fun paper 1 fall 2011 instructions

PSY 102 — Psychology in the Modern WorldInstructor: Bob Melara

Fall 2011

FUN PAPER # 1Due Date: Monday, October 3 rd , 2011, by 5 pm on **Blackboard**

Critical Thinking using Psychological Science

The Learning Alliance for Higher Education, an educational consulting firm based at the University of Pennsylvania, was hired by City College in 2011 to investigate and make recommendations for improving undergraduate retention and graduation at the College. Even though most City College students receive financial assistance, have decent high school grades, and live at home with their parents – factors that should contribute to good graduation rates – in fact, currently only 7% of students admitted to the College graduate from it in four years. Only 36% graduate in six years. Indeed, roughly half of the students admitted drop out completely within two years. Students who transfer to City College from another school, either inside or outside the CUNY system (e.g., a CUNY community college), disappear even faster: Half leave the College, and half of those leave by their first year at the College.

The goal of Fun Paper #1 is to use your critical thinking skills to evaluate the consultant’s report and consider hypotheses for explaining and improving the low City College graduation rate. We want you to write a paper that considers the strengths and weaknesses of the evidence and arguments, provides interpretations, and reaches your own conclusions using psychological science. Begin by reading the report, which is included at the bottom of this assignment.

First, title your paper “A Critical Examination of Retention and Dropout at City College”. Next: FOLLOW EACH OF THE FOLLOWING FIVE INSTRUCTIONS EXACTLY (The following is a detailed outline on how you should write this paper):

Your paper should consist of five paragraphs corresponding to the 5 questions below. DO NOT write an outlined paper: It needs to be in essay format. Within each paragraph, please be clear on which letter you are answering by placing a bold letter in front of the sentences. If you are answering “1a” place a letter “a” before the sentence/s. (Here’s an example: The purpose of this paper is to evaluate a summary of a report conducted by City College to make recommendations for student admission to the College. I found several strengths in this report. 1 a. One of the most convincing statements by the report’s author was…)

1. Begin the first paragraph of the paper with these sentences: “The purpose of this paper is to evaluate a report conducted by The Learning Alliance to investigate student retention at City College. I found several findings from this report helpful in illuminating the retention problem.”

a. Which combinations of ethnicity and gender are most vulnerable to becoming college dropouts at City College? Which combinations of ethnicity and gender are least vulnerable to becoming college dropouts? Develop one hypothesis for why certain ethnic/gender groupings tend to drop out. (5 pts).

b. Describe the correlation between when someone is admitted to the College and the tendency to drop out? Has this correlation increased, decreased, or stayed the same between 2004 and 2006? Suggest one interpretation of this correlation and its trend. (5 pts).

c. City College dropout rates appear to depend in part on where someone originally comes from: the city, the state, or outside the country (which could include both the documented and the undocumented). How does where you come from affect dropout? Develop one hypothesis for why place of origin affects retention. (5 pts).

2. Begin the second paragraph of the paper with this sentence: “The retention problem may be due in part to the background preparation of students for college.”

a. Describe the relationship between retention at City College and scores on pre-admission indices such as high school grades and SAT scores. What do these indices and this relationship suggest is one reason why City College students drop out in such great numbers? (4 pts).

b. Describe the relationship between retention at City College and the numbers of courses students take and receive credit for each semester. Why would the number of courses taken affect retention? (4 pts).

c. Use the relationships you have described in the second paragraph to develop a hypothesis about the role of background preparation for college in explaining dropout. (5 pts).

3. Begin the third paragraph of the paper with these sentences: “The retention problem may also be due in part to the reasons students come to study at City College, which has a lower retention and graduation rate than other senior colleges within CUNY. For example, many students come to City College to become engineering or pre-med majors.”

a. Describe