, 2009). In addition, many studies have indicated high school preparation as evidenced by high school grade point average is strongly related to retention and may account for as much as 29% of the variance in retention (Tross, Harper, Osher, & Kneidinger, 2000). More recent findings suggest that the Merit Indicator may be a better predictor of retention as it takes into consideration the standing of student within a particular high school and provides a better estimate of a student’s performance given the resources available (St. John, Paulsen & Starkey, 2001).

9

C. Project Outline and Description of Analysis Performed The current project is comprised of three separate but related projects. The first project involves analysis of the retained and non-retained students. The second project focuses on specific programs and retention rates associated with students enrolled in these programs. The third project entails a narrow focus on specific freshman courses and factors associated with successful completion of these courses. The following research questions will be addressed for each section of the project. Project One

• What factors are associated with retention and non-retention in the freshman class of 2007 and 2008?

• Among the factors associated with non-retention, which ones may represent a need for targeted intervention by the university?

Project Two • Which programs are associated with relatively lower levels of retention

for freshman students? • What factors are associated with success in the programs with lower levels

of retention? • What predictive factors for success can be used to refine admission

standards for these programs? Project Three

• What is the effect of performance in key freshman courses on future retention? (e.g. Biology 1305, English 1302, Chemistry 1301, Math 1321)

• What is the relationship between high school preparation and success in the key courses?

• Is the effect of success in key courses similar across majors?

D. Explanation of Analyses Analyses were performed on data from the 2007 and 2008 freshman cohorts using MPlus (v. 4.2), SAS (v. 9.2), SAS Enterprise Miner (6.0) and SPSS (v. 18). Analyses were performed separately on each cohort. 2007

• Logistic regression using fall retention as an outcome variable for the 2007 cohort

• Logistic regression using retention as an outcome variable for specific groups of interest (Premedical/pre-dental, STEM, Undecided, Engineering and Computer Science, Biology)

• Logistic regression using retention as an outcome variable for specific freshman courses (English 1302, Math 1321, Religion 1310, Biology 1305)

• Descriptive statistics

10

2008 • Logistic regression using fall retention as an outcome variable for the

2008 cohort • Logistic regression using fall retention as an outcome variable for groups

of interest (Premedical/predental, STEM, Undecided, Engineering and Computer Science, Biology)

• Logistic regression using fall retention as an outcome variable for groups of interest

• Logistic regression using fall retention as an outcome variable for specific freshman courses (English 1302, Math 1321, Religion 1310, Biology 1305)

• Linear regression using fall grade point average as an outcome variable for specific freshman courses

• Two-Step Cluster analysis by individual and institutional variables • Descriptive statistics

Regression is a statistical procedure for describing the relationship between variables and is useful for predicting the change in the outcome variable based on the subsequent change in one or more outcome (or explanatory) variable(s) (Kirk, 2008). We used two types of regression: linear and logistical. Linear regression, such as Ordinary Least Squares, may be applied when the data have a linear relationship and the outcome variable is continuous, such as grade point average. Logistic regression may be used in predictive modeling with dichotomous outcomes, such as retained or not retained (Dalgaard, 2008). Both procedures identify the relationship between the outcomes and the explanatory variable when all other variables are held static. Two-Step Cluster analysis is a statistical method used to form categories within large sets of data by maximizing between group differences and minimizing within group differences (Shih, Jheng, and Lai, 2010). Two-Step Cluster analysis utilizes continuous and categorical variables and provide useful exploratory solutions to categorizing data (Shih, Jheng, and Lai, 2010). One hundred twenty-nine variables were put into the dataset for this study. Some of these included factors that we know about the student academically at the outset: ACT composite score, high school percentile, number of hours completed through AP/IB before entering Baylor, etc. Some were factors related to contacts with Baylor before enrollment: whether or not the student attended Premiere, Line Camp, Orientation, etc. Some were factors related to demographics and finances: distance from home, percentage of financial need met, ethnicity, first generation college student, gender, etc. Other factors were related to academic profile in the first year: fall GPA, major, SI session, the number of times the student changed majors, etc. Other factors were related to the social aspects of the student experience: number of extracurricular activities, residential hall, etc. See Appendix A1 for the entire list of variables considered.

11

II. Institutional Retention Analysis Results

A. Overall Logistic Regression for 2007 and 2008 Cohorts Results of the overall logistic regression model applied to the 2007 cohort:

Variable Effect on Retention

Significance Odds Ratio

Fall GPA Positive < .0001 3.036 Didn’t Request

Transcript Positive < .0001 3.721

Of the 129 variables entered into the regression model, only the fall grade point average and whether or not the student requested a transcript were significantly related to fall retention (See Appendix A1 for a listing of the variables utilized). The significance levels of p < .0001 indicate that these results could only occur by chance 1 out of 10,000 times, which is very stringent compared to the traditional cutoff of significance level in social sciences, p < 0.05. Interpreting the odds ratio is fairly straightforward for variables with well-defined levels, such as the Requested Transcript variable. The student either requested a transcript (trans = 1), or they did not (trans = 0). The odds ratio for the 2007 cohort indicates that students who did not request a transcript are 3.721 times more likely to retain than those who did. The interpretation of odds ratio for continuous variables, such as grade point average, is more difficult, but for this cohort, the results indicate that each unit increase in grade point average is associated with a student being 3.036 times as likely to retain. Effect plots visually illustrate the impact of an explanatory variable on an outcome variable and are useful when interpreting the effect of a continuous variable. These plots show us the predicted probability of student retention across the full range of the continuous variable of interest. In the first plot below, we examine the effect of fall grade point average on retention for the 2007 cohort, among those who did not request a transcript.

12

The general trend of the plot shows that for increasing grade point average, the student’s probability of retention increases, as we would expect. More specifically, a student who did not request a transcript and made a 3.0 grade point average in the fall would have about a 90% probability of retaining. If the student made a 2.0 grade point average, that student would only have around a 77% probability of retaining. The plot below again plots retention probability across fall grade point average for the 2007 cohort, this time for those students who did request a transcript.

13

We see lower retention probabilities on the whole, which can be attributed to the significantly negative effect that requesting a transcript has on retention. Here, a student who did request a transcript and made a 3.0 grade point average has only a 75% probability of retention. Results of the overall logistic regression model applied to the 2008 cohort:


Significance Odds Ratio

Fall GPA Positive <.0001 2.761 Didn’t Request Transcript Positive <.0001 4.352 Spring Course Difficulty Positive =.0003 - ‐ Not interpretable

Fall GPA and requesting a transcript were significant for the 2008 cohort;

however, spring course difficulty is also a significant predictor of retention. Spring course difficulty is calculated from the percentage of Ds and Fs historically earned in a course. An individual student’s measure is an accumulation of these percentages across his or her spring schedule. One unexpected result in our study revealed that the more difficult the spring course schedule, the more likely a student in this cohort was to retain.

Here again we can examine the effect plots to better understand the effects of the continuous variables on retention. The first plot shows retention probability plotted across the range of grade point average for the 2008 cohort, this time controlling for both transcript and course difficulty. With course difficulty held constant, this graph illustrates the effect of GPA on retention for students who did not request a transcript.

14

These results are similar to what we saw in the 2007 cohort – a student who did not request a transcript, with average spring course difficulty, would have about a 90% chance of retaining if that student earned a 3.0 grade point average in the fall.

The next plot differs only in the transcript variable – these are 2008 freshmen who did request a transcript, and again we see that on the whole, retention probabilities are lower. The graph shows the effect of GPA on retention for students who requested a transcript with GPA held constant.

Here we see that a much higher fall grade point average would be required for a

student to have 90% probability of retention. Because spring course difficulty also arose as significant in the 2008 cohort, we

can model an effect plot for this continuous variable. While holding GPA constant, the following graph shows the effect of spring course difficulty on retention for students who did not request a transcript. We see that a student who did not request a transcript with a spring course difficulty of 0.75 (moderately difficult) would have about a 90% probability of retention.

15

While holding GPA constant, the following graph shows the effect of spring

course difficulty on retention for students who requested a transcript. Among the students who did request a transcript, those with the same spring course difficulty of 0.75 would have only around an 80% probability of retention.

16

B. Cluster Analysis for 2008 Cohort

1. Cluster Solution for Institutional Variables

Applying a Two-Step Cluster analysis to the 2008 freshman cohort revealed six distinct clusters of students with respect to fall retention when using institutional variables as input. The variables used to form the cluster included fall grade point average, percent of total financial need met, academic unit change, major change, request of a transcript, and number of extracurricular activities. The analysis excluded students who did not retain for the spring. Specific attributes related to each cluster may be found in the table located in Appendix A3. Fall GPA emerged as the most significant variable predicting fall retention in the Logistic Regression analysis of the 2008 cohort and this finding is supported by the Two-Step Cluster analysis. Cluster 1 is associated with the lowest level of fall retention and students in this cluster also have the lowest average fall GPA, 2.27. Conversely, cluster 4 is associated with the highest level of retention but students in this cluster do not necessarily have the highest average fall GPA suggesting that fall GPA may be a more accurate indicator of attrition when the GPA is low. Another explanation is that fall GPA may have a threshold point and once this point is crossed, retention does not increase with an increase in GPA. Requesting a transcript was the second most significant variable related to retention according to the Logistic Regression analysis. For cluster 1, the cluster with the lowest percentage of retention, 6.9% of students requested a transcript while no students in cluster 4, the cluster with the highest percentage of retention, requested a transcript. Students request transcripts for a variety of reasons, including needs not related to retention, but according to the results of the Logistic Regression equation and the Two-Step Cluster analysis, requesting a transcript may be a red flag indicating a student needs an intervention. Spring course difficulty also emerged as significant in the Logistic Regression analysis, but does not appear to differentiate between clusters in the Two-Step Cluster analysis. Cluster 1 has a spring course difficulty level of 2.91 and cluster 4 has a spring course difficulty of 2.94, a difference which is not statistically significant according to a paired t test. The non-significant difference between spring course difficulty at the cluster level does not diminish the significance of the variable for determining fall retention.

2. Cluster Analysis for Individual Variables

The Two-Step Cluster analysis using individual variables revealed six clusters with respect to fall retention. As in the institutional analysis, students who did not retain for the spring were not included in the analysis. The variables used in the individual analysis include high school percentile, SAT score, number of AP/IB hours, beginning hours upon entering Baylor, Baptist, number of self-initiated contacts, and a variable indicating whether or not the student attended an official campus visit during recruitment.

17

The clusters found in the individual solution delineate according to fall retention. Cluster 2 has the lowest fall retention at 0% and cluster 3, 4, and 5 have the highest retention rate at 100%. With the differences in retention reflected by the clusters, it is expected that the variables that most significantly predict retention in the Logistic Regression would have different values when comparing cluster 2 to the clusters with higher retention. Fall GPA emerges as the most significant predictor of retention in the Logistic Regression analysis. For cluster 2, the 0% retention cluster, the average fall GPA was 2.47. For the three clusters that retain at 100%, the average fall GPA is above 3.10. The difference between the fall GPA in cluster 2 and cluster 4 is statistically significant according to an explanatory samples t test, t (675) = -9.426, p < 0.001. This finding indicates that this difference would only occur by chance 1 out of 1000 opportunities and that the difference in GPA is most likely tied to cluster membership. Thus, from both analyses, fall GPA appears to be a very strong indication of likelihood to retain the following fall. Requesting a transcript was also a significant predictor of retention in the Logistic Regression analysis. For the individual cluster solution, 36.5% of students in cluster 2, which had 0% retention, requested a transcript while 11% or less of students in the 100% retained clusters requested a transcript. A chi square analysis of the difference between cluster 2 and cluster 4 indicates requesting a transcript is associated with specific cluster membership, χ2(6) = 170.901, p < 0.001. The significance level of this finding suggests that this difference would only occur by chance 1 out of 100 times. Spring course difficulty predicted retention according to the Logistic Regression analysis, but, as in the institutional Cluster analysis solution, it does not appear to differentiate between clusters. Cluster 2 has a spring course difficulty of 3.05 indicating the least difficult course load of all the clusters, but the most difficult course load, found in cluster 5, is only 2.95. It is possible that the course difficulty measured as a GPA may not differentiate well between clusters because the variation of course difficulty is small. For example, the descriptive analysis of course difficulty reveals that 68% of the values lie between 2.76 and 3.22, indicating that the majority of freshman students have a course load in this range. In addition, the lowest spring course difficulty indicated in the data set was 2.09 while the highest spring course difficulty was 3.67. These values do not allow much room for variation at the high end of the spectrum.

3. Institutional by Individual Cluster Cross-analysis It might be of particular interest to examine the cross-clusters, or intersections of individual and institutional clusters. The descriptive information related to the cross-clusters may be found in Appendix A3. If we look specifically at the cross-clusters in which no student was retained (highlighted in the table in Appendix A3) we see that the 2X2 cluster was a bit of an anomaly – these 35 students had a high average SAT, high school percentile, AP/IB hours, and fall GPA; and yet none of them retained. In the 1X2 cluster, however, we find 72 students who scored much lower in these areas (including having an average fall GPA of 1.77) and did not retain. Students representing the intersection of Institution cluster 1 and Individual cluster 2 may be an ideal sample to target for an intervention.

18

The cross-clusters with zero fall retention represent 172 students, which is approximately 5.6% of the 2008 freshman cohort. While the cross-clusters appear to be slightly different with respect to most variables, they do share some similarities which could be used to direct intervention efforts. With the exception of the 2X2 cluster, all other zero retention cross-clusters either have a high school percentile below 80% or a total number of AP/IB hours below 2.0. The SAT scores among the cross-clusters differ but the clusters with a lower high school percentile, an SAT score in the 1100’s, and a total number of AP/IB hours below 2.0 seem to be highly represented among the zero retention cross-clusters.

C. Overall Institutional Recommendations Analysis for both the 2007 and 2008 cohorts revealed a relatively small number of significant variables for retention, and those variables that arose as significant were very similar for the two cohorts. This suggests that the fall grade point average and the action of requesting a transcript are highly important in determining whether a student will retain to sophomore year. These variables also differentiated well among the cluster. For example, clusters with low retention rates requested a transcript at a higher rate and had lower grade point averages when compared with a cluster with high retention rates. Then, the next point of interest is what the institution might do with this information. At the end of the fall semester, the institution will obtain students’ fall grade point averages. Based on this analysis, we know that freshman with lower grade point averages tend to retain at a lower rate, so the institution may wish to target any freshman with a grade point average below a certain cutoff for special intervention – perhaps offering tutoring services, having students work with academic deans, and/or mentoring. The predicted probability plots could even be helpful in this regard. We see similar general trends across cohorts. For students not requesting a transcript, those earning below a 2.5 have predicted retention probabilities dropping below 80%. This indicates that students achieving less than a 2.5 GPA may be in need of intervention services even if they have not requested a transcript. The same trend is found in the cluster solution. The action of requesting a transcript during the freshman year seems to be highly indicative of lower retention rates. Thus the institution may decide to implement a new type of intervention for students requesting an official transcript. Perhaps the student would need to meet with a Baylor staff member for a brief discussion over the transcript request before being authorized to receive the transcript. According to Tinto’s Interactionalist Theory (1993), a meeting with a Baylor staff member may serve to reinforce institutional commitment and promote academic integration for a student requesting a transcript. Since the probability plots indicate that GPA and requesting a transcript appear to jointly affect retention, then perhaps the two variables should be considered in tandem when meeting with students who have requested a transcript. In the cluster solution, students in clusters with lower grade point averages also requested a transcript, a finding that supports the combination of these two variables as indicators of retention risk. Both findings support the Interactionalist Theory (1993) by indicating that academic and social integration of a student may have an impact on retention. Finally, the spring course difficulty came up as significant to retention for the 2008 cohort under both analyses. The university may also consider this variable in

19

determining a student’s need for intervention. In the same way that fall grade point average can be evaluated for whether or not a student needs special attention, those students with less difficult course loads might be targeted. A less difficult course load may be a symptom of a larger problem, such as low academic self-confidence or lack of preparation for the major. Again, the predicted probability plots could assist in relating course difficulty to probability of retention.

20

III. Retention Analysis on Groups of Interest and Major Groupings

A. Logistic regression using all variables (groups of interest):

1. Biology majors a. 2007 Biology

85.37% retained (321 out of 376)


Significance Standardized Betas

Odds Ratio

Spring course difficulty Positive <.0001 0.44 -

SAT score Positive =.0160 0.30 1.005 ‐ Not interpretable

The retention rate of Biology majors closely parallels the retention rate for the freshman cohort. Spring course difficulty was significantly related to retention among Biology majors, with the odds ratio indicating that a more difficult course of study in the spring is related to higher retention. The SAT score also significantly predicts retention among Biology majors. For every 100 point increase in the SAT score, the odds of retention increase by 50%. Spring course difficulty has a larger standardized beta than SAT score, which means spring course difficulty affects retention more than SAT score.

b. 2008 Biology

81.03% retained (376 out of 464) Variable Effect on

Retention Significance Standardized

Betas Odds Ratio


Initial rate Positive =.0024 0.28 - Number of extracurricular

activities Positive =.0041 0.38 1.868

First generation college student Negative =.0165 -0.19 0.417 Percent hours complete in fall Positive =.0207 0.25 -

- Not interpretable Biology majors in the 2008 cohort had a lower retention rate than the 2007 cohort. Spring course difficulty was the most significant predictive variable and explained the greatest amount of variance in retention. The initial rate of contact was also significant, but the odds ratio and direction of prediction cannot be determined due to the format of the variable. Other significant factors include number of extracurricular activities, whether the student is a first generation college student, and percent of attempted hours

21

completed in the fall. Just as in 2008, spring course difficulty had the largest effect on retention (.58), followed by the number of extracurricular activities (.38).

c. Recommendations for Biology Majors

Decision-makers in Biology may wish to consider capping and managing enrollment through an admissions process. They also might consider several of these variables in determining those students who may need special attention in order to enhance those students’ chances of retaining. For example, we see that first generation college students may need special intervention for success. Attending orientation and other first year college experiences designed to familiarize students with the university may be more important for first generation college students than for a typical student and, if a first generation student was not able to participate in these experiences, then perhaps that student may require additional mentoring and intervention from staff directly involved in the student’s program of study (ACT, 2004). Course difficulty and number of extracurricular activities could also be examined. If a student has signed up for a relatively light spring schedule, and we see that the student is not getting involved on campus, the department may wish to target this student for intervention. National research indicates that academic and social integration have an impact on student retention (Tinto, 1993); this may be particularly important for students in such a large major.

2. Engineering and Computer Science Majors

a. 2007 ECS



Beta Odds Ratio

Request transcript Negative <.0001 -0.65 0.023

Percent hours complete in fall semester

Positive =.0038 0.50 -

Received probation or suspension in the fall

Negative =.016 -0.32 0.223

Number of extracurricular activities

Positive =.020 0.56 4.541

- Not interpretable The most significant predictor of retention for the 2007 ECS cohort is requesting a transcript. This variable has a negative impact on retention and accounts for 0.65 standard deviations of change in the fall retention variable. Receiving probation or suspension in the fall also has a negative impact on retention for this major grouping. Both percent hours completed in the fall and the number of extracurricular activities have a positive impact on fall retention and account for approximately 0.50 standard deviations of change in the retention variable.

22

b. 2008 ECS



Beta Odds Ratio

Spring course difficulty Positive =.0003 0.41 -


Negative =.0010 -0.35 0.200


Positive =.0010 0.21 -

- Not interpretable The 2008 ECS cohort retained at higher rate than the 2007 cohort. Spring course difficulty emerged as the most significant predictor of retention for this group. Receiving a probation or suspension had a negative impact on retention and ECS students who received a probation or suspension in the fall were 20% less likely to retain than those who did not receive probation or suspension. The percent hours completed in the fall had a positive impact on fall retention.

c. Recommendations for ECS Majors

Decision-makers in ECS may wish to consider capping and managing enrollment through an admissions process. The transcript variable came up as significant for ECS majors, just as it did in the overall institutional analysis. We might make the same recommendation here: that students requesting a transcript would need to go through some processing with a Baylor staff member before receiving the official transcript, in order to determine the reasons for the request and for the staff member to provide some intervention if possible. The need for increased staff involvement at this point is supported by literature (Tinto, 1993). Those students signing up for a light spring course load, as well as those not getting involved on campus, may need to be targeted for intervention. ECS students receiving a probation or suspension in the fall should also receive special attention.

23

3. Premed Students

a. 2007 Premed



Betas Odds Ratio

Spring course difficulty Positive <.0001 0.49 - Request transcript Negative =.0005 -0.22 0.283

Received a deficiency Positive =.0019 0.23 0.405

SAT score Positive =.0100 0.24 1.003


Positive =.0200 0.18 -

- Not interpretable Premed students in 2007 retained at approximately 85% and spring course difficulty is again the most significant positive predictor of retention, with a standardized beta of 0.49. Requesting a transcript has a negative impact on retention and has a standardized beta of 0.22, meaning that a 1 standard deviation increase in requesting transcripts will lead to a 0.22 standard deviation decrease in retention. Receiving a deficiency, SAT score, and the percent of hours completed in the fall semester all have significant positive impacts on retention.

b. 2008 Premed 81.71% retained (670 out of 820)


Significance Standardized Betas

Odds Ratio

Spring course difficulty Positive <.0001 0.42 - Request transcript Negative <.0001 -0.25 0.271


Positive =.0003 0.27 -


Negative =.0007 -0.19 0.728

Attended Line Camp Positive =.0025 0.23 2.492


Positive =.0210 0.19 1.374

- Not interpretable The 2008 cohort of premed students retained at approximately 82%. Spring course difficulty was the most significant positive predictor, followed by the percent of hours completed in the fall (.27). The odds ratios for these two variables are not interpretable because the variables are based on rates or percentages. Requesting a

24

transcript was the third most important predictor in the model, with a negative effect on retention (.25). Students who requested a transcript and were declared premed were 27% more likely to retain than premed students who did not request a transcript.

c. Recommendations for Premed Students Decision-makers in Premed wish to consider capping and managing enrollment through an admissions process. Premed students who register for a light spring course load and/or do not get involved in extracurricular activities may need to be targeted for intervention to improve their chances of retaining. The transcript variable came up as significant for premed students, just as it did in the overall institutional analysis. We might make the same recommendation here: that students requesting a transcript would need to go through some processing with a Baylor staff member before receiving the official transcript, in order to determine the reasons for the request and for the staff member to provide some intervention if possible. As we might expect, premed students receiving a deficiency or probation/suspension in the fall would need extra attention in order to improve retention probability.

4. STEM Majors (Applied Mathematics, Biochemistry, Bioinformatics, Biology, Chemistry, Computer Science, Earth Science, Electrical & Computer Engineering, Engineering, Environmental Science, Environmental Studies, Exercise Physiology, Geology, Geophysics, Mathematics, Mechanical Engineering, Neuroscience, Physics, Statistics)

a. 2007 STEM



Significance Standardized Beta

Odds Ratio


Request transcript Negative <.0004 -0.22 0.263 Percent hours complete in fall

semester Positive =.0018 0.26 -


Positive =.0120 0.20 1.539

Credit by exam Math 1321

Positive =.0398 0.24 3.157

- Not interpretable Spring course difficulty is again the most significant, positive predictor of retention for the 2007 STEM cohort. Percent of hours completed in the fall, number of extracurricular activities, and credit by exam for Math 1321 all have a positive impact on retention as well. Requesting a transcript has a negative effect on retention with a standardized beta of 0.22.

25

b. 2008 STEM




Odds Ratio



Positive =.0002 0.25 -

Percent need met by scholarship Positive =.0022 0.22 -

Received probation or suspension in the spring

Negative =.0207 -0.14 0.501

Beginning cumulative hours Positive =.0379 0.15 1.025

Percent need met by financial aid

Negative =.0493 -0.14 -

- Not interpretable For the 2008 cohort, the percent of hours completed in the fall has the most significant positive impact on retention (.25). Requesting a transcript has a negative impact on retention, similar to the finding for the 2007 cohort. STEM students requesting a transcript were 31% less likely to retain than STEM students not requesting a transcript. Receiving probation or suspension also has a negative impact on retention for the 2008 cohort with students receiving probation or suspension being 50% less likely to retain than those who did not within this major grouping. The percent of financial need met and the percent of financial need met by scholarship are also significant predictors of retention for this cohort, a finding that differs from the previous cohort.

c. Recommendations for STEM Majors

The transcript variable came up as significant for STEM majors, just as it did in the overall institutional analysis. We might make the same recommendation here: that students requesting a transcript would need to go through some processing with a Baylor staff member before receiving the official transcript, in order to determine the reasons for the request and for the staff member to provide some intervention if possible. Those students signing up for a light spring course load, as well as those not getting involved on campus, may need to be targeted for intervention. We also see for the 2008 cohort that scholarship funding may play an important role in retention for STEM majors; this may warrant further study. Additionally, STEM programs may examine the student’s mathematical preparation, in the form of receiving credit by exam for Calculus I, as a guideline for placement decisions or for intervention during the student’s first year. Decision-makers in STEM areas may wish to consider capping and managing enrollment through an admissions process specifically based on Math preparation. Tinto’s

26

Interactionalist Theory (1993) illustrates the interaction between high school preparation and goal commitment, with students who enter programs without appropriate preparation showing significantly lower levels of goal commitment.

5. Undecided Students a. 2007 Undecided



Beta Odds Ratio


Positive <.0001 0.52 -

Request transcript Negative =.0010 -0.29 0.088 Spring course difficulty Positive =.0037 0.39 -

- Not interpretable For the 2007 Undecided cohort, the percent of hours completed in the fall semester is the most significant predictor of fall retention (0.52), followed by spring course difficulty (.39). As in the previous major grouping analyses, requesting a transcript has a negative impact on retention.

b. 2008 Undecided



Beta Odds Ratio

Received probation or suspension in the spring

Negative <.0001 -0.49 0.055

Request transcript Negative =.0004 -0.39 0.128 Spring course difficulty Positive =.0010 0.43 -

- Not interpretable Receiving a probation or suspension has the strongest impact on retention for the 2008 cohort (.49), followed by spring course difficulty (.43). Requesting a transcript also had a negative impact on retention (.39).

c. Recommendations for Undecided Students

The analysis for Undecided students seems to reflect generally the analysis for the overall freshmen populations in 2007 and 2008. This population can certainly benefit greatly from intrusive advising. We recommend that undecided students requesting a transcript go through a process with a Baylor staff member before the transcript can be

27

received. This may allow for an extra intervention. Undecided students who register for relatively light course loads in the spring should also be targeted for intervention in order to attempt to raise their likelihood of retaining.

B. Logistic Regression Using Admissions Variables (major groupings)

In order to analyze how various majors impact fall-to-fall freshmen retention at

Baylor, this study grouped majors that have some similarities. In reading these findings, one should keep in mind that freshmen who have declared certain majors may in fact not have very much contact with the related department. For the major groupings with a high risk of attrition, it might be that students who self-select into these majors need more intervention (rather than that the academic program is not supporting the students). Alternatively, these departments might consider creating admissions standards so that students who get in to the major have a higher chance of success.

1. Biology

a. Findings for Biology 2008

81.03% Retention (376 out of 464) Significant Academic Predictors of Retention:

• SAT Math: For every 100 point increase in the SAT Math score, the odds of the student retaining increase by 60%. For instance, the odds of a student who has scored a 640 on the Math portion of the SAT are 60% higher than the odds of a student who has scored 540 on the Math portion of the SAT. The standardized regression coefficient for SAT Math is 0.27.

• High School Percentile: For every 10% increase in the student’s high school percentile, the odds of the student retaining increase by 10.8%. For instance, the odds of a student from the 85th percentile retaining are 10.8% higher than the odds of a student from the 75th percentile retaining. The standardized regression coefficient for high school percentile is 0.13.

• AP English: If the student took the AP English exam before college, the odds of retaining increase by 91%. The standardized regression coefficient for AP English exam is 0.18.

b. Recommendations for Biology (admissions variables only)

As administrators in Biology plan to manage enrollment, they may wish to

consider some of these factors either for entrance into the Biology major or as prerequisites into gateway courses. They should also give these variables increased consideration when identifying students in their major who may be at risk for not retaining. SAT Math is the most important variable for retention (0.26), followed by AP English (0.17) and high school percentile (0.13). High school preparation appears to be an important factor related to success for Biology majors.

28

2. Chemistry, Hard Sciences, and Math (Biochemistry, Chemistry, Clinical Laboratory Science, Astronomy, Astrophysics, Aviation Science, Earth Science, Environmental Health Science, Environmental Science, Environmental Studies, Forestry, Geology, Geophysics, Physics, Applied Mathematics, Mathematics, Statistics) a. Findings for Chemistry, Math and Hard Sciences 2008


• AP Math: If the student took the AP Math exam before college, the odds of retaining decrease by 69.6%. The standardized regression coefficient for AP Chemistry is -0.30.

b. Recommendations for Chemistry, Math and Hard Sciences (admissions

variables only)

Even though the odds of predicted retention supposedly decrease when students have taken an AP Math class, this factor should not play a role in future policies aimed at increasing retention. Note: The regression model for Chemistry, Math, and Hard sciences did not include the variable for AP Biology, because its presence created problems for the Maximum Likelihood Estimate and the model fit. Please see the Technical Note at the end of this section for more information.

3. Communications (Communication Specialist, Film and Digital Media, Journalism, Speech Communication)

a. Findings for Communications 2008


• High School Percentile: For every 10% increase in high school percentile, the odds of retention increase by 33%. For instance, the odds of a student from the 85th percentile retaining are 33% higher than the odds of a student from the 75th percentile retaining. The standard coefficient for high school percentile is 0.31.

b. Recommendations for Communications (admissions variables only)

Administrators in Communications should consider high school percentile as an

important variable for predicting whether freshmen in their major will retain. Note: The regression model for Communications did not include the variables for AP Biology and AP Chemistry, because their presence created problems for the Maximum

29

Likelihood Estimate and the model fit. Please see the Technical Note at the end of this section for more information.

4. Education (Art All Level, Computer Science 8-12, Early Childhood-4 Generalist, Language Arts 4-8, Language Arts 8-12, Mathematics 4-8, Mathematics 8-12, Middle Grades Science, Physical Education All Level, Secondary Life Sciences, Social Studies 8-12, Special Education All Level)

a. Findings for Education 2008


• High School Percentile: For every 10% increase in the student’s high school percentile, the odds of the student retaining increase by 40%. For instance, the odds of a student from the 85th percentile retaining are 40% higher than the odds of a student from the 75th percentile retaining. The standardized regression coefficient for high school percentile is 0.42.

b. Recommendations for Education (admissions variables only)

Administrators in the School of Education may want to take the student’s high

school percentile into consideration when making admissions or intervention decisions, as this stands out as the key indicator of whether an Education student will be successful. Note: The regression model for Education did not include the variable for AP Biology and AP Chemistry, because their presence created problems for the Maximum Likelihood Estimate and the model fit. Please see the Technical Note at the end of this section for more information.

5. Engineering and Computer Science (Bioinformatics, Computer Science, Engineering, Mechanical Engineering, Electrical and Computer Engineering)

a. Findings for ECS 2008


• SAT Math: For every 100 point increase in the SAT Math score, the odds of the student retaining increase by 80%. The standardized regression coefficient for SAT Math is 0.30.

• High School Percentile: For every 10% increase in the student’s high school percentile, the odds of the student retaining increase by 23%. For instance, the odds of a student from the 85th percentile retaining are 23% higher than the odds of a student from the 75th percentile retaining. The standardized regression coefficient for high school percentile is 0.19.

30

b. Recommendations for ECS Majors (admissions variables only)

As administrators in ECS plan to manage enrollment, they may wish to consider some of these factors either for entrance into the ECS majors or as prerequisites into gateway courses. Administrators in Engineering and Computer Science should consider these variables when identifying students in their major who may be at risk for not retaining. SAT Math is the most important variable for retention (0.30), followed by high school percentile (0.19). A low SAT Math score and/or a low class rank may indicate that a student entering the program may need special intervention in order to succeed.

6. Family Consumer Sciences and Fine Arts (Child and Family Studies, General FCS, Fashion Merchandising, Architecture, Art History, Fashion Design, Interior Design, Studio Art, Theater Arts, Theater Design, Theater Performance)

a. Findings for Family Consumer Sciences and Fine Arts 2008


• High School Percentile: For every 10 percentage point increases in high school percentage, the odds of the student increase by 47%. For instance, the odds of a student from the 85th percentile retaining are 47% higher than the odds of a student from the 75th percentile retaining. The standardized estimate is 0.43.

b. Recommendations for Family Consumer Sciences and Fine Arts

(admissions variables only)

Administrators in Family Consumer Sciences and Fine Arts should view high school percentage as the most important variable for determining whether incoming students are at risk for not retaining.

7. Health-related (Clinical Laboratory Science, Nutrition Sciences, Medical Humanities, Communication Sciences and Disorders, Athletic Training, Community Health, Exercise Physiology, General Studies in HHPR, Health Sciences Studies, Recreation)

a. Findings for Health-related 2008


• AP English: If the student was enrolled in an AP English class during high school, the odds of retaining increase by 5.6 times. The standardized estimate is 0.47.

31

b. Recommendations for Health-Related (admissions variables only)

Administrators in Health-related programs may want to know whether the student was enrolled in an AP English class in high school to predict whether students in the health related majors are likely to retain.

8. Honors College (Great Texts, University Scholars)

a. Findings for Honors College 2008


• No academic predictors came up as significant. The variable that came closest to being significant was the high school percentile (p=0.08), with a standardized regression coefficient of 0.70.

b. Recommendations for Honors College (admissions variables only)

The only possible recommendation might be to target Honors students with lower high school percentiles as students who may potentially be at risk for not retaining.

9. Humanities (English, Professional Writing, Linguistics, History, Sociology, Anthropology, Museum Studies, Religion, Philosophy, Biblical and Related Languages, Classics, French, German, Greek, Language and Linguistics, Latin, Russian, Spanish)

a. Findings for Humanities 2008


• SAT Verbal: For every 100 point increase in the SAT verbal score, the odds of retaining decrease by 8%. The standardized estimate is -0.36.

b. Recommendations for Humanities (admissions variables only)

The SAT verbal score appears to have a slight negative effect on retention, but

this finding should not lead to any policy changes. This study should be run on other cohorts. Administrators should also refer to recommendations for the overall freshmen cohorts (pp. 18-9).

32

10. Music (Applied Music, Choral Music, Church Music, Instrumental Music, Music, Pedagogy, Composition)

a. Findings for Music 2008


• High School Percentile: For every 10% increase in the student’s high school percentile, the odds of the student retaining increase by 74%. For instance, the odds of a student from the 85th percentile retaining are 74% higher than the odds of a student from the 75th percentile retaining. The standardized regression coefficient for high school percentile is 0.54. This is the only variable that came up as significant to retention for music majors.

b. Recommendations for Music (admissions variables only)

Administrators in the Music department may want to take the student’s high school percentile into consideration when making admissions or intervention decisions, as this stands out as the key indicator of whether a Music student will retain. Note: The regression model for Music did not include the variables for AP Biology and AP Chemistry, because their presence created problems for the Maximum Likelihood Estimate and the model fit. Please see the Technical Note at the end of this section for more discussion.

11. Nursing (Pre-Nursing)

a. Findings for Nursing 2008 75.24% Retention (79 out of 105) Significant Academic Predictors of Retention:

• High School Percentile: For every 10% increase in the student’s high school percentile, the odds of the student retaining increase by 66%. For instance, the odds of a student from the 85th percentile retaining are 66% higher than the odds of a student from the 75th percentile retaining. The standardized regression coefficient for high school percentile is 0.47. This is the only variable that came up as significant to retention for Nursing majors.

b. Recommendations for Nursing (admissions variables only)

Administrators in the Nursing program may want to take the student’s high school

percentile into consideration when making admissions or intervention decisions, as this stands out as the key indicator of whether a nursing student will retain. This program may need some special attention since it has the lowest retention rate of all the major

33

groupings; it is unique in that students transition to another campus starting in the junior year.

12. Political Science (Political Science, American Studies, Political Science, Geography, International Studies, Slavic and Eastern European Studies, Asian Studies)

a. Findings for Political Science 2008


• SAT Math: For every 100 point increase in the SAT Math score, the odds of retention increase by 13%. The standardized regression coefficient for SAT Math is 0.57.

• SAT Verbal: For every 100 point increase in the SAT verbal score, the odds of the student retaining decrease by 13%. The standardized regression coefficient for SAT verbal is -0.56.

b. Recommendations for Political Science (admissions variables only)

Administrators in Political Science should give these variables increased

consideration when identifying students in their major who may be at risk for not retaining. SAT Math and SAT Verbal have about the same magnitude of predicting retention (.57 and -.56), although they predict opposite effects. Even though SAT verbal has a statistically negative effect on retention, this does not mean that the results should influence policy. This study should be run on other cohorts. Administrators should also refer to recommendations for the overall freshmen cohorts (pp. 18-9). Note: The regression model for Political Science did not include the variable for AP Chemistry or AP Math, because their presence created problems for the Maximum Likelihood Estimate and the model fit. Please see the Technical Note at the end of this section for more discussion.

34

13. Pre-Business (Pre-Business, Baylor Business Fellows, Economics)

a. Findings for Pre-Business 2008 86.34% Retention (550 out of 637) Significant Academic Predictors of Retention:

• SAT Math: For every 100 point increase in the SAT Math score, the odds of the student retaining increase by 40%. The standardized regression coefficient for SAT Math is .17.

• AP Math: If the student was enrolled in an AP Math class during high school, the odds of retaining increase by 4.6 times. The standardized estimate is 0.30.

b. Recommendations for Pre-Business (admissions variables only)

As administrators in the Business school plan to manage enrollment, they may

wish to consider some of these factors either for entrance into the business school or as prerequisites into gateway courses. Math is obviously very important for the retention of Pre-Business majors. The standardized estimates demonstrate that being enrolled in an AP Math class during high school is a more important predictor of retention for Pre-Business majors than their SAT Math score.

14. Psychology and Social Work (Psychology, Neuroscience, Social Work)

a. Findings for Psychology and Social Work 2008 81.31% Retention (161 out of 198) Significant Academic Predictors of Retention:

• AP Math: If the student was enrolled in an AP Math class during high school, the odds of retaining decrease by 77.2%. The standardized estimate is -0.29.

b. Recommendations for Psychology and Social Work (admissions variables

only)

Even though there is a negative relationship between taking an AP Math class and retention, Administrators in Psychology and Social Work should not change policy based on this finding. Administrators should refer to recommendations for the overall freshmen cohorts (pp. 18-9).

35

15. Undecided

a. Findings for Undecided 2008 80.10% Retention (209 out of 261) Significant Academic Predictors of Retention:

• No academic predictors came up as significant. This may indicate that Undecided students vary greatly in their characteristics and reasons for retaining (or leaving).

b. Recommendations for Undecided (admissions variables only)

Please refer to the separate analysis on Undecided students.

36

IV. Freshman Gateway Courses

A. Logistic Regression Using All Variables (selected gateway courses)

1. Biology 1305 a. Findings for BIO 1305 2007

2007 Biology 1305



Odds Ratio


Request transcript Negative =.0031 -0.23 0.247 Biology grade Positive =.0038 0.35 1.399

Total contacts with college during admissions

Positive =.0251 0.23 1.131

Academic Index Negative =.0450 -0.25 0.974 Deficiency Negative =.0497 -0.23 0.473

- Not interpretable

b. Findings for BIO 1305 2008 2008 Biology 1305



Odds Ratio


Request transcript Negative =.0031 -0.21 0.328 Percent hours complete in fall Positive =.0044 0.23 -


Positive =.0120 0.22 1.432

Biology grade Positive =.0305 0.19 1.196 - Not interpretable

c. Recommendations for Biology 1305

Among students who are enrolled in Biology 1305 during their freshman year, variables such as their grade in Biology 1305, the difficulty of their courses, and whether they requested a transcript were significant predictors of retention for both 2007 and 2008. Administrators who are concerned about the retention of students who take Biology 1305 should concentrate on these variables.

37

2. English 1302

a. Findings for ENG 1302 2007 2007 English 1302



Odds Ratio


Request transcript Negative <.0001 -0.20 0.266 Percent hours complete in fall

semester Positive <.0001 0.24 -

Probation or suspension in spring semester

Negative =.0021 -0.15 0.430

High school rate Positive =.0037 0.12 N/A


Negative =.0410 .11 1.311

- Not interpretable

b. Findings for ENG 1302 2008 2008 English 1302



Odds Ratio


Request transcript Negative <.0001 -0.24 0.252 Probation or suspension in

spring semester Negative =.0001 -0.15 0.561


Negative =.0004 -0.18 -

Academic unit change Negative =.0056 -0.12 0.559

Percent need met by scholarship

Positive =.0170 0.10 -

Total self-initiated contacts Negative =.0375 -0.25 0.833

- Not interpretable

38

c. Recommendations for English 1302

Among freshman in English 1302, spring course difficulty, whether they had a probation or suspension, and whether they requested a transcript were predictors for both 2007 and 2008 of whether they would retain. These also proved to be some of the most important variables based on the strength of their standardized regression coefficients, and so these variables should be taken into account by administrators seeking to improve the retention rate among students in English 1302.

3. Math 1304 a. Findings for MTH 1304 2007

2007 MTH 1304



Odds Ratio



Positive <.0001 0.36 -

Probation or suspension in fall semester

Negative <.0001 -0.29 0.230



Positive =.0046 0.23 1.737

Beginning earned hours Positive =0.0291 0.16 1.038

- Not interpretable

b. Findings for MTH 1304 2008

2008 Math 1304 Variable Effect on


Beta Odds Ratio


Positive <.0001 0.31 -

Spring course difficulty Positive <.0001 0.29 - Request transcript Negative =0.002 -0.21 0.292

Age Negative =0.0119 -0.15 0.517 Application span Positive =0.0284 0.13 1.152

- Not interpretable

39

c. Recommendations for Math 1304

Similar to English 1302 and Biology 1305, the variables of spring course difficulty, whether they requested a transcript, and if the student was on probation are all variables that are key to the retention of students in Math 1304. Administrators seeking to increase retention among students in Math 1304 should take these key variables into consideration.

4. Math 1321

a. Findings for MTH 1321 2007

2007 MTH 1321



Odds Ratio


Request transcript Negative =.0004 -0.22 0.247 Percent hours complete in fall

semester Positive =.0062 0.20 -

Probation or suspension in spring semester

Negative =.0115 -0.17 0.247

- Not interpretable

b. Findings for MTH 1321 2008

2008 Math 1321 Variable Effect on


Beta Odds Ratio


Request transcript Negative =.0002 -0.23 0.277 Probation or suspension in the

fall Negative =.0017 -0.19 0.308

- Not interpretable

c. Recommendations for Math 1321

Similar to English 1302 and Biology 1305, the variables of spring course difficulty, whether they requested a transcript, and if the student was on probation are all variables that are key to the retention of students in Math 1321. Administrators seeking to increase retention among students in Math 1321 should take these key variables into consideration.

40

5. Religion 1310

a. Findings for REL 1310 2007 2007 Religion 1310



Odds Ratio


Request transcript Negative <.0001 -0.19 0.287 Probation or suspension in

spring semester Negative <.0001 -0.17 0.913


Positive =.0004 0.15 -

Percent need met with scholarship

Positive =.0081 0.11 -

Total contacts with college during admissions

Positive =.0082 0.12 1.070

Deficiency Negative =.0103 -0.11 0.624

High school rate Positive =.0146 0.09 -

- Not interpretable

b. Findings for REL 1310 2008 2008 Religion 1310



Odds Ratio


Probation or suspension in fall semester

Negative <.0001 -0.18 0.354

Requested a transcript Negative <.0001 -0.25 0.228

Religion grade Positive =.0002 0.17 1.196

Fall course difficulty Negative =.0007 -0.16 0.032

Percent need met by scholarship

Positive =.0017 0.20 -


Positive =.0020 0.14 1.326


Positive =.0024 0.13 -

41

Academic unit change Negative =.0028 -0.11 0.562

- Not interpretable

c. Recommendations for Religion 1310

Similar to other gateway courses, the difficulty of the spring schedule, whether they requested a transcript, and whether they were on probation were all significant predictors of whether students in Religion 1310 will retain. In 2008, the students’ grades in religion were also a significant predictor of whether they would retain, with higher grades leading to a higher likelihood of retention. Administrators should take this into account when seeking to improve retention among students in Religion 1310.

B. Linear Regression Applied to Grade Received in Course (selected gateway courses) 1. Biology 1305

a. Significant Factors on Course Grade

2008 Biology 1305


Significance t Score

Standardized Estimate

High school percentile Positive <.0001 6.88 0.226

SAT verbal Negative <.0001 4.14 -0.158 SAT math Positive <.0001 7.50 0.288

AP exam in Biology 1305 Positive =.0098 2.59 0.085

42

b. Descriptive Information by Grade in Course 2008 Biology 1305 - Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours

Fall Course Difficulty

Spring Course Difficulty

AP/IB Hours

Fall GPA

0.0 76.28 1114.63 4.78 0.548 0.485 0.61 1.53 1.0 78.32 1135.57 3.39 0.570 0.485 0.47 2.04 2.0 83.27 1187.43 6.85 0.571 0.558 2.04 2.63 2.5 87.47 1208.60 6.40 0.567 0.554 1.28 2.93 3.0 85.86 1228.33 8.31 0.579 0.565 3.97 3.24 3.5 89.29 1255.48 11.50 0.578 0.582 4.45 3.48 4.0 92.35 1299.81 12.44 0.562 0.579 8.60 3.77

Descriptive information by grade achieved in Biology 1305 illustrates interesting trends among several variables. For the students in the 2008 cohort, as AP/IB hours and beginning cumulative hours when entering Baylor increase, so do grades achieved in Biology 1305. Higher SAT scores are also associated with higher grades in Biology 1305. The grade achieved in Biology is positively correlated with the fall GPA for students in this cohort, r = 0.994, p < .001, meaning that as the grade achieved increases so does the student’s overall fall GPA. A correlation of 1.0 between GPA and grade in course would indicate a perfect linear relationship, so the r = 0.994 indicates a strong correlation. A chi square analysis revealed that taking the AP/IB exam for Biology 1305 was associated with higher achievement in the course, χ2 (8)= 171.835, p < 0.001.

c. Recommendations

In 2007, the better freshmen did in Biology, the more likely they were to retain. The SAT Math score, whether they took an AP Biology class in high school, and their high school percentile were significant and positive predictors of how well students would perform in Biology 1305. Students who do not excel at these indicators may potentially need assistance in order to be successful in Biology 1305, and retain at Baylor University. This finding is supported by Tinto’s Interactionalist Theory (1993) which indicates that high school preparation impacts goal commitments and eventual retention.

43

2. Chemistry 1301


2008 Chemistry 1301





SAT math Positive <.0001 9.44 0.382 AP exam in Chemistry 1301 Positive =.0018 3.13 0.107

b. Descriptive Information by Grade in Course

2008 Chemistry 1301 - Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours



AP/IB Hours

Fall GPA

0.0 72.91 1146.48 4.74 0.516 0. 489 0.50 1.57 1.0 80.03 1168.72 7.10 0.516 0.486 1.92 2.09 2.0 84.49 1194.57 6.63 0.537 0.518 2.01 2.80 2.5 90.24 1193.91 8.35 0.513 0.514 2.04 2.96 3.0 86.20 1239.47 8.85 0.526 0.510 4.01 3.25 3.5 88.66 1255.38 11.77 0.539 0.550 6.23 3.45 4.0 93.87 1327.48 13.99 0.535 0.532 10.26 3.80

Descriptive information by grade achieved in Chemistry 1301 reveals that a higher SAT Math score and a higher high school percentile is associated with higher grade in the course. This trend is similar to the findings from the logistic regression.

c. Recommendations

Students with lower high school percentiles and SAT Math scores may not perform as well in Chemistry 1301, and may potentially need assistance in order to perform well in this class and retain for their sophomore year.

44

3. English 1302


2008 English 1302 Variable Effect on

Retention Significance t

Score Standardized

Estimate High school percentile Positive <.0001 7.92 0.201

SAT verbal Negative <.0001 5.15 -0.139 SAT math Positive <.0001 2.03 0.055


2008 English 1302 - Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours



AP/IB Hours

Fall GPA

0.0 69.41 1150.23 1.77 0.535 0.401 0.64 0.90 1.0 60.20 1124.17 2.17 0.522 0.470 0.00 1.15 2.0 68.05 1108.02 2.91 0.545 0.502 0.64 1.96 2.5 72.00 1097.35 2.78 0.541 0.463 1.56 1.96 3.0 74.38 1147.66 2.88 0.531 0.509 1.25 2.61 3.5 80.91 1158.50 2.89 0.540 0.512 1.4 2.85 4.0 84.93 1203.31 4.20 0.543 0.533 2.46 3.35

The descriptive information by grade in English 1302 mirrors the trends found in Biology 1305. Higher grades in English 1302 are associated with a higher high school percentile, a higher SAT score, more beginning hours, and more AP/IB hours. These variables are also associated with a higher fall GPA for the 2008 cohort. Taking the AP/IB exam in English was also associated with higher grades in the course explanatory of receiving credit, χ2 (9)= 70.731, p < 0.001. The performance in English 1302 is positively correlated to fall GPA, r = 0.969, p < .01.

c. Recommendations

The SAT Math score and high school percentile are predictors of how well freshmen will do in English 1302, and should be taken into consideration when attempting to improve students’ grades, or targeting students for tutoring or other forms of assistance before the semester begins. Research indicates that high school preparation has a significant impact on performance and eventual retention; however, increasing the academic involvement of students with lower preparation appears to be an effective intervention (Tinto, 1993).

45

4. Math 1304

a. Significant Factors on Course Grade 2008 Math 1304





SAT math Positive <.0001 9.54 0.351

b. Descriptive Information by Grade in Course 2008 Math 1304 – Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours



AP/IB Hours

Fall GPA

0.0 72.20 1129.86 3.98 0.542 0. 493 0.99 1.57 1.0 76.84 1136.00 5.90 0.546 0.487 1.11 1.94 2.0 78.78 1148.32 4.04 0.547 0.541 0.69 2.43 2.5 83.50 1152.14 4.78 0.546 0.536 0.43 2.80 3.0 80.66 1165.32 6.36 0.540 0.524 1.47 3.03 3.5 86.68 1169.62 4.85 0.545 0.507 1.96 3.40 4.0 88.60 1203.06 6.09 0.543 0.537 2.52 3.63

The descriptive information for Math 1304 supports the findings from the logistic

regression analysis. High school percentile is related to course grade as is the SAT score.

c. Recommendations

The SAT Math score and high school percentile are predictors of how well freshmen will do in Math 1304, and should be taken into consideration when attempting to improve student’s grades, or targeting students for tutoring or other forms of assistance before the semester begins. Departmental administrators might also consider creating a course for students who do not have a high likelihood of success in MTH 1304 but who need to take the course on their degree program.

46

5. Math 1321

a. Significant Factors on Course Grade 2008 Math 1321





SAT math Positive <.0001 7.50 0.288

b. Descriptive Information by Grade in Course 2008 Math 1321 - Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours



AP/IB Hours

Fall GPA

0.0 75.17 1184.66 5.28 0.543 0. 517 0.69 2.23 1.0 83.14 1208.08 5.30 0.538 0.541 1.63 2.57 2.0 82.66 1208.95 6.06 0.551 0.542 1.96 2.92 2.5 82.16 1213.88 7.43 0.543 0.512 2.96 3.06 3.0 87.33 1232.61 6.34 0.555 0.552 2.34 3.22 3.5 89.84 1248.08 8.37 0.543 0.541 3.03 3.43 4.0 91.73 1288.36 8.84 0.546 0.552 5.41 3.73

The means by grade achieved in Math 1321 reveal similar trends to the means from Biology 1305. For the students in the 2008 cohort, as AP/IB hours and beginning cumulative hours when entering Baylor increase, so do grades achieved in Math 1321. Higher SAT scores are also associated with higher grades in Math 1321. A chi square analysis revealed that taking the AP/IB exam for Math 1321 was associated with higher achievement in the course, χ2 (8)= 169.683, p < 0.001. The average fall GPA was significantly correlated to performance in Math 1321, r = 0.990, p < .001, meaning that as the grade achieved increases so does the student’s overall fall GPA. A correlation of 1.0 between GPA and grade in course would indicate a perfect linear relationship, so the r = 0.990 indicates a strong correlation.

c. Recommendations

The SAT Math score and high school percentile are predictors of how well freshmen will do in Math 1321, and should be taken into consideration when attempting to improve student’s grades, or targeting students for tutoring or other forms of assistance before the semester begins.

47

6. Religion 1310

a. Significant Factors on Course Grade 2008 Religion 1310





SAT verbal Negative <.0001 9.98 0.217 SAT math Positive =.0010 3.30 0.072


2008 Religion 1310 - Descriptive information (means) by grade in course Grade High

School Percentile

SAT Total

Beginning Hours



AP/IB Hours

Fall GPA

0.0 71.30 1141.48 3.01 0.533 0.404 0.40 0.87 1.0 71.45 1129.41 4.56 0.526 0.491 0.56 1.80 2.0 75.37 1134.47 4.82 0.536 0.503 0.86 2.31 2.5 77.07 1150.59 5.14 0.539 0.500 1.18 2.60 3.0 81.57 1184.85 6.75 0.542 0.529 2.20 3.02 3.5 85.47 1210.81 7.71 0.535 0.523 2.84 3.23 4.0 90.29 1269.14 11.57 0.542 0.539 6.08 3.63

Students taking Religion 1310 in the fall of 2008 followed the same trends as students in Biology 1305, Math 1321, and English 1302. As beginning hours and AP/IB hours increase, so does performance in Religion 1310. Fall GPA also appears to be correlated with the grade in Religion 1310, r = 0.996, p < .001. This indicates that as the grade in religion increases, so does the overall GPA of the student.

c. Recommendations

The SAT Math score and high school percentile are predictors of how well

freshmen will do in Religion 1310, and should be taken into consideration when attempting to improve student’s grades, or targeting students for tutoring or other forms of assistance before the semester begins. High school percentile proves to be the most important factor, because it has the largest standardized regression coefficient (.25).

48

Technical Notes The Difference Between Odds Ratios and Probabilities When regression analysis is used to predict a binary outcome (e.g. retain or not retain), the results must be interpreted using odds ratios. An odds ratio is the ratio that one group will retain to another group. If students from the 75th and 85th percentile retain at the same rate, then the odds ratio is 1:1. A 10% increase in the odds ratio is not the same as a 10% increase in the probability. A 10% increase in the probability of retention could mean that students from the 75th percentile retain 84% of the time, while students from the 85th percentile retain 94% of the time. A 10% increase in the odds ratio would mean that the new odds ratio is 1.1:1. Quasi-Complete Separation of Variables In some of the regression models there was quasi-complete separation of various explanatory variables, which brought the validity of the model into question. This quasi-complete separation can occur when one of the categories of the binary outcomes variable only comes from one response of the binary explanatory variable. For instance, if none of the students who retained took AP Biology in high school, then it would lead to quasi-complete separation of the binary explanatory variable. This phenomenon occurred several times in the regression analysis of our major groupings, and the problem was solved by removing the variables that were experiencing quasi-complete separation. None of these removed variables were significant, and their removal led to certainty of the validity of the model.

49

Appendix A1 Master Variable List

Variable Variable Name (in dataset) Set to Description Fall Academic Unit Acad_unit_fall Nominal

The student’s academic unit in the fall semester

Spring Academic Unit Acad_unit_spring Nominal

The student’s academic unit in the spring semester

Academic Index

ACADEMIC_INDEX Interval

The academic index of the student. Combination of SAT score and high school percentile

ACT Composite ACTC Interval ACT Composite score

ACT English ACTE Interval ACT English score

Highest ACT score acthigh Interval

The student’s highest score on the ACT

ACT Math ACTM Interval ACT Math score

ACT Reading ACTR Interval ACT Reading score

ACT Science ACTS Interval ACT Science score

Age Age Interval The student’s age Baylor legacy

alum Binary

1 indicates the student has a relative who has previously attended Baylor

AP and IB hours

Ap_ib_hrs Interval

Number of credit hours the student entered with, coming from AP and/or IB tests

Took AP exam for BIO 1305

Apexam_bio1305 Binary

1=the student submitted a score from the AP biology exam (even if credit for BIO 1305 was not received)

Took AP exam for CHE 1301

Apexam_chem1301 Binary

1=the student submitted a score from the AP chemistry exam (even if credit for CHE 1301 was not received)

Took AP exam for ENG 1302

Apexam_eng1302 Binary

1=the student submitted a score from either AP English exam (even if credit for ENG 1302 was not received)

Took AP exam for MTH 1321

Apexam_mth1321 Binary

1=the student submitted a score from the AP Calculus AB or AP Calculus BC exam (even if credit for MTH 1321 was not received)

50

Variable Variable Name (in dataset) Set to Description Application span

App_span Interval

Length of time between date of application and start of the fall semester

Area retention rate

Area_rate Interval

Percentage of students from 3-digit zip codes who have retained at Baylor (3 years worth of data)

Fall attempted hours Att_hrs_fall Interval

The number of hours the student enrolled in for the fall

Spring attempted hours Att_hrs_spr

The number of hours the student enrolled in for the spring

Change in academic unit

Au_change Binary

1=the student changed academic units from the fall to the spring; 0=the student did not change academic units

Baptist bapt Binary

1 - The inquiry is Baptist, 0 the inquiry is not Baptist

Beginning earned hours

BEG_CUMEARNED_HRS_UG Interval

Number of hours earned by student prior to fall semester of freshman year

BIC student Bic_flag Binary 1=the student is in BIC Biology major

Bio_major Binary

1=the student was listed as a biology major in the fall of freshman year

Number of campus visits CAMPUS_VISIT Interval

The number of times the inquiry visited Baylor.

Received credit by exam for BIO 1305 Cbe_bio1305 Binary

1=the student received credit by exam for Introductory Biology

Received credit by exam for CHE 1301 Cbe_chem1301 Binary

1=the student received credit by exam for Introductory Chemistry

Received credit by exam for ENG 1302 Cbe_eng1302 Binary

1=the student received credit by exam for English: Thinking & Writing

Received credit by exam for MTH 1321 Cbe_mth1321 Binary

1=the student received credit by exam for Calculus I

Course difficulty in fall (average GPA earned in courses) Cdgpafall Interval

A measure of the difficulty of the courses for which the student was enrolled in the fall. An average of the average gpa’s earned in those courses over the past 3 years.

Course difficulty in spring (average GPA earned in courses) cdgpaspr Interval

A measure of the difficulty of the courses for which the student was enrolled in the spring. An average of the average gpa’s earned in those courses over the past 3 years.

51

Variable Variable Name (in dataset) Set to Description Course difficulty in fall (percentage of D’s and F’s in courses) Crsdifffall Interval

A measure of the difficulty of the courses for which the student was enrolled in the fall. Based on past history of percentage of D’s and F’s in the class

Course difficulty in spring (percentage of D’s and F’s in courses) crsdiffspr Interval

A measure of the difficulty of the courses for which the student was enrolled in the spring. Based on past history of percentage of D’s and F’s in the class

Deficiency Indicator

defic Binary

1=the student received a deficiency in the fall; 0=the student did not receive any deficiencies

Number of deficiencies Deficnum Interval

Number of deficiencies the student received in the fall

Distance from home distance Interval

The distance from the student’s residence to Baylor.

Residence hall Dorm_code Nominal The student’s residence hall

Residence hall retention rate

Dorm_rate Interval

The three year rate of retention based on the residence hall the student lives in

ECS student

Ecs_flag Binary

1=the student is enrolled the engineering & computer science academic unit

Expected family contribution

efc Interval

The student’s expected family contribution (only calculated if the student submitted a FAFSA)

ELG indicator Elg_flag Binary

1=the student is in an engaged learning group

ERMS cell

Erms_cell Nominal

The erms (enrollment/retention management strategy) cell that the student was placed in (based on academic ability and financial need). A ‘U’ indicates a missing value for this variable.

Ethnicity

Ethnicity Nominal

The student’s ethnicity. A – Asian P – Pacific Islander H – Hispanic B – Black/African American C – Caucasian I – Native American N – Not specified O - Other

Extracurricular indicator extracurrflag Binary

1= the student participates in at least one extracurricular activity

52

Variable Variable Name (in dataset) Set to Description # of extracurricular activities extracurrnum Interval

The number of extracurricular activities the student participates in

Filed a FAFSA fafsa Binary 1=the student submitted a FAFSA Fall GPA at Baylor Fall_gpa Interval

The first year fall GPA of the student

First generation college student First_gen_coll_student Binary

1=the student is a first-generation college student

Baylor was first choice

Firstchoice Nominal

Y=the student indicated that Baylor was their first choice, N=the student indicated that Baylor was their second choice or below, U=the student did not supply that information

Foot-on-campus indicator FOC_Flag Binary

Y' indicates that the inquiry has been on campus

Gender Gender Binary Student’s gender Grade achieved in particular Baylor classes

Grade_ Nominal

The student’s grade in one of 16 different gateway courses. There are 16 total variables indicating student grade for each of the following courses (the student may have taken the course in the fall or spring of freshman year): BIO 1305 MTH 1304 (precal) MTH 1308 (precal for business students) MTH 1309 (calc for business students) MTH 1321 (calculus I) CHE 1301 EGR 1301 CSI 1430 REL 1310 ENG 1302 BUS 1301 PSY 1305 HIS 1305 HIS 2365 HIS 2366 SPA 1401 NOTE: if the student did not take that course during either semester, the variable is given a value of ‘DT’ for “didn’t take”

Grade achieved in study skills Grade_edc1200 Nominal

The student’s grade in the “College Reading and Study Skills” course.

53

Variable Variable Name (in dataset) Set to Description course If the student did not take this

class, the variable has a value of ‘DT’.

Grade achieved in study skills course

Grade_edp1101 Nominal

The student’s grade in one of several study skills-related courses. Courses include “Mind over Math”, “Managing Time”, “Taking Notes”, and “Developing Memory”. If the student did not take this class, the variable has a value of ‘DT’.

Household income Hh_income Interval

Household income of student based on data from Experian

High school percentile HIGH_SCHOOL_PERCENTILE Interval High school ranking of the student Honors student

Honors_flag Binary 1=the student is enrolled in the honors program

Enrolled in HP 1420

Hp1420_ind Binary

1= the student took HP 1420 (anatomy) in either the fall or spring

High school retention rate

Hsc_rate Interval

The three year rate of retention based on the high school the student attended.

Initial method of contact retention rate Init_rate Interval

The three year rate of retention based on the initial contact code

Attended line camp Linecamp Binary

Indicator of attendance of Baylor Line Camp. 1=Yes; 0=No

Living & Learning Center

Llc_code Nominal

The student’s living & learning center. If none, this is given a value of ‘NONE’ BR – Brooks Residential College ECS – Engineering and Computer Science HC – Honors Residential College GC – Global Community LLC LEAD – Leadership LLC OA – Outdoor Adventure LLC ROTC – AFROTC LLC

LLC indicator Llc_flag Binary

1=the student is enrolled in a Living & Learning Center

Changed Major from fall to spring Major_change Binary

1=the student changed majors from the fall to the spring; 0=the student did not change majors

Number of major changes

Major_change_num Interval

Number of times the student changed majors in the first semester

Major grouping Major_group Nominal Group that the student’s major falls

54

Variable Variable Name (in dataset) Set to Description in; see major groupings list

Major in the fall Major_p1_fall Nominal

Student’s major during the fall semester

Major in the spring Major_p1_spring Nominal

Student’s major during the spring semester

Merit indicator MERIT_IND Binary

Has the student received merit aid? 1=Yes; 0=No

Minority indicator Minority Binary

1 = the student is a minority, 0=the student is not a minority

Major retention rate Mjr_rate Interval

The three year rate of retention based on the major of the student

Attended orientation Orientation Binary

Indicator of attendance of Orientation. 1=Yes; 0=No

Percent financial need met Perc_met Interval Percentage of financial need met Percent need met with loans Perc_metl Interval

Percentage of financial need met with loans

Percent need met with scholarships Perc_mets Interval

Percentage of financial need met with scholarships

Percent need met with work study Perc_metw Interval

Percentage of financial need met with work study

Percent of hours completed in the fall Percent_hrs_comp_fall Interval

Percent of the total hours attempted in the fall that the student received credit for

PIDM PIDM ID PIDM Premed indicator Premed_flag Binary

1=the student has indicated that he/she is either pre-med or pre-dent

Attended premiere premiere Binary

Indicator of attendance at a Premiere. 1=Yes; 0=No

Provisional admit student Provisional_admit Binary 1=the student is a provisional admitProbations/ suspensions in the fall

Ps_fall Nominal

‘P’=the student received probation or suspended-reinstated for the sophomore fall semester; ‘S’=the student received suspension for the sophomore fall semester; ‘N’=the student did not receive a probation or suspension

Probations/ suspensions in the spring

Ps_spr Nominal

‘P’=the student received probation or suspended-reinstated for the freshman spring semester; ‘S’=the student received suspension for the freshman spring semester; ‘N’=the student did not receive a probation

55

Variable Variable Name (in dataset) Set to Description or suspension

HS rank relative to other Baylor freshmen Relative_HS_Rank Interval

The relative high school rank of students as compared to others within the freshman class.

Religion Religion_code Nominal The student’s religion

Retained to fall of sophomore year

Retained_fall Target

Indicator of retention from spring of freshman year to fall of sophomore year. 1=Retained; 0=Did not Retain

Retained to spring of freshman year

Retained_spring Target

Indicator of retention from fall of freshman year to spring of freshman year. 1=Retained; 0=Did not Retain

Highest SAT score sathigh Interval

The student’s highest score on the SAT

SAT Math score SATM Interval SAT Math score SAT or ACT score converted to SAT satmodel Interval

The highest score between the SAT and ACT in terms of SAT.

SAT total SATT Interval SAT total score

SAT verbal SATV Interval SAT Verbal score

Student Athlete indicator Scholar_athlete Binary 1=the student is a student athlete Number of self-initiated contacts by student to BU SELF_INIT_CNTCTS Interval

The Number of contacts made by the inquiry to Baylor

SI sessions in particular courses

SI_ interval

Indicates how many SI sessions the student attended for some of our gateway courses of interest. NOTE: Imputation will not be appropriate here. Missingness is tricky here as some courses only provided SI for certain sections. Fall 2007 freshmen: BIO 1305 (fall and spring) CHE 1301 (fall and spring) MTH 1321 (fall and spring) PSY 1305 (fall and spring) REL 1310 (spring) Fall 2008 freshmen: BIO 1305 (fall and spring) CHE 1301 (fall and spring)

56

Variable Variable Name (in dataset) Set to Description PSY 1305 (fall and spring)

STEM student

Stem_flag Binary

1=the student is a STEM major (science, technology, engineering, math)

Attended summer events

Summer_attend Nominal

Indicator for attendance at summer events. ORIENTATION=attended orientation only; LINECAMP=attended line camp only; BOTH=attended both orientation and line camp; NONE=attended neither program

From Texas Texas Binary

Indicator of student being from Texas. 1=Yes; 0=No

Total # contacts between the student and Baylor TOTAL_CONTACTS Interval

The total number of contacts between Baylor and the inquiry.

Total hours earned in first year Total_credit_hours Interval

Total number of hours the student has earned in their first year

Requested Transcript

trans Binary

1= the student requested a transcript; 0=the student did not request a transcript

Undecided indicator Undecided Binary

1=the student’s major is listed as undecided for the fall semester

Worked on campus Work_on_campus Binary 1=the student had a job on campus

57

Appendix A2 Logistic Regression Output

2007 Cohort: Overall Freshman Class Analysis

2008 Cohort: Overall Freshman Class Analysis

58

Appendix A3 Cluster Analysis Output

Individual Variables

2008 Cohort Frequencies and Means of Variables

Class 1 (501) Class 2 (296) Class 3 (389) Class 4 (480) Class 5 (221) Class 6 (560) Outlier class (487)

Retained % fall

79.4 0.0 100 100 100 97.8 85.1

Retained % spring

93.2 67.2 100 100 100 98.4 95.0

% Baptist 30.7 33.1 36.2 32.7 30.9 52.3 30.6 % from Texas

84.6 85.8 84.1 85.2 83.9 88.1 58.6

% Alumni 18.6 13.9 10.8 0.0 0.0 100 34.1 Campus visit (at least 1)

0.0 41.5 50.9 44.6 43.5 36.6 39.3

Attended premiere

0.0 45.6 48.6 48.5 44.5 49.5 31.7

Attended orientation

91.4 96.3 96.1 95.4 95.5 96.9 92

Attended line camp

19.4 30.7 43.4 42.1 30.4 37.4 27.3

Merit Index 88.2 83.1 92.5 91.7 89.9 87.1 Female 52.7 61.1 61.4 62.5 60.1 62.2 53.8 Male 47.3 38.9 38.6 37.5 39.9 37.8 46.2 Minority 47.5 36.8 27.2 36.0 35.4 16.6 22.8 Scholar Athlete

5.4 1.0 1.5 2.5 2.5 1.0 3.3

Request a transcript

11.0 36.5 11.1 8.8 9.0 8.8 7.3

Deficiency 28.7 43.2 22.9 22.1 24.1 18.8 26.6 First Generation

21.8 23.6 15.2 16.0 16.1 2.7 8.2

Honors flag 6.2 6.1 8.7 11.9 10.8 6.5 9.9 ELG flag 4.0 6.1 4.4 5.2 6.5 3.3 3.5 Largest major fall

BIO BIO PBUS BIO BIO PBUS PBUS

largest major spring

BIO BIO PBUS BIO BIO PBUS PBUS

AU change 16.6 40.9 10.3 10.2 8.3 11.2 15.4 Major change

40.3 38.0 38.2 40.9 38.5 38.5 17.4

PMed 33.3 34.5 27.2 34.6 31.9 15.7 19.1 ECS 9.4 8.4 9.0 7.3 7.5 8.6 7.6 Undecided 8.2 11.1 6.2 8.3 8.5 10.4 7.2 FAFSA 75.6 78.7 73.5 77.3 70.5 60.3 52.1 BIC 3.8 2.4 5.9 5.8 6.0 3.1 5.9 Extra Curric 44.9 36.5 52.5 51.9 48.5 47.8 39.1 Largest % dorm

PENLAND NRUSS COLLIN COLLIN COLLIN COLLIN PENLAND

LLC 13.8 15.9 24.4 25.0 23.6 15.3 20.0 Bio major 20.0 19.9 13.4 17.7 20.4 9.8 8.9 Stem flag 40.7 36.5 29.8 34.8 33.7 21.9 24.7

59

BU 1st choice 67.2 83.8 84.9 80.9 80.8 87.3 78.9 PS fall 12.2 22.3 6.7 8.7 10.6 8.4 11.5 PS spring 13.2 30.0 7.5 7.9 6.3 7.0 9.8 Means Fall GPA 2.96 2.47 3.17 3.11 3.19 3.22 3.04 Attempted hours fall

14.76 14.54 14.98 15.07 14.93 14.57 14.88

Attempted hours spring

15.11 14.91 15.29 15.23 15.46 14.78 15.05

Distance 278.70 254.67 239.62 230.03 250.72 186.67 404.85 High School Percentile

81.04 77.16 83.88 83.19 81.97 80.84 0

SAT Model 1171.58 1155.14 1214.40 1207.42 1204.07 1195.99 1199.26 SARRRAT 154.5 149.49 159.86 158.71 157.33 155.61 150.11 Beginning hours

6.33 5.39 9.37 9.01 7.60 7.70 6.37

Percent met 0.890 0.898 0.943 0.937 0.937 0.955 0.956 Percent met loan

0.541 0.618 0.643 0.601 0.636 0.725 0.780

Percent met scholarship

0.706 0.698 0.797 0.780 0.785 0.834 0.849

Percent met work/study

0.375 0.426 0.454 0.423 0.469 0.588 0.662

Percent hours complete fall

0.928 0.803 0.967 0.959 0.966 0.970 0.925

EFC 18720.67 24341.79 28626.16 25467.46 27043.29 32323.16 35350.36 Course difficulty fall

2.94 2.92 2.95 2.94 2.95 2.98 2.97

Course difficulty spring

2.97 3.05 2.97 2.97 2.95 3.02 3.01

AP/IB hours 2.47 1.59 3.83 3.88 3.52 2.53 2.15

60

Institutional Variables

2008 Cohort Frequencies and Means of Variables

Class 1 (540)

Class 2 (465)

Class 3 (436)

Class 4 (381)

Class 5 (221)

Class 6 (560)

Outlier class (459)

Retained % fall

78.1 88.2 90.4 95.0 94.6 92.1 56.1

Retained % spring

100 100 100 100 100 100 61.8

% Baptist 33.3 36.6 38.8 33.9 31.2 37.0 37.0 % from Texas

85.6 79.1 83.0 82.9 76.5 81.3 78.6

% Alumni 25.0 25.6 29.8 33.1 22.6 33.2 25.2 Campus visit (at least 1)

30.4 43.5 33.9 34.1 33.5 37.3 34.5

Attended premiere

39.3 34.0 37.4 40.9 41.6 35.4 36.1

Attended orientation

94.8 96.3 95.2 95.3 97.3 94.8 90.7

Attended line camp

31.3 32.3 36.5 41.2 38.9 27.7 27.5

Merit Index 81.9 92.7 88.1 91.9 95.5 86.1 86.4 Female 54.1 65.4 62.4 61.4 68.3 51.6 57.3 Male 45.9 34.6 37.6 38.6 31.7 48.4 42.7 Minority 40.2 31.8 29.8 30.7 35.3 26.8 25.7 Scholar Athlete

1.7 1.1 1.6 0.3 0.0 2.9 8.9

Request a transcript

6.9 7.6 0.0 8.1 0.0 29.2

Deficiency 99.6 1.3 22.7 0.0 0.0 0.0 32.1 First Generation

17.8 12.9 15.1 11.0 11.8 13.4 14.5

Honors flag 11.6 8.3 8.7 16.3 8.0 7.3 ELG flag 4.3 13.1 4.6 0.6 3.6 0.2 4.5 Largest major fall

PBUS BIO Undec PBUS BIO PBUS PBUS

largest major spring

PBUS BIO PBUS PBUS BIO PBUS PBUS

AU change 2.8 1.1 52.8 0.5 1.8 0.0 44.3 Major change

5.0 0 96.6 22.6 2.3 23.9 45.0

PMed 34.1 26.5 29.8 28.9 39.8 21.6 17.7 ECS 6.7 2.6 10.3 2.4 5.0 4.6 25.5 Undecided 6.9 3.9 22.2 6.3 4.5 8.0 6.6 FAFSA 70.4 74.8 72.0 68.8 66.1 61.6 68.9 BIC 2.6 4.3 6.7 6.3 5.9 5.4 3.4 Extra Curric

40.9 45.6 45.6 100 100 0.0 37.1

Largest % dorm

Penland KKNT COLLIN COLLIN COLLIN Penland NVHN

LLC 14.8 27.1 14.9 14.7 19.9 13.6 34.3 Bio major 18.0 12.0 14.9 15.7 24.9 15.2 11.6

61

Class 1 (540)

Class 2 (465)

Class 3 (436)

Class 4 (381)

Class 5 (221)

Class 6 (560)

Outlier class (459)

Stem flag 33.3 20.0 33.5 28.1 37.6 25.7 46.1 BU first choice

83.0 75.1 81.9 82.7 82.7 81.1 78.7

PS fall 27.4 4.9 8.0 6.1 4.6 10.3 8.1 PS spring 30.0 1.5 6.4 2.1 1.8 3.2 22.7 Means Fall GPA 2.27 3.46 3.07 3.32 3.41 3.20 3.06 Attempted hours fall

14.69 14.98 14.82 14.83 15.12 14.76 14.77

Attempted hours spring

15.09 15.36 15.02 15.04 15.29 14.96 15.25

Distance 237.80 274.84 270.79 234.23 297.34 266.94 268.11 High School Percentile

75.35 85.39 81.53 84.17 86.42 80.52 80.46

SAT Model 1143.00 1234.99 1184.75 1207.27 1239.77 1188.73 1196.11 SARRRAT 146.31 161.97 154.81 158.56 162.64 153.72 154.50 Beginning hours

5.23 10.58 7.10 7.80 9.14 6.98 7.76

Percent met 0.919 0.955 0.926 0.941 0.951 0.928 0.926 Percent met loan

0.639 0.610 0.632 0.677 0.707 0.675 0.659

Percent met scholarship

0.725 0.815 0.762 0.803 0.843 0.794 0.783

Percent met work/study

0.444 0.435 0.462 0.526 0.579 0.540 0.503

Percent hours complete fall

0.858 0.982 0.953 0.984 0.986 0.980 0.845

EFC 23640.89 27319.80 25454.17 31973.98 34439.15 25942.53 27009.87 Course difficulty fall

2.89 3.04 2.95 2.92 2.90 2.96 2.98

Course difficulty spring

2.91 3.05 3.00 2.94 2.94 2.96 3.14

AP/IB hours 1.06 4.40 2.62 3.48 5.14 2.64 2.8

62

Appendix B Logistic Regression Using All Variables (Groups of Interest)

Biology Majors, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.TWO Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 376 Number of Observations Used 376 Response Profile Ordered retained_ Total Value fall Frequency 1 1 321 2 0 55 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 314.979 248.260 SC 318.909 283.626 -2 Log L 312.979 230.260 R-Square 0.1975 Max-rescaled R-Square 0.3495 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 82.7192 8 <.0001 Score 104.0340 8 <.0001 Wald 50.2738 8 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -17.4257 6.0966 8.1696 0.0043 satmodel 1 0.00458 0.00192 5.7026 0.0169 0.2994 ACADEMIC_INDEX 1 -0.0145 0.0137 1.1217 0.2896 -0.1375 ps_spr2 1 -0.5312 0.5661 0.8802 0.3481 -0.0943 TOTAL_CONTACTS 1 0.0856 0.0671 1.6242 0.2025 0.1389 trans 1 -0.3959 0.6030 0.4311 0.5115 -0.0643 hsc_rate 1 10.9994 5.9216 3.4503 0.0632 0.1750 crsdiffspr 1 5.8451 1.3173 19.6870 <.0001 0.4398 percent_hrs_comp_fal 1 2.1291 1.1495 3.4307 0.0640 0.2011

63

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.005 1.001 1.008 ACADEMIC_INDEX 0.986 0.960 1.012 ps_spr2 0.588 0.194 1.783 TOTAL_CONTACTS 1.089 0.955 1.243 trans 0.673 0.206 2.195 hsc_rate >999.999 0.545 >999.999 crsdiffspr 345.527 26.131 >999.999 percent_hrs_comp_fal 8.407 0.884 79.994 Association of Predicted Probabilities and Observed Responses Percent Concordant 81.0 Somers' D 0.626 Percent Discordant 18.4 Gamma 0.630 Percent Tied 0.6 Tau-a 0.157 Pairs 17655 c 0.813

64



The LOGISTIC Procedure Model Information Data Set WORK.TWO Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 472 Number of Observations Used 464 Response Profile Ordered retained_ Total Value fall Frequency 1 1 376 2 0 88 Probability modeled is retained_fall=1. NOTE: 8 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 452.751 287.876 SC 456.890 349.975 -2 Log L 450.751 257.876 R-Square 0.3401 Max-rescaled R-Square 0.5473 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 192.8742 14 <.0001 Score 194.6596 14 <.0001 Wald 77.0603 14 <.0001

65

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -31.8968 10.2653 9.6550 0.0019 satmodel 1 0.00287 0.00190 2.2840 0.1307 0.1940 ACADEMIC_INDEX 1 0.00371 0.0116 0.1027 0.7486 0.0364 ps_spr2 1 -0.8172 0.5126 2.5417 0.1109 -0.1605 trans 1 -1.2645 0.4501 7.8929 0.0050 -0.2468 extracurrnum 1 0.6246 0.2177 8.2313 0.0041 0.3785 init_rate 1 23.9638 7.8976 9.2070 0.0024 0.2759 area_rate 1 -0.2756 7.5023 0.0013 0.9707 -0.00339 first_gen_coll_stude 1 -0.8739 0.3645 5.7475 0.0165 -0.1990 perc_mets 1 1.2128 0.6824 3.1583 0.0755 0.1788 minority2 1 0.3973 0.3766 1.1134 0.2913 0.1095 app_span 1 0.0698 0.0994 0.4937 0.4823 0.0634 crsdiffspr 1 6.9068 1.2350 31.2759 <.0001 0.5709 cbe_eng1302 1 1.5878 1.1572 1.8827 0.1700 0.2567 percent_hrs_comp_fal 1 2.1537 0.9310 5.3516 0.0207 0.2521 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.003 0.999 1.007 ACADEMIC_INDEX 1.004 0.981 1.027 ps_spr2 0.442 0.162 1.206 trans 0.282 0.117 0.682 extracurrnum 1.868 1.219 2.862 init_rate >999.999 >999.999 >999.999 area_rate 0.759 <0.001 >999.999 first_gen_coll_stude 0.417 0.204 0.853 perc_mets 3.363 0.883 12.812 minority2 1.488 0.711 3.112 app_span 1.072 0.883 1.303 crsdiffspr 999.028 88.784 >999.999 cbe_eng1302 4.893 0.506 47.270 percent_hrs_comp_fal 8.617 1.390 53.434 Association of Predicted Probabilities and Observed Responses Percent Concordant 89.3 Somers' D 0.789 Percent Discordant 10.5 Gamma 0.790 Percent Tied 0.2 Tau-a 0.243 Pairs 33088 c 0.894

66


Engineering & Computer Science Majors, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.FOUR2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 175 Number of Observations Used 174 Response Profile Ordered retained_ Total Value fall Frequency 1 1 137 2 0 37 Probability modeled is retained_fall=1. NOTE: 1 observation was deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 182.069 126.896 SC 185.228 167.964 -2 Log L 180.069 100.896 R-Square 0.3656 Max-rescaled R-Square 0.5670 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 79.1725 12 <.0001 Score 75.9516 12 <.0001 Wald 37.1441 12 0.0002

67

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 16.7706 23.0920 0.5274 0.4677 satmodel 1 0.00336 0.00300 1.2546 0.2627 0.2388 ACADEMIC_INDEX 1 -0.00096 0.0192 0.0025 0.9602 -0.0113 ps_fall2 1 -1.5002 0.6195 5.8636 0.0155 -0.3214 trans 1 -3.7869 0.8644 19.1938 <.0001 -0.6530 dorm_rate 1 -25.9992 23.5122 1.2227 0.2688 -0.1530 extracurrnum 1 1.5131 0.6516 5.3928 0.0202 0.5581 first_gen_coll_stude 1 0.4264 0.7174 0.3533 0.5523 0.0866 male 1 -1.3196 0.8275 2.5431 0.1108 -0.2860 hh_income 1 1.394E-6 4.34E-6 0.1032 0.7480 0.0498 perc_mets 1 -0.1942 1.1612 0.0280 0.8672 -0.0267 att_hrs_fall 1 0.2594 0.2016 1.6563 0.1981 0.1863 percent_hrs_comp_fal 1 3.6371 1.2552 8.3960 0.0038 0.4984 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.003 0.997 1.009 ACADEMIC_INDEX 0.999 0.962 1.037 ps_fall2 0.223 0.066 0.751 trans 0.023 0.004 0.123 dorm_rate <0.001 <0.001 >999.999 extracurrnum 4.541 1.266 16.284 first_gen_coll_stude 1.532 0.375 6.249 male 0.267 0.053 1.353 hh_income 1.000 1.000 1.000 perc_mets 0.823 0.085 8.018 att_hrs_fall 1.296 0.873 1.924 percent_hrs_comp_fal 37.982 3.244 444.656 Association of Predicted Probabilities and Observed Responses Percent Concordant 92.3 Somers' D 0.847 Percent Discordant 7.6 Gamma 0.848 Percent Tied 0.1 Tau-a 0.285 Pairs 5069 c 0.923

68


Engineering & Computer Science Majors, Fall 2008 The LOGISTIC Procedure Model Information Data Set WORK.FOUR2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 255 Number of Observations Used 250 Response Profile Ordered retained_ Total Value fall Frequency 1 1 205 2 0 45 Probability modeled is retained_fall=1. NOTE: 5 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 237.697 195.279 SC 241.218 234.015 -2 Log L 235.697 173.279 R-Square 0.2209 Max-rescaled R-Square 0.3619 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 62.4180 10 <.0001 Score 66.6843 10 <.0001 Wald 42.0700 10 <.0001

69

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -5.8637 2.5460 5.3042 0.0213 satmodel 1 -0.00061 0.00227 0.0714 0.7894 -0.0435 ACADEMIC_INDEX 1 0.0126 0.0133 0.8960 0.3439 0.1401 ps_fall2 1 -1.6113 0.4899 10.8154 0.0010 -0.3478 trans 1 -0.8166 0.6033 1.8324 0.1758 -0.1304 first_gen_coll_stude 1 0.8516 0.5753 2.1913 0.1388 0.1807 male 1 0.2553 0.5350 0.2277 0.6332 0.0580 hh_income 1 5.143E-6 2.735E-6 3.5374 0.0600 0.2454 perc_mets 1 0.6955 0.9032 0.5930 0.4413 0.0889 crsdiffspr 1 7.2951 2.0087 13.1890 0.0003 0.4120 percent_hrs_comp_fal 1 1.9158 0.9576 4.0022 0.0454 0.2120 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 0.999 0.995 1.004 ACADEMIC_INDEX 1.013 0.987 1.039 ps_fall2 0.200 0.076 0.522 trans 0.442 0.135 1.442 first_gen_coll_stude 2.343 0.759 7.237 male 1.291 0.452 3.683 hh_income 1.000 1.000 1.000 perc_mets 2.005 0.341 11.771 crsdiffspr >999.999 28.732 >999.999 percent_hrs_comp_fal 6.792 1.040 44.378 Association of Predicted Probabilities and Observed Responses Percent Concordant 83.1 Somers' D 0.665 Percent Discordant 16.6 Gamma 0.667 Percent Tied 0.3 Tau-a 0.197 Pairs 9225 c 0.833

70


Pre-Med Students, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.THREE2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 784 Number of Observations Used 777 Response Profile Ordered retained_ Total Value fall Frequency 1 1 663 2 0 114 Probability modeled is retained_fall=1. NOTE: 7 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 649.977 470.030 SC 654.633 521.240 -2 Log L 647.977 448.030 R-Square 0.2269 Max-rescaled R-Square 0.4011 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 199.9471 10 <.0001 Score 239.3805 10 <.0001 Wald 119.1487 10 <.0001

71

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 1.9287 10.4511 0.0341 0.8536 satmodel 1 0.00346 0.00136 6.4959 0.0108 0.2440 ACADEMIC_INDEX 1 -0.00880 0.00921 0.9141 0.3390 -0.0911 ps_spr2 1 -0.2664 0.4002 0.4430 0.5057 -0.0484 defic 1 -0.9043 0.2908 9.6722 0.0019 -0.2331 trans 1 -1.2613 0.3600 12.2765 0.0005 -0.2183 area_rate 1 7.0584 5.7919 1.4851 0.2230 0.0828 hsc_rate 1 4.4063 4.0307 1.1951 0.2743 0.0716 crsdiffspr 1 6.4233 0.8996 50.9794 <.0001 0.4927 mjr_rate 1 -19.0015 8.4243 5.0875 0.0241 -0.1664 percent_hrs_comp_fal 1 1.8011 0.7898 5.2003 0.0226 0.1761 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.003 1.001 1.006 ACADEMIC_INDEX 0.991 0.974 1.009 ps_spr2 0.766 0.350 1.679 defic 0.405 0.229 0.716 trans 0.283 0.140 0.574 area_rate >999.999 0.014 >999.999 hsc_rate 81.969 0.030 >999.999 crsdiffspr 616.057 105.647 >999.999 mjr_rate <0.001 <0.001 0.083 percent_hrs_comp_fal 6.056 1.288 28.476 Association of Predicted Probabilities and Observed Responses Percent Concordant 85.8 Somers' D 0.719 Percent Discordant 13.9 Gamma 0.721 Percent Tied 0.3 Tau-a 0.180 Pairs 75582 c 0.859

72


Pre-Med Students, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.THREE2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 843 Number of Observations Used 820 Response Profile Ordered retained_ Total Value fall Frequency 1 1 670 2 0 150 Probability modeled is retained_fall=1. NOTE: 23 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 782.316 554.740 SC 787.026 639.508 -2 Log L 780.316 518.740 R-Square 0.2731 Max-rescaled R-Square 0.4449 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 261.5762 17 <.0001 Score 280.9589 17 <.0001 Wald 141.8874 17 <.0001

73

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -30.1920 10.4701 8.3153 0.0039 satmodel 1 -0.00029 0.00124 0.0541 0.8162 -0.0201 ACADEMIC_INDEX 1 0.00890 0.00789 1.2746 0.2589 0.0875 ps_spr2 1 -0.3177 0.3691 0.7409 0.3894 -0.0611 ps_fall2 1 -1.0392 0.3076 11.4149 0.0007 -0.1931 linecamp 1 0.9129 0.3019 9.1432 0.0025 0.2324 trans 1 -1.3049 0.2976 19.2304 <.0001 -0.2509 dorm_rate 1 11.1994 8.6136 1.6905 0.1935 0.0837 extracurrnum 1 0.3175 0.1376 5.3256 0.0210 0.1912 init_rate 1 9.4190 5.3031 3.1546 0.0757 0.1143 area_rate 1 6.0429 5.1594 1.3718 0.2415 0.0751 first_gen_coll_stude 1 -0.2445 0.2697 0.8217 0.3647 -0.0548 app_span 1 0.0354 0.0651 0.2946 0.5873 0.0322 minority2 1 0.1378 0.2481 0.3085 0.5786 0.0380 cbe_chem1301 1 -0.9176 0.8612 1.1354 0.2866 -0.0656 cbe_eng1302 1 1.1467 0.6188 3.4347 0.0638 0.1918 crsdiffspr 1 5.4324 0.8422 41.6045 <.0001 0.4230 percent_hrs_comp_fal 1 2.4718 0.6824 13.1211 0.0003 0.2724 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.000 0.997 1.002 ACADEMIC_INDEX 1.009 0.993 1.025 ps_spr2 0.728 0.353 1.500 ps_fall2 0.354 0.194 0.646 linecamp 2.492 1.379 4.503 trans 0.271 0.151 0.486 dorm_rate >999.999 0.003 >999.999 extracurrnum 1.374 1.049 1.799 init_rate >999.999 0.377 >999.999 area_rate 421.093 0.017 >999.999 first_gen_coll_stude 0.783 0.462 1.329 app_span 1.036 0.912 1.177 minority2 1.148 0.706 1.867 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits cbe_chem1301 0.399 0.074 2.160 cbe_eng1302 3.148 0.936 10.585 crsdiffspr 228.691 43.889 >999.999 percent_hrs_comp_fal 11.843 3.109 45.114 Association of Predicted Probabilities and Observed Responses Percent Concordant 85.9 Somers' D 0.720 Percent Discordant 13.9 Gamma 0.722 Percent Tied 0.3 Tau-a 0.216 Pairs 100500 c 0.860

74


STEM Majors, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.FIVE2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 784 Number of Observations Used 784 Response Profile Ordered retained_ Total Value fall Frequency 1 1 655 2 0 129 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 703.090 505.238 SC 707.754 565.875 -2 Log L 701.090 479.238 R-Square 0.2465 Max-rescaled R-Square 0.4170 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 221.8515 12 <.0001 Score 244.5437 12 <.0001 Wald 118.9759 12 <.0001

75

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -22.0566 6.3418 12.0962 0.0005 satmodel 1 0.00214 0.00136 2.4648 0.1164 0.1510 ACADEMIC_INDEX 1 -0.00459 0.00807 0.3240 0.5692 -0.0487 ps_spr2 1 -0.4122 0.3907 1.1132 0.2914 -0.0762 trans 1 -1.3357 0.3762 12.6078 0.0004 -0.2193 extracurrnum 1 0.4309 0.1716 6.3039 0.0120 0.2052 honors_flag2 1 0.8095 0.5498 2.1679 0.1409 0.1545 area_rate 1 10.5371 5.5050 3.6638 0.0556 0.1217 hsc_rate 1 7.2739 3.7136 3.8366 0.0501 0.1203 crsdiffspr 1 5.2352 0.8906 34.5541 <.0001 0.3943 cbe_eng1302 1 1.2220 0.8606 2.0162 0.1556 0.2159 cbe_mth1321 1 1.1496 0.5592 4.2265 0.0398 0.2429 percent_hrs_comp_fal 1 2.3409 0.7496 9.7530 0.0018 0.2566 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.002 0.999 1.005 ACADEMIC_INDEX 0.995 0.980 1.011 ps_spr2 0.662 0.308 1.424 trans 0.263 0.126 0.550 extracurrnum 1.539 1.099 2.154 honors_flag2 2.247 0.765 6.599 area_rate >999.999 0.777 >999.999 hsc_rate >999.999 0.995 >999.999 crsdiffspr 187.758 32.774 >999.999 cbe_eng1302 3.394 0.628 18.334 cbe_mth1321 3.157 1.055 9.447 percent_hrs_comp_fal 10.390 2.391 45.149 Association of Predicted Probabilities and Observed Responses Percent Concordant 84.6 Somers' D 0.694 Percent Discordant 15.2 Gamma 0.696 Percent Tied 0.3 Tau-a 0.191 Pairs 84495 c 0.847

76


STEM Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.FIVE2 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 965 Number of Observations Used 965 Response Profile Ordered retained_ Total Value fall Frequency 1 1 786 2 0 179 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 927.667 667.972 SC 932.539 741.054 -2 Log L 925.667 637.972 R-Square 0.2578 Max-rescaled R-Square 0.4179 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 287.6953 14 <.0001 Score 313.4044 14 <.0001 Wald 158.6434 14 <.0001

77

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -11.2073 4.6545 5.7976 0.0160 satmodel 1 0.000841 0.00111 0.5740 0.4487 0.0592 ACADEMIC_INDEX 1 0.00184 0.00716 0.0658 0.7975 0.0188 ps_spr2 1 -0.6919 0.2990 5.3542 0.0207 -0.1416 linecamp 1 0.3997 0.2448 2.6650 0.1026 0.1019 trans 1 -1.1859 0.2822 17.6641 <.0001 -0.2166 extracurrnum 1 0.2748 0.1225 5.0332 0.0249 0.1563 area_rate 1 7.2751 4.6975 2.3985 0.1214 0.0903 first_gen_coll_stude 1 -0.4593 0.2384 3.7122 0.0540 -0.1015 perc_mets 1 1.6138 0.5274 9.3641 0.0022 0.2249 perc_met 1 -1.9018 0.9672 3.8666 0.0493 -0.1369 app_span 1 0.000175 0.0559 0.0000 0.9975 0.000174 crsdiffspr 1 6.1891 0.8132 57.9248 <.0001 0.4578 BEG_CUM_EARNED_HRS_U 1 0.0250 0.0120 4.3092 0.0379 0.1542 percent_hrs_comp_fal 1 2.2329 0.6005 13.8260 0.0002 0.2494 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.001 0.999 1.003 ACADEMIC_INDEX 1.002 0.988 1.016 ps_spr2 0.501 0.279 0.900 linecamp 1.491 0.923 2.410 trans 0.305 0.176 0.531 extracurrnum 1.316 1.035 1.673 area_rate >999.999 0.145 >999.999 first_gen_coll_stude 0.632 0.396 1.008 perc_mets 5.022 1.786 14.118 perc_met 0.149 0.022 0.994 app_span 1.000 0.896 1.116 crsdiffspr 487.426 99.017 >999.999 BEG_CUM_EARNED_HRS_U 1.025 1.001 1.050 percent_hrs_comp_fal 9.327 2.875 30.262 Association of Predicted Probabilities and Observed Responses Percent Concordant 83.7 Somers' D 0.678 Percent Discordant 16.0 Gamma 0.680 Percent Tied 0.3 Tau-a 0.205 Pairs 140694 c 0.839

78


Undecided Majors, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.TWO Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 271 Number of Observations Used 271 Response Profile Ordered retained_ Total Value fall Frequency 1 1 240 2 0 31 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 194.735 142.877 SC 198.337 168.092 -2 Log L 192.735 128.877 R-Square 0.2099 Max-rescaled R-Square 0.4125 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 63.8576 6 <.0001 Score 90.0548 6 <.0001 Wald 35.7154 6 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -7.2979 2.5313 8.3119 0.0039 satmodel 1 0.000076 0.00285 0.0007 0.9788 0.00559 ACADEMIC_INDEX 1 0.00740 0.0164 0.2036 0.6518 0.0896 trans 1 -2.4316 0.7418 10.7454 0.0010 -0.2870 alum 1 -0.3085 0.5062 0.3713 0.5423 -0.0791 crsdiffspr 1 6.5411 2.2529 8.4302 0.0037 0.3912 percent_hrs_comp_fal 1 5.7233 1.3440 18.1351 <.0001 0.5182

79

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.000 0.995 1.006 ACADEMIC_INDEX 1.007 0.976 1.040 trans 0.088 0.021 0.376 alum 0.735 0.272 1.981 crsdiffspr 693.079 8.378 >999.999 percent_hrs_comp_fal 305.900 21.959 >999.999 Association of Predicted Probabilities and Observed Responses Percent Concordant 83.7 Somers' D 0.680 Percent Discordant 15.7 Gamma 0.684 Percent Tied 0.6 Tau-a 0.138 Pairs 7440 c 0.840

80



The LOGISTIC Procedure Model Information Data Set WORK.TWO Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 261 Number of Observations Used 261 Response Profile Ordered retained_ Total Value fall Frequency 1 1 209 2 0 52 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 262.655 186.326 SC 266.219 225.536 -2 Log L 260.655 164.326 R-Square 0.3086 Max-rescaled R-Square 0.4886 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 96.3286 10 <.0001 Score 102.3709 10 <.0001 Wald 47.2647 10 <.0001

81

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.3389 2.2679 0.0223 0.8812 satmodel 1 -0.00088 0.00252 0.1208 0.7282 -0.0576 ACADEMIC_INDEX 1 0.00681 0.0147 0.2150 0.6429 0.0783 ps_spr2 1 -2.9014 0.5782 25.1817 <.0001 -0.4960 SELF_INIT_CNTCTS 1 -0.1114 0.0942 1.3970 0.2372 -0.1421 trans 1 -2.0552 0.5754 12.7586 0.0004 -0.3915 llc_flag 1 2.2984 1.2741 3.2541 0.0712 0.3929 male 1 0.1368 0.4728 0.0837 0.7724 0.0361 perc_mets 1 1.4112 1.4138 0.9963 0.3182 0.1987 perc_metw 1 -0.6448 0.7758 0.6908 0.4059 -0.1672 crsdiffspr 1 5.3798 1.6294 10.9018 0.0010 0.4342 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 0.999 0.994 1.004 ACADEMIC_INDEX 1.007 0.978 1.036 ps_spr2 0.055 0.018 0.171 SELF_INIT_CNTCTS 0.895 0.744 1.076 trans 0.128 0.041 0.396 llc_flag 9.959 0.820 120.987 male 1.147 0.454 2.896 perc_mets 4.101 0.257 65.502 perc_metw 0.525 0.115 2.401 crsdiffspr 216.987 8.902 >999.999 Association of Predicted Probabilities and Observed Responses Percent Concordant 84.9 Somers' D 0.701 Percent Discordant 14.8 Gamma 0.703 Percent Tied 0.3 Tau-a 0.224 Pairs 10868 c 0.850

82

Appendix C Logistic Regression Using Admission Variables (Major Groupings)


The LOGISTIC Procedure Model Information Data Set WORK.BIOLOGY Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 472 Number of Observations Used 472 Response Profile Ordered retained_ Total Value fall Frequency 1 1 383 2 0 89 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 459.016 433.717 SC 463.173 466.972 -2 Log L 457.016 417.717 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 39.2996 7 <.0001 Score 38.1344 7 <.0001 Wald 34.5549 7 <.0001 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -4.4831 1.2677 12.5060 0.0004 HIGH_SCHOOL_PERCENTI 1 0.0175 0.00852 4.2058 0.0403 0.1306 SATM 1 0.00616 0.00195 9.9349 0.0016 0.2651 SATV 1 0.00104 0.00203 0.2617 0.6089 0.0428 apexam_bio1305 1 -0.1886 0.3394 0.3086 0.5785 -0.0457 apexam_chem1301 1 -0.2855 0.5402 0.2793 0.5972 -0.0434 apexam_eng1302 1 0.6468 0.2924 4.8954 0.0269 0.1781 apexam_mth1321 1 0.3384 0.3624 0.8720 0.3504 0.0820

83

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.018 1.001 1.035 SATM 1.006 1.002 1.010 SATV 1.001 0.997 1.005 apexam_bio1305 0.828 0.426 1.611 apexam_chem1301 0.752 0.261 2.167 apexam_eng1302 1.910 1.077 3.387 apexam_mth1321 1.403 0.689 2.854 Association of Predicted Probabilities and Observed Responses Percent Concordant 70.2 Somers' D 0.409 Percent Discordant 29.3 Gamma 0.411 Percent Tied 0.4 Tau-a 0.125 Pairs 34087 c 0.704

84


Chemistry, Hard Sciences, and Math Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.CHEMHARDSCIMATH Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 150 Number of Observations Used 150 Response Profile Ordered retained_ Total Value fall Frequency 1 1 125 2 0 25 Probability modeled is retained_fall=1. Model Convergence Status Quasi-complete separation of data points detected. WARNING: The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 137.168 129.001 SC 140.179 153.087 -2 Log L 135.168 113.001 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 22.1669 7 0.0024 Score 16.6462 7 0.0198 Wald 9.7268 7 0.2046 The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.

85

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -3.6953 2.7639 1.7876 0.1812 HIGH_SCHOOL_PERCENTI 1 0.0250 0.0208 1.4356 0.2309 0.1547 SATM 1 0.00545 0.00378 2.0760 0.1496 0.2335 SATV 1 0.000082 0.00351 0.0005 0.9813 0.00366 apexam_bio1305 1 12.2595 186.0 0.0043 0.9474 2.6054 apexam_chem1301 1 1.3485 0.6761 3.9786 0.0461 0.3325 apexam_eng1302 1 -0.8719 0.5064 2.9653 0.0851 -0.2412 apexam_mth1321 1 -1.0191 0.5502 3.4312 0.0640 -0.2567 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.025 0.984 1.068 SATM 1.005 0.998 1.013 SATV 1.000 0.993 1.007 apexam_bio1305 >999.999 <0.001 >999.999 apexam_chem1301 3.852 1.024 14.492 apexam_eng1302 0.418 0.155 1.128 apexam_mth1321 0.361 0.123 1.061 Association of Predicted Probabilities and Observed Responses Percent Concordant 75.3 Somers' D 0.509 Percent Discordant 24.4 Gamma 0.511 Percent Tied 0.4 Tau-a 0.142 Pairs 3125 c 0.755

86


Communications Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.COMMUNICATION Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 117 Number of Observations Used 117 Response Profile Ordered retained_ Total Value fall Frequency 1 1 101 2 0 16 Probability modeled is retained_fall=1. Model Convergence Status Quasi-complete separation of data points detected. WARNING: The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 95.372 95.714 SC 98.134 117.811 -2 Log L 93.372 79.714 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 13.6580 7 0.0576 Score 13.2500 7 0.0662 Wald 9.2330 7 0.2364 The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.

87

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.9424 2.8342 0.1106 0.7395 HIGH_SCHOOL_PERCENTI 1 0.0317 0.0156 4.1317 0.0421 0.2984 SATM 1 0.00257 0.00484 0.2818 0.5955 0.1039 SATV 1 -0.00247 0.00462 0.2859 0.5929 -0.1154 apexam_bio1305 1 10.3208 531.3 0.0004 0.9845 1.0384 apexam_chem1301 1 10.5637 482.0 0.0005 0.9825 1.1830 apexam_eng1302 1 1.4456 0.8910 2.6324 0.1047 0.3960 apexam_mth1321 1 -0.4858 1.2102 0.1612 0.6881 -0.0845 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.032 1.001 1.064 SATM 1.003 0.993 1.012 SATV 0.998 0.989 1.007 apexam_bio1305 >999.999 <0.001 >999.999 apexam_chem1301 >999.999 <0.001 >999.999 apexam_eng1302 4.244 0.740 24.333 apexam_mth1321 0.615 0.057 6.594 Association of Predicted Probabilities and Observed Responses Percent Concordant 76.5 Somers' D 0.533 Percent Discordant 23.2 Gamma 0.535 Percent Tied 0.2 Tau-a 0.127 Pairs 1616 c 0.767

88


Education Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.EDUCATION Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 110 Number of Observations Used 110 Response Profile Ordered retained_ Total Value fall Frequency 1 1 98 2 0 12 Probability modeled is retained_fall=1. Model Convergence Status Quasi-complete separation of data points detected. WARNING: The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 77.814 82.972 SC 80.515 101.876 -2 Log L 75.814 68.972 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 6.8418 6 0.3357 Score 7.0090 6 0.3200 Wald 5.6350 6 0.4653 The SAS System 11:35 Tuesday, August 17, 2010 8 The LOGISTIC Procedure WARNING: The validity of the model fit is questionable. NOTE: The following parameters have been set to 0, since the variables are a linear combination of other variables as shown. apexam_chem1301 = 0

89

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.3452 3.1503 0.0120 0.9127 HIGH_SCHOOL_PERCENTI 1 0.0384 0.0178 4.6293 0.0314 0.4036 SATM 1 -0.00054 0.00528 0.0103 0.9191 -0.0212 SATV 1 0.000642 0.00472 0.0185 0.8918 0.0285 apexam_bio1305 1 11.7118 533.3 0.0005 0.9825 1.3512 apexam_chem1301 0 0 . . . . apexam_eng1302 1 -0.5652 0.8623 0.4297 0.5121 -0.1521 apexam_mth1321 1 -0.4484 1.2994 0.1191 0.7300 -0.0606 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.039 1.003 1.076 SATM 0.999 0.989 1.010 SATV 1.001 0.991 1.010 apexam_bio1305 >999.999 <0.001 >999.999 apexam_eng1302 0.568 0.105 3.080 apexam_mth1321 0.639 0.050 8.153 Association of Predicted Probabilities and Observed Responses Percent Concordant 71.8 Somers' D 0.446 Percent Discordant 27.1 Gamma 0.451 Percent Tied 1.1 Tau-a 0.088 Pairs 1176 c 0.723

90


Engineering & Computer Science Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.ECS Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 255 Number of Observations Used 255 Response Profile Ordered retained_ Total Value fall Frequency 1 1 208 2 0 47 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 245.715 244.607 SC 249.256 280.020 -2 Log L 243.715 224.607 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 19.1076 9 0.0243 Score 18.1396 9 0.0336 Wald 16.1521 9 0.0638 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -5.1946 2.4009 4.6812 0.0305 att_hrs_fall 1 0.0782 0.1799 0.1888 0.6639 0.0589 crsdifffall 1 0.0382 4.3389 0.0001 0.9930 0.00120 HIGH_SCHOOL_PERCENTI 1 0.0229 0.0114 4.0672 0.0437 0.1880 SATM 1 0.00756 0.00279 7.3481 0.0067 0.3005 SATV 1 -0.00182 0.00262 0.4845 0.4864 -0.0832 apexam_bio1305 1 1.2107 1.0729 1.2733 0.2591 0.1798 apexam_chem1301 1 0.7071 0.8044 0.7725 0.3794 0.1161 apexam_eng1302 1 -0.5131 0.4193 1.4976 0.2210 -0.1361 apexam_mth1321 1 -0.0138 0.4037 0.0012 0.9727 -0.00366

91

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits att_hrs_fall 1.081 0.760 1.539 crsdifffall 1.039 <0.001 >999.999 HIGH_SCHOOL_PERCENTI 1.023 1.001 1.046 SATM 1.008 1.002 1.013 SATV 0.998 0.993 1.003 apexam_bio1305 3.356 0.410 27.482 apexam_chem1301 2.028 0.419 9.813 apexam_eng1302 0.599 0.263 1.362 apexam_mth1321 0.986 0.447 2.176 Association of Predicted Probabilities and Observed Responses Percent Concordant 70.4 Somers' D 0.413 Percent Discordant 29.2 Gamma 0.414 Percent Tied 0.4 Tau-a 0.125 Pairs 9776 c 0.706

92


Humanities Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.ENGHISTPHIL3 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 143 Number of Observations Used 143 Response Profile Ordered retained_ Total Value fall Frequency 1 1 120 2 0 23 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 128.143 131.805 SC 131.106 155.507 -2 Log L 126.143 115.805 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 10.3382 7 0.1702 Score 9.4342 7 0.2230 Wald 8.3128 7 0.3058 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 2.4760 2.5100 0.9731 0.3239 HIGH_SCHOOL_PERCENTI 1 0.00700 0.0169 0.1725 0.6779 0.0546 SATM 1 0.00578 0.00407 2.0150 0.1558 0.2408 SATV 1 -0.00798 0.00362 4.8666 0.0274 -0.3581 apexam_bio1305 1 -0.4287 0.8703 0.2426 0.6223 -0.0705 apexam_chem1301 1 -2.0948 1.1476 3.3320 0.0679 -0.2501 apexam_eng1302 1 0.6304 0.5543 1.2934 0.2554 0.1744 apexam_mth1321 1 1.8473 1.3275 1.9364 0.1641 0.3308

93


94


Family Consumer Sciences & Fine Arts Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.FAMILYFINEARTS Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 100 Number of Observations Used 100 Response Profile Ordered retained_ Total Value fall Frequency 1 1 84 2 0 16 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 89.934 87.095 SC 92.539 107.936 -2 Log L 87.934 71.095 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 16.8393 7 0.0185 Score 15.9570 7 0.0255 Wald 11.6795 7 0.1116 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -2.5278 2.7209 0.8631 0.3529 HIGH_SCHOOL_PERCENTI 1 0.0459 0.0175 6.9129 0.0086 0.4287 SATM 1 0.00260 0.00507 0.2632 0.6079 0.1042 SATV 1 -0.00165 0.00441 0.1405 0.7078 -0.0768 apexam_bio1305 1 -1.7855 1.3622 1.7181 0.1899 -0.2350 apexam_chem1301 1 -3.1285 1.7339 3.2555 0.0712 -0.2427 apexam_eng1302 1 1.7798 0.9653 3.3996 0.0652 0.4761 apexam_mth1321 1 0.5414 1.5073 0.1290 0.7195 0.1009

95


96


Health and Health Education Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.HEALTH Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 164 Number of Observations Used 164 Response Profile Ordered retained_ Total Value fall Frequency 1 1 144 2 0 20 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 123.621 127.806 SC 126.721 152.605 -2 Log L 121.621 111.806 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 9.8145 7 0.1993 Score 8.6656 7 0.2776 Wald 7.5657 7 0.3724 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.6371 2.0841 0.0934 0.7598 HIGH_SCHOOL_PERCENTI 1 0.00641 0.0175 0.1346 0.7137 0.0531 SATM 1 0.00716 0.00420 2.9061 0.0882 0.3161 SATV 1 -0.00398 0.00428 0.8646 0.3525 -0.1756 apexam_bio1305 1 -1.2337 1.1611 1.1288 0.2880 -0.2183 apexam_chem1301 1 -0.4333 1.2866 0.1134 0.7363 -0.0573 apexam_eng1302 1 1.7189 0.8492 4.0971 0.0430 0.4594 apexam_mth1321 1 0.0588 1.0180 0.0033 0.9539 0.0127

97


98


Honors College Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.HONORS Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 57 Number of Observations Used 57 Response Profile Ordered retained_ Total Value fall Frequency 1 1 53 2 0 4 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 30.967 34.137 SC 33.010 50.482 -2 Log L 28.967 18.137 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 10.8292 7 0.1462 Score 13.2068 7 0.0672 Wald 5.1263 7 0.6446 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -24.6034 14.2738 2.9710 0.0848 HIGH_SCHOOL_PERCENTI 1 0.1586 0.0909 3.0481 0.0808 0.6856 SATM 1 0.0226 0.0173 1.7028 0.1919 0.6980 SATV 1 -0.00177 0.0126 0.0197 0.8883 -0.0557 apexam_bio1305 1 -3.7414 2.5164 2.2107 0.1371 -1.0365 apexam_chem1301 1 -0.9336 2.0521 0.2070 0.6491 -0.2478 apexam_eng1302 1 0.4971 1.7337 0.0822 0.7743 0.1218 apexam_mth1321 1 1.7926 1.6072 1.2441 0.2647 0.4979

99

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.172 0.981 1.400 SATM 1.023 0.989 1.058 SATV 0.998 0.974 1.023 apexam_bio1305 0.024 <0.001 3.289 apexam_chem1301 0.393 0.007 21.944 apexam_eng1302 1.644 0.055 49.159 apexam_mth1321 6.005 0.257 140.136 Association of Predicted Probabilities and Observed Responses Percent Concordant 90.6 Somers' D 0.811 Percent Discordant 9.4 Gamma 0.811 Percent Tied 0.0 Tau-a 0.108 Pairs 212 c 0.906

100


Music Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.MUSIC Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 88 Number of Observations Used 88 Response Profile Ordered retained_ Total Value fall Frequency 1 1 79 2 0 9 Probability modeled is retained_fall=1. Model Convergence Status Quasi-complete separation of data points detected. WARNING: The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 60.088 62.858 SC 62.566 82.676 -2 Log L 58.088 46.858 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 11.2308 7 0.1289 Score 12.3800 7 0.0887 Wald 8.0351 7 0.3295 WARNING: The validity of the model fit is questionable. Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.0885 3.9990 0.0005 0.9823 HIGH_SCHOOL_PERCENTI 1 0.0708 0.0260 7.3859 0.0066 0.5319 SATM 1 0.00354 0.00719 0.2424 0.6225 0.1662 SATV 1 -0.00924 0.00629 2.1546 0.1421 -0.4177 apexam_bio1305 1 10.5736 282.3 0.0014 0.9701 1.4778 apexam_chem1301 1 10.9202 317.8 0.0012 0.9726 1.4017 apexam_eng1302 1 0.1523 0.8980 0.0288 0.8654 0.0413 apexam_mth1321 1 -0.4101 1.4301 0.0822 0.7743 -0.0917

101

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.073 1.020 1.130 SATM 1.004 0.990 1.018 SATV 0.991 0.979 1.003 apexam_bio1305 >999.999 <0.001 >999.999 apexam_chem1301 >999.999 <0.001 >999.999 apexam_eng1302 1.164 0.200 6.769 apexam_mth1321 0.664 0.040 10.946 Association of Predicted Probabilities and Observed Responses Percent Concordant 75.7 Somers' D 0.522 Percent Discordant 23.5 Gamma 0.526 Percent Tied 0.8 Tau-a 0.097 Pairs 711 c 0.761

102


Nursing Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.NURSING Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 105 Number of Observations Used 105 Response Profile Ordered retained_ Total Value fall Frequency 1 1 79 2 0 26 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 119.538 117.659 SC 122.192 138.891 -2 Log L 117.538 101.659 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 15.8789 7 0.0262 Score 15.7078 7 0.0279 Wald 12.4251 7 0.0874 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -5.1402 2.8254 3.3097 0.0689 HIGH_SCHOOL_PERCENTI 1 0.0638 0.0204 9.7696 0.0018 0.4684 SATM 1 0.00429 0.00508 0.7110 0.3991 0.1603 SATV 1 -0.00255 0.00365 0.4869 0.4853 -0.1108 apexam_bio1305 1 -0.4337 0.8665 0.2505 0.6167 -0.0994 apexam_chem1301 1 -0.9086 1.3690 0.4405 0.5069 -0.1335 apexam_eng1302 1 0.7293 0.7458 0.9562 0.3281 0.1918 apexam_mth1321 1 0.4049 1.0272 0.1554 0.6934 0.0806

103


104


Political Science Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.POLITICALSCI Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 117 Number of Observations Used 116 Response Profile Ordered retained_ Total Value fall Frequency 1 1 100 2 0 16 Probability modeled is retained_fall=1. NOTE: 1 observation was deleted due to missing values for the response or explanatory variables. Model Convergence Status Quasi-complete separation of data points detected. WARNING: The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 95.076 94.212 SC 97.830 116.241 -2 Log L 93.076 78.212 WARNING: The validity of the model fit is questionable. Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 14.8641 7 0.0378 Score 11.3198 7 0.1253 Wald 7.1891 7 0.4095 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 1.0210 2.9286 0.1215 0.7274 HIGH_SCHOOL_PERCENTI 1 0.0295 0.0236 1.5619 0.2114 0.2113 SATM 1 0.0114 0.00531 4.5894 0.0322 0.5129 SATV 1 -0.0129 0.00556 5.3518 0.0207 -0.5378 apexam_bio1305 1 -0.0681 1.2739 0.0029 0.9574 -0.0101 apexam_chem1301 1 10.8990 407.6 0.0007 0.9787 1.2257 apexam_eng1302 1 -0.1855 0.6559 0.0800 0.7774 -0.0513 apexam_mth1321 1 11.5372 237.9 0.0024 0.9613 2.2593

105

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits HIGH_SCHOOL_PERCENTI 1.030 0.983 1.079 SATM 1.011 1.001 1.022 SATV 0.987 0.977 0.998 apexam_bio1305 0.934 0.077 11.343 apexam_chem1301 >999.999 <0.001 >999.999 apexam_eng1302 0.831 0.230 3.004 apexam_mth1321 >999.999 <0.001 >999.999 Association of Predicted Probabilities and Observed Responses Percent Concordant 77.6 Somers' D 0.556 Percent Discordant 22.0 Gamma 0.558 Percent Tied 0.4 Tau-a 0.133 Pairs 1600 c 0.778

106


Pre-Business Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.PREBUSINESS Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 638 Number of Observations Used 637 Response Profile Ordered retained_ Total Value fall Frequency 1 1 550 2 0 87 Probability modeled is retained_fall=1. NOTE: 1 observation was deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 509.946 496.663 SC 514.403 541.231 -2 Log L 507.946 476.663 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 31.2834 9 0.0003 Score 27.0903 9 0.0014 Wald 24.3520 9 0.0038 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -4.4613 1.6564 7.2543 0.0071 att_hrs_fall 1 0.0946 0.1261 0.5621 0.4534 0.0628 crsdifffall 1 2.1595 2.9794 0.5253 0.4686 0.0609 HIGH_SCHOOL_PERCENTI 1 0.00478 0.00810 0.3492 0.5545 0.0407 SATM 1 0.00387 0.00189 4.2227 0.0399 0.1671 SATV 1 0.00213 0.00182 1.3722 0.2414 0.0962 apexam_bio1305 1 0.4923 0.8079 0.3714 0.5423 0.0575 apexam_chem1301 1 -1.2088 0.6383 3.5864 0.0583 -0.1218 apexam_eng1302 1 -0.3499 0.2983 1.3752 0.2409 -0.0901 apexam_mth1321 1 1.5292 0.6302 5.8886 0.0152 0.2966

107

Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits att_hrs_fall 1.099 0.858 1.407 crsdifffall 8.666 0.025 >999.999 HIGH_SCHOOL_PERCENTI 1.005 0.989 1.021 SATM 1.004 1.000 1.008 SATV 1.002 0.999 1.006 apexam_bio1305 1.636 0.336 7.971 apexam_chem1301 0.299 0.085 1.043 apexam_eng1302 0.705 0.393 1.265 apexam_mth1321 4.615 1.342 15.868 Association of Predicted Probabilities and Observed Responses Percent Concordant 67.3 Somers' D 0.354 Percent Discordant 31.9 Gamma 0.357 Percent Tied 0.8 Tau-a 0.084 Pairs 47850 c 0.677

108


Psychology and Social Work Majors, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.PSYCHSW Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 198 Number of Observations Used 198 Response Profile Ordered retained_ Total Value fall Frequency 1 1 161 2 0 37 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 192.734 195.668 SC 196.022 221.974 -2 Log L 190.734 179.668 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 11.0655 7 0.1358 Score 11.9525 7 0.1021 Wald 10.9544 7 0.1406 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -1.3149 2.0289 0.4200 0.5169 HIGH_SCHOOL_PERCENTI 1 0.0220 0.0122 3.2360 0.0720 0.1807 SATM 1 0.000645 0.00300 0.0463 0.8297 0.0272 SATV 1 0.00174 0.00289 0.3629 0.5469 0.0711 apexam_bio1305 1 0.2305 0.6461 0.1273 0.7213 0.0375 apexam_chem1301 1 -0.6740 0.7752 0.7560 0.3846 -0.0853 apexam_eng1302 1 -0.1604 0.4178 0.1474 0.7010 -0.0440 apexam_mth1321 1 -1.4767 0.5222 7.9952 0.0047 -0.2926

109


110



The LOGISTIC Procedure Model Information Data Set WORK.UNDECIDED Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 261 Number of Observations Used 261 Response Profile Ordered retained_ Total Value fall Frequency 1 1 209 2 0 52 Probability modeled is retained_fall=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 262.655 272.898 SC 266.219 301.414 -2 Log L 260.655 256.898 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 3.7563 7 0.8074 Score 3.8344 7 0.7986 Wald 3.7624 7 0.8067 Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 0.1498 1.4332 0.0109 0.9168 HIGH_SCHOOL_PERCENTI 1 0.0160 0.0105 2.3245 0.1273 0.1424 SATM 1 0.000328 0.00248 0.0175 0.8948 0.0141 SATV 1 -0.00007 0.00238 0.0008 0.9774 -0.00295 apexam_bio1305 1 0.3836 0.7072 0.2942 0.5875 0.0523 apexam_chem1301 1 -0.1858 0.6263 0.0880 0.7668 -0.0260 apexam_eng1302 1 -0.4667 0.3733 1.5630 0.2112 -0.1235 apexam_mth1321 1 -0.0204 0.4479 0.0021 0.9637 -0.00446

111


112

Appendix D Study of Freshman Gateway Courses Applied to

Retention and Grades Received

Logistic Regression Applied to Fall Retention (Freshman Gateway Courses)

Biology 1305, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.BIO13057 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 643 Number of Observations Used 613 Response Profile Ordered retained_ Total Value fall Frequency 1 1 545 2 0 68 Probability modeled is retained_fall=1. NOTE: 30 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 429.206 317.775 SC 433.624 370.796 -2 Log L 427.206 293.775 R-Square 0.1956 Max-rescaled R-Square 0.3898 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 133.4305 11 <.0001 Score 165.5997 11 <.0001 Wald 83.6367 11 <.0001

113

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -11.7621 8.6460 1.8507 0.1737 satmodel 1 0.00241 0.00182 1.7454 0.1865 0.1579 ACADEMIC_INDEX 1 -0.0264 0.0131 4.0202 0.0450 -0.2521 defic 1 -0.7478 0.3811 3.8508 0.0497 -0.1932 trans 1 -1.3965 0.4721 8.7508 0.0031 -0.2323 TOTAL_CONTACTS 1 0.1230 0.0549 5.0165 0.0251 0.2309 extracurrnum 1 0.2112 0.2054 1.0581 0.3036 0.1026 area_rate 1 5.0827 7.6014 0.4471 0.5037 0.0565 hsc_rate 1 4.7123 4.7389 0.9888 0.3200 0.0757 crsdiffspr 1 5.8299 1.1749 24.6234 <.0001 0.3837 percent_hrs_comp_fal 1 1.1038 0.9165 1.4505 0.2284 0.0829 biograde 1 0.3357 0.1161 8.3567 0.0038 0.3541 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.002 0.999 1.006 ACADEMIC_INDEX 0.974 0.949 0.999 defic 0.473 0.224 0.999 trans 0.247 0.098 0.624 TOTAL_CONTACTS 1.131 1.015 1.259 extracurrnum 1.235 0.826 1.847 area_rate 161.209 <0.001 >999.999 hsc_rate 111.313 0.010 >999.999 crsdiffspr 340.311 34.028 >999.999 percent_hrs_comp_fal 3.016 0.500 18.177 biograde 1.399 1.114 1.756 Association of Predicted Probabilities and Observed Responses Percent Concordant 86.8 Somers' D 0.739 Percent Discordant 12.9 Gamma 0.741 Percent Tied 0.3 Tau-a 0.146 Pairs 37060 c 0.869

114

Appendix D Logistic Regression Applied to Fall Retention (Freshman Gateway

Courses) Biology 1305, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.BIO13055 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 750 Number of Observations Used 725 Response Profile Ordered retained_ Total Value fall Frequency 1 1 602 2 0 123 Probability modeled is retained_fall=1. NOTE: 25 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 662.242 493.173 SC 666.828 557.379 -2 Log L 660.242 465.173 R-Square 0.2359 Max-rescaled R-Square 0.3947 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 195.0688 13 <.0001 Score 219.0095 13 <.0001 Wald 110.4086 13 <.0001

115

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -24.2415 9.5481 6.4459 0.0111 satmodel 1 -8.1E-6 0.00140 0.0000 0.9954 -0.00055 ACADEMIC_INDEX 1 0.00781 0.00878 0.7903 0.3740 0.0739 ps_spr2 1 -0.1320 0.3916 0.1137 0.7360 -0.0257 trans 1 -1.1162 0.3372 10.9553 0.0009 -0.2050 dorm_rate 1 11.7711 8.4334 1.9482 0.1628 0.0924 extracurrnum 1 0.3593 0.1431 6.3060 0.0120 0.2156 area_rate 1 7.4796 5.5381 1.8241 0.1768 0.0893 first_gen_coll_stude 1 -0.4381 0.2817 2.4191 0.1199 -0.0979 perc_mets 1 0.8271 0.5297 2.4379 0.1184 0.1157 minority2 1 -0.1213 0.2740 0.1958 0.6581 -0.0335 crsdiffspr 1 5.7854 0.9644 35.9881 <.0001 0.4149 percent_hrs_comp_fal 1 2.4800 0.8698 8.1307 0.0044 0.2256 biograde 1 0.1787 0.0826 4.6817 0.0305 0.1913 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.000 0.997 1.003 ACADEMIC_INDEX 1.008 0.991 1.025 ps_spr2 0.876 0.407 1.888 trans 0.328 0.169 0.634 dorm_rate >999.999 0.009 >999.999 extracurrnum 1.432 1.082 1.896 area_rate >999.999 0.034 >999.999 first_gen_coll_stude 0.645 0.372 1.121 perc_mets 2.287 0.810 6.458 minority2 0.886 0.518 1.516 crsdiffspr 325.525 49.168 >999.999 percent_hrs_comp_fal 11.942 2.171 65.676 biograde 1.196 1.017 1.406 Association of Predicted Probabilities and Observed Responses Percent Concordant 82.6 Somers' D 0.656 Percent Discordant 17.0 Gamma 0.658 Percent Tied 0.4 Tau-a 0.185 Pairs 74046 c 0.828

116


Courses) English 1302, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.ENGLISH13023 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 1478 Number of Observations Used 1467 Response Profile Ordered retained_ Total Value fall Frequency 1 1 1231 2 0 236 Probability modeled is retained_fall=1. NOTE: 11 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 1296.228 1032.251 SC 1301.519 1101.033 -2 Log L 1294.228 1006.251 R-Square 0.1782 Max-rescaled R-Square 0.3041 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 287.9774 12 <.0001 Score 346.6568 12 <.0001 Wald 197.3639 12 <.0001

117

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -10.6693 4.3818 5.9288 0.0149 satmodel 1 0.00203 0.00104 3.8010 0.0512 0.1163 ACADEMIC_INDEX 1 -0.00477 0.00563 0.7173 0.3970 -0.0513 ps_spr2 1 -0.8450 0.2753 9.4223 0.0021 -0.1445 trans 1 -1.3253 0.2522 27.6037 <.0001 -0.1972 extracurrnum 1 0.2706 0.1324 4.1741 0.0410 0.1074 area_rate 1 -0.9388 3.8524 0.0594 0.8075 -0.0111 hsc_rate 1 8.0408 2.7685 8.4353 0.0037 0.1233 male 1 -0.0522 0.1743 0.0897 0.7645 -0.0142 crsdiffspr 1 5.2487 0.6447 66.2718 <.0001 0.3835 crsdifffall 1 -1.7617 1.1758 2.2450 0.1340 -0.0783 percent_hrs_comp_fal 1 2.6887 0.5967 20.3051 <.0001 0.2419 englishgrade 1 -0.00312 0.0674 0.0021 0.9631 -0.00246 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.002 1.000 1.004 ACADEMIC_INDEX 0.995 0.984 1.006 ps_spr2 0.430 0.250 0.737 trans 0.266 0.162 0.436 extracurrnum 1.311 1.011 1.699 area_rate 0.391 <0.001 743.723 hsc_rate >999.999 13.662 >999.999 male 0.949 0.674 1.336 crsdiffspr 190.313 53.785 673.398 crsdifffall 0.172 0.017 1.721 percent_hrs_comp_fal 14.712 4.569 47.377 englishgrade 0.997 0.873 1.138 Association of Predicted Probabilities and Observed Responses Percent Concordant 78.0 Somers' D 0.566 Percent Discordant 21.4 Gamma 0.569 Percent Tied 0.5 Tau-a 0.153 Pairs 290516 c 0.783

118


Courses) English 1302, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.ENGLISH13023 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 1651 Number of Observations Used 1592 Response Profile Ordered retained_ Total Value fall Frequency 1 1 1315 2 0 277 Probability modeled is retained_fall=1. NOTE: 59 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 1473.532 1113.694 SC 1478.905 1215.776 -2 Log L 1471.532 1075.694 R-Square 0.2201 Max-rescaled R-Square 0.3650 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 395.8384 18 <.0001 Score 448.3605 18 <.0001 Wald 252.4973 18 <.0001

119

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -10.8434 5.4144 4.0108 0.0452 satmodel 1 -0.00042 0.000904 0.2110 0.6460 -0.0260 ACADEMIC_INDEX 1 0.00426 0.00530 0.6453 0.4218 0.0450 ps_fall2 1 -0.8119 0.2140 14.3998 0.0001 -0.1503 ps_spr2 1 -0.5775 0.2781 4.3119 0.0378 -0.1013 SELF_INIT_CNTCTS 1 -0.1829 0.0861 4.5142 0.0336 -0.2545 TOTAL_CONTACTS 1 0.1533 0.0737 4.3279 0.0375 0.2524 trans 1 -1.3786 0.2239 37.9152 <.0001 -0.2402 extracurrnum 1 0.1989 0.1046 3.6144 0.0573 0.0952 init_rate 1 3.9106 3.7878 1.0659 0.3019 0.0473 area_rate 1 -1.7882 3.5933 0.2476 0.6187 -0.0221 hsc_rate 1 3.5906 2.2561 2.5329 0.1115 0.0673 perc_mets 1 0.6976 0.2923 5.6938 0.0170 0.1036 app_span 1 0.0898 0.0486 3.4179 0.0645 0.0826 crsdiffspr 1 4.4172 0.6845 41.6397 <.0001 0.3296 att_hrs_fall 1 0.1055 0.0569 3.4353 0.0638 0.0781 au_change 1 -0.5820 0.2102 7.6682 0.0056 -0.1181 percent_hrs_comp_fal 1 2.0922 0.5924 12.4742 0.0004 0.1811 englishgrade 1 0.0486 0.0617 0.6207 0.4308 0.0387 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.000 0.998 1.001 ACADEMIC_INDEX 1.004 0.994 1.015 ps_fall2 0.444 0.292 0.675 ps_spr2 0.561 0.325 0.968 SELF_INIT_CNTCTS 0.833 0.704 0.986 TOTAL_CONTACTS 1.166 1.009 1.347 trans 0.252 0.162 0.391 extracurrnum 1.220 0.994 1.498 init_rate 49.931 0.030 >999.999 area_rate 0.167 <0.001 191.437 hsc_rate 36.254 0.436 >999.999 perc_mets 2.009 1.133 3.563 app_span 1.094 0.995 1.203 crsdiffspr 82.866 21.662 316.994 att_hrs_fall 1.111 0.994 1.242 au_change 0.559 0.370 0.844 percent_hrs_comp_fal 8.103 2.537 25.873 englishgrade 1.050 0.930 1.185 Association of Predicted Probabilities and Observed Responses Percent Concordant 82.2 Somers' D 0.647 Percent Discordant 17.5 Gamma 0.649 Percent Tied 0.4 Tau-a 0.186 Pairs 364255 c 0.824

120


Courses) Math 1304, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.MATH13044 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 728 Number of Observations Used 721 Response Profile Ordered retained_ Total Value fall Frequency 1 1 586 2 0 135 Probability modeled is retained_fall=1. NOTE: 7 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 697.327 517.061 SC 701.907 581.190 -2 Log L 695.327 489.061 R-Square 0.2488 Max-rescaled R-Square 0.4021 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 206.2652 13 <.0001 Score 222.5206 13 <.0001 Wald 117.5727 13 <.0001

121

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -13.1922 6.4018 4.2466 0.0393 satmodel 1 0.000959 0.00157 0.3737 0.5410 0.0485 ACADEMIC_INDEX 1 -0.00834 0.00869 0.9217 0.3370 -0.0765 ps_fall2 1 -1.4686 0.3036 23.4065 <.0001 -0.2892 ps_spr2 1 0.8541 0.4153 4.2299 0.0397 0.1701 trans 1 -1.4871 0.3638 16.7073 <.0001 -0.2445 extracurrnum 1 0.5522 0.1950 8.0186 0.0046 0.2281 honors_flag2 1 -0.8870 0.5340 2.7595 0.0967 -0.1052 hsc_rate 1 4.9277 3.7171 1.7575 0.1849 0.0774 area_rate 1 4.1579 5.6547 0.5407 0.4622 0.0476 crsdiffspr 1 6.5296 0.9985 42.7681 <.0001 0.4819 BEG_CUM_EARNED_HRS_U 1 0.0373 0.0171 4.7644 0.0291 0.1649 percent_hrs_comp_fal 1 3.3046 0.7625 18.7810 <.0001 0.3618 mathgrade 1 -0.0223 0.0693 0.1033 0.7479 -0.0241 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.001 0.998 1.004 ACADEMIC_INDEX 0.992 0.975 1.009 ps_fall2 0.230 0.127 0.417 ps_spr2 2.349 1.041 5.302 trans 0.226 0.111 0.461 extracurrnum 1.737 1.185 2.546 honors_flag2 0.412 0.145 1.173 hsc_rate 138.062 0.095 >999.999 area_rate 63.937 <0.001 >999.999 crsdiffspr 685.153 96.805 >999.999 BEG_CUM_EARNED_HRS_U 1.038 1.004 1.073 percent_hrs_comp_fal 27.238 6.111 121.411 mathgrade 0.978 0.854 1.120 Association of Predicted Probabilities and Observed Responses Percent Concordant 81.8 Somers' D 0.640 Percent Discordant 17.8 Gamma 0.643 Percent Tied 0.4 Tau-a 0.195 Pairs 79110 c 0.820

122




123

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -0.3994 7.1826 0.0031 0.9557 satmodel 1 0.00122 0.00132 0.8632 0.3528 0.0648 ACADEMIC_INDEX 1 -0.00178 0.00752 0.0563 0.8125 -0.0165 ps_spr2 1 -0.4028 0.3068 1.7233 0.1893 -0.0862 linecamp 1 0.4033 0.2513 2.5749 0.1086 0.1053 trans 1 -1.2314 0.3352 13.4940 0.0002 -0.2080 extracurrnum 1 0.1847 0.1311 1.9846 0.1589 0.0966 AGE 1 -0.6592 0.2620 6.3316 0.0119 -0.1479 hsc_rate 1 2.9191 3.2621 0.8007 0.3709 0.0539 perc_mets 1 0.7767 0.4250 3.3399 0.0676 0.1117 app_span 1 0.1416 0.0646 4.8032 0.0284 0.1268 area_rate 1 3.4888 4.9443 0.4979 0.4804 0.0423 crsdiffspr 1 4.0575 0.9477 18.3293 <.0001 0.2870 au_change 1 -0.3320 0.3023 1.2064 0.2720 -0.0680 percent_hrs_comp_fal 1 2.9798 0.6696 19.8010 <.0001 0.3154 mathgrade 1 0.1486 0.0762 3.8024 0.0512 0.1570 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.001 0.999 1.004 ACADEMIC_INDEX 0.998 0.984 1.013 ps_spr2 0.668 0.366 1.220 linecamp 1.497 0.915 2.449 trans 0.292 0.151 0.563 extracurrnum 1.203 0.930 1.555 AGE 0.517 0.310 0.864 hsc_rate 18.524 0.031 >999.999 perc_mets 2.174 0.945 5.002 app_span 1.152 1.015 1.308 area_rate 32.748 0.002 >999.999 crsdiffspr 57.828 9.025 370.549 au_change 0.717 0.397 1.298 percent_hrs_comp_fal 19.684 5.298 73.131 mathgrade 1.160 0.999 1.347 Association of Predicted Probabilities and Observed Responses Percent Concordant 79.9 Somers' D 0.601 Percent Discordant 19.8 Gamma 0.603 Percent Tied 0.3 Tau-a 0.187 Pairs 90699 c 0.800

124




125

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -5.9118 2.2097 7.1578 0.0075 satmodel 1 0.00151 0.00159 0.9114 0.3397 0.0940 ACADEMIC_INDEX 1 0.000727 0.0103 0.0049 0.9439 0.00653 ps_fall2 1 -0.9483 0.3754 6.3818 0.0115 -0.1628 trans 1 -1.3993 0.3917 12.7638 0.0004 -0.2228 male 1 -0.5648 0.2914 3.7560 0.0526 -0.1554 perc_met 1 0.3752 0.9864 0.1447 0.7037 0.0276 minority2 1 -0.2318 0.2995 0.5987 0.4391 -0.0628 crsdiffspr 1 6.0640 1.1425 28.1704 <.0001 0.3671 percent_hrs_comp_fal 1 2.7726 1.0132 7.4889 0.0062 0.1998 mathgrade 1 0.1276 0.0799 2.5486 0.1104 0.1370 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.002 0.998 1.005 ACADEMIC_INDEX 1.001 0.981 1.021 ps_fall2 0.387 0.186 0.809 trans 0.247 0.115 0.532 male 0.568 0.321 1.006 perc_met 1.455 0.211 10.059 minority2 0.793 0.441 1.427 crsdiffspr 430.108 45.821 >999.999 percent_hrs_comp_fal 16.000 2.196 116.551 mathgrade 1.136 0.971 1.329 Association of Predicted Probabilities and Observed Responses Percent Concordant 79.7 Somers' D 0.599 Percent Discordant 19.8 Gamma 0.602 Percent Tied 0.5 Tau-a 0.139 Pairs 40290 c 0.799

126




127

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -1.6114 1.8901 0.7268 0.3939 satmodel 1 -0.00124 0.00145 0.7299 0.3929 -0.0766 ACADEMIC_INDEX 1 0.00437 0.00931 0.2206 0.6386 0.0399 ps_fall2 1 -1.1777 0.3749 9.8670 0.0017 -0.1942 trans 1 -1.2827 0.3486 13.5402 0.0002 -0.2248 crsdiffspr 1 5.4145 1.3372 16.3960 <.0001 0.2812 percent_hrs_comp_fal 1 1.8441 0.9412 3.8385 0.0501 0.1196 mathgrade 1 0.1067 0.0823 1.6830 0.1945 0.1159 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 0.999 0.996 1.002 ACADEMIC_INDEX 1.004 0.986 1.023 ps_fall2 0.308 0.148 0.642 trans 0.277 0.140 0.549 crsdiffspr 224.632 16.341 >999.999 percent_hrs_comp_fal 6.322 0.999 39.999 mathgrade 1.113 0.947 1.307 Association of Predicted Probabilities and Observed Responses Percent Concordant 73.9 Somers' D 0.486 Percent Discordant 25.3 Gamma 0.490 Percent Tied 0.8 Tau-a 0.095 Pairs 44548 c 0.743

128


Courses) Religion 1310, Fall 2007

The LOGISTIC Procedure Model Information Data Set WORK.REL13104 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 2322 Number of Observations Used 2293 Response Profile Ordered retained_ Total Value fall Frequency 1 1 2006 2 0 287 Probability modeled is retained_fall=1. NOTE: 29 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 1731.327 1300.403 SC 1737.064 1415.156 -2 Log L 1729.327 1260.403 R-Square 0.1849 Max-rescaled R-Square 0.3492 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 468.9236 19 <.0001 Score 554.3403 19 <.0001 Wald 308.5625 19 <.0001

129

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -15.3318 7.5615 4.1113 0.0426 satmodel 1 0.000189 0.000981 0.0372 0.8471 0.0125 ACADEMIC_INDEX 1 -0.00305 0.00556 0.2999 0.5840 -0.0323 ps_spr2 1 -0.0915 0.2774 0.1089 0.7414 -0.0146 ps_fall2 1 -1.0449 0.2115 24.4008 <.0001 -0.1741 defic 1 -0.4713 0.1837 6.5824 0.0103 -0.1144 TOTAL_CONTACTS 1 0.0679 0.0257 6.9983 0.0082 0.1244 dorm_rate 1 6.1199 6.7170 0.8301 0.3622 0.0396 trans 1 -1.2485 0.2296 29.5727 <.0001 -0.1925 extracurrnum 1 0.1832 0.1143 2.5690 0.1090 0.0760 CAMPUS_VISIT 1 0.1432 0.1597 0.8032 0.3701 0.0418 area_rate 1 -0.1445 3.5434 0.0017 0.9675 -0.00169 male 1 0.0721 0.1605 0.2018 0.6533 0.0195 hsc_rate 1 6.1682 2.5265 5.9605 0.0146 0.0950 perc_mets 1 0.8042 0.3039 7.0026 0.0081 0.1070 cbe_eng1302 1 0.7628 0.4468 2.9140 0.0878 0.1288 crsdiffspr 1 6.2275 0.5975 108.6292 <.0001 0.4491 percent_hrs_comp_fal 1 1.9128 0.5431 12.4046 0.0004 0.1473 BEG_CUM_EARNED_HRS_U 1 0.0178 0.00928 3.6826 0.0550 0.0966 relgrade 1 0.0166 0.0544 0.0929 0.7605 0.0160 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 1.000 0.998 1.002 ACADEMIC_INDEX 0.997 0.986 1.008 ps_spr2 0.913 0.530 1.572 ps_fall2 0.352 0.232 0.532 defic 0.624 0.435 0.895 TOTAL_CONTACTS 1.070 1.018 1.125 dorm_rate 454.823 <0.001 >999.999 trans 0.287 0.183 0.450 extracurrnum 1.201 0.960 1.503 CAMPUS_VISIT 1.154 0.844 1.578 area_rate 0.865 <0.001 898.361 male 1.075 0.785 1.472 hsc_rate 477.347 3.375 >999.999 perc_mets 2.235 1.232 4.054 cbe_eng1302 2.144 0.893 5.148 crsdiffspr 506.476 157.024 >999.999 percent_hrs_comp_fal 6.772 2.336 19.633 BEG_CUM_EARNED_HRS_U 1.018 1.000 1.037 relgrade 1.017 0.914 1.131 Association of Predicted Probabilities and Observed Responses Percent Concordant 82.3 Somers' D 0.650 Percent Discordant 17.3 Gamma 0.653 Percent Tied 0.4 Tau-a 0.142 Pairs 575722 c 0.825

130


Courses) Religion 1310, Fall 2008

The LOGISTIC Procedure Model Information Data Set WORK.REL13104 Response Variable retained_fall Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 2547 Number of Observations Used 2520 Response Profile Ordered retained_ Total Value fall Frequency 1 1 2153 2 0 367 Probability modeled is retained_fall=1. NOTE: 27 observations were deleted due to missing values for the response or explanatory variables. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 2093.913 1481.005 SC 2099.745 1597.646 -2 Log L 2091.913 1441.005 R-Square 0.2276 Max-rescaled R-Square 0.4036 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 650.9077 19 <.0001 Score 785.5091 19 <.0001 Wald 386.7948 19 <.0001

131

Analysis of Maximum Likelihood Estimates Standard Wald Standardized Parameter DF Estimate Error Chi-Square Pr > ChiSq Estimate Intercept 1 -13.3192 5.9167 5.0675 0.0244 satmodel 1 -0.00054 0.000768 0.4907 0.4836 -0.0377 ACADEMIC_INDEX 1 -0.00117 0.00511 0.0528 0.8183 -0.0126 ps_spr2 1 -0.3104 0.2468 1.5825 0.2084 -0.0537 ps_fall2 1 -1.0398 0.1894 30.1469 <.0001 -0.1827 TOTAL_CONTACTS 1 0.0267 0.0246 1.1728 0.2788 0.0465 dorm_rate 1 7.9519 5.2942 2.2560 0.1331 0.0568 trans 1 -1.4764 0.1921 59.0723 <.0001 -0.2538 extracurrnum 1 0.2822 0.0915 9.5173 0.0020 0.1397 area_rate 1 1.2816 2.9961 0.1830 0.6688 0.0167 first_gen_coll_stude 1 -0.2627 0.1853 2.0103 0.1562 -0.0498 hsc_rate 1 2.4975 2.0606 1.4690 0.2255 0.0453 perc_mets 1 1.4716 0.4694 9.8278 0.0017 0.2032 perc_metw 1 -0.3553 0.2740 1.6814 0.1947 -0.0912 app_span 1 0.0941 0.0412 5.2205 0.0223 0.0887 crsdiffspr 1 5.6210 0.6492 74.9632 <.0001 0.4095 crsdifffall 1 -3.4421 1.0110 11.5919 0.0007 -0.1549 au_change 1 -0.5755 0.1927 8.9170 0.0028 -0.1109 percent_hrs_comp_fal 1 1.5538 0.5115 9.2289 0.0024 0.1258 relgrade 1 0.1789 0.0485 13.6108 0.0002 0.1728 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits satmodel 0.999 0.998 1.001 ACADEMIC_INDEX 0.999 0.989 1.009 ps_spr2 0.733 0.452 1.189 ps_fall2 0.354 0.244 0.512 TOTAL_CONTACTS 1.027 0.979 1.078 dorm_rate >999.999 0.089 >999.999 trans 0.228 0.157 0.333 extracurrnum 1.326 1.108 1.586 area_rate 3.602 0.010 >999.999 first_gen_coll_stude 0.769 0.535 1.106 hsc_rate 12.152 0.214 689.685 perc_mets 4.356 1.736 10.931 perc_metw 0.701 0.410 1.199 app_span 1.099 1.013 1.191 crsdiffspr 276.167 77.367 985.795 crsdifffall 0.032 0.004 0.232 au_change 0.562 0.386 0.821 percent_hrs_comp_fal 4.729 1.736 12.888 relgrade 1.196 1.087 1.315 Association of Predicted Probabilities and Observed Responses Percent Concordant 84.1 Somers' D 0.687 Percent Discordant 15.4 Gamma 0.690 Percent Tied 0.4 Tau-a 0.171 Pairs 790151 c 0.843

132

Appendix D Ordinary Least Squares Regression Applied

to Grade Received in Course (Freshman Gateway Courses) Biology 1305, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: biograde Number of Observations Read 750 Number of Observations Used 725 Number of Observations with Missing Values 25 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 6 718.81398 119.80233 42.81 <.0001 Error 718 2009.46740 2.79870 Corrected Total 724 2728.28138 Root MSE 1.67293 R-Square 0.2635 Dependent Mean 3.53655 Adj R-Sq 0.2573 Coeff Var 47.30404 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 -6.25469 0.89620 -6.98 <.0001 0 att_hrs_fall 1 -0.02934 0.05527 -0.53 0.5957 -0.02089 crsdifffall crsdifffall 1 0.65054 1.20242 0.54 0.5887 0.02113 HIGH_SCHOOL_PERCENTILE 1 0.03368 0.00489 6.88 <.0001 0.22610 SATM 1 0.00730 0.00097375 7.50 <.0001 0.28769 SATV 1 0.00414 0.00099927 4.14 <.0001 0.15794 apexam_bio1305 1 0.39174 0.15127 2.59 0.0098 0.08485

133


to Grade Received in Course (Freshman Gateway Courses) Chemistry 1301, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: chegrade Number of Observations Read 626 Number of Observations Used 610 Number of Observations with Missing Values 16 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 6 747.80124 124.63354 45.35 <.0001 Error 603 1657.19712 2.74825 Corrected Total 609 2404.99836 Root MSE 1.65779 R-Square 0.3109 Dependent Mean 3.80164 Adj R-Sq 0.3041 Coeff Var 43.60713 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 -7.85206 0.94672 -8.29 <.0001 0 att_hrs_fall 1 -0.00742 0.06086 -0.12 0.9029 -0.00515 crsdifffall crsdifffall 1 1.90294 1.34369 1.42 0.1572 0.05916 HIGH_SCHOOL_PERCENTILE 1 0.04589 0.00547 8.39 <.0001 0.29584 SATM 1 0.01041 0.00110 9.44 <.0001 0.38213 SATV 1 0.00017669 0.00102 0.17 0.8625 0.00722 apexam_chem1301 1 0.58420 0.18651 3.13 0.0018 0.10714

134


to Grade Received in Course (Freshman Gateway Courses) English 1302, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: enggrade Number of Observations Read 1651 Number of Observations Used 1635 Number of Observations with Missing Values 16 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 6 345.85404 57.64234 30.56 <.0001 Error 1628 3070.45789 1.88603 Corrected Total 1634 3416.31193 Root MSE 1.37333 R-Square 0.1012 Dependent Mean 4.80734 Adj R-Sq 0.0979 Coeff Var 28.56733 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 0.58479 0.46320 1.26 0.2069 0 att_hrs_fall 1 0.05991 0.02870 2.09 0.0370 0.05557 crsdifffall crsdifffall 1 -0.78448 0.47637 -1.65 0.0998 -0.04320 HIGH_SCHOOL_PERCENTILE 1 0.01944 0.00246 7.92 <.0001 0.20148 SATM 1 0.00099855 0.00049212 2.03 0.0426 0.05485 SATV 1 0.00281 0.00054593 5.15 <.0001 0.13927 apexam_eng1302 1 0.08041 0.07469 1.08 0.2819 0.02671

135


to Grade Received in Course (Freshman Gateway Courses) Math 1304, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: mathgrade Number of Observations Read 784 Number of Observations Used 744 Number of Observations with Missing Values 40 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 5 536.25509 107.25102 30.26 <.0001 Error 738 2615.30942 3.54378 Corrected Total 743 3151.56452 Root MSE 1.88249 R-Square 0.1702 Dependent Mean 3.47581 Adj R-Sq 0.1645 Coeff Var 54.15989 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 -6.46882 1.10203 -5.87 <.0001 0 att_hrs_fall 1 0.00664 0.06028 0.11 0.9123 0.00434 crsdifffall crsdifffall 1 0.23110 1.26109 0.18 0.8546 0.00716 HIGH_SCHOOL_PERCENTILE 1 0.03237 0.00482 6.71 <.0001 0.22729 SATM 1 0.01341 0.00141 9.54 <.0001 0.35097 SATV 1 -0.00116 0.00105 -1.10 0.2723 -0.04099

136


to Grade Received in Course (Freshman Gateway Courses) Math 1321, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: mathgrade Number of Observations Read 709 Number of Observations Used 676 Number of Observations with Missing Values 33 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 5 386.95850 77.39170 23.22 <.0001 Error 670 2232.80925 3.33255 Corrected Total 675 2619.76775 Root MSE 1.82553 R-Square 0.1477 Dependent Mean 3.61686 Adj R-Sq 0.1413 Coeff Var 50.47267 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 -5.93707 1.14119 -5.20 <.0001 0 att_hrs_fall 1 0.04279 0.06541 0.65 0.5132 0.02871 crsdifffall crsdifffall 1 0.43494 1.37953 0.32 0.7526 0.01379 HIGH_SCHOOL_PERCENTILE 1 0.04080 0.00540 7.56 <.0001 0.27548 SATM 1 0.00772 0.00124 6.23 <.0001 0.24633 SATV 1 0.00052872 0.00104 0.51 0.6128 0.02036

137


to Grade Received in Course (Freshman Gateway Courses) Religion 1310, Fall 2008

The REG Procedure Model: MODEL1 Dependent Variable: relgrade Number of Observations Read 2547 Number of Observations Used 2519 Number of Observations with Missing Values 28 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 5 1392.04317 278.40863 110.48 <.0001 Error 2513 6332.72380 2.51999 Corrected Total 2518 7724.76697 Root MSE 1.58745 R-Square 0.1802 Dependent Mean 4.15204 Adj R-Sq 0.1786 Coeff Var 38.23288 Parameter Estimates Parameter Standard Standardized Variable Label DF Estimate Error t Value Pr > |t| Estimate Intercept Intercept 1 -2.61498 0.42326 -6.18 <.0001 0 att_hrs_fall 1 0.03794 0.02613 1.45 0.1467 0.02914 crsdifffall crsdifffall 1 -0.03408 0.42581 -0.08 0.9362 -0.00159 HIGH_SCHOOL_PERCENTILE 1 0.03103 0.00234 13.25 <.0001 0.25433 SATM 1 0.00157 0.00047735 3.30 0.0010 0.07174 SATV 1 0.00467 0.00046816 9.98 <.0001 0.21656

138

References

Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25, 297-308.

Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of

student attrition. Research in Higher Education, 12, 155-187. Kuh, George D., Jillian Kinzie, John H. Schuh, Elizabeth J. Whitt and Associates. (2005).

Student Success in College: Creating Conditions that Matter. Washington D.C.: Jossey-Bass.

Lotkowski, V. A., Robbins, S. B., & Noeth, R. J. (2004). The role of academic and non-

academic factors in improving college retention. Iowa City, IA: ACT Policy Report.

Reason, R. D. (2009). Student variables that predict retention: Recent research and new

developments. NASPA Journal, 46, 482-501. St. John, E. P., Paulsen, M. B., & Starkey, J. B. (1996). The nexus between college

choice and persistence. Research in Higher Education, 37, 175-220. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition.

(2nd. ed.). Chicago: The University of Chicago Press. Tross, S. A., Harper, J. P., Osher, L. W., & Kneidinger, L. M. (2000). Not just the usual

cast of characteristics: Using personality to predict college retention. Journal of College Student Development, 41, 323-334.

What Works in Student Retention—Four-Year Private Colleges (2004). Iowa City: ACT,

Inc. What Works in Student Retention—Four-Year Private Colleges (2010). Iowa City: ACT,

Inc.

bu retention report summer 2010

Documents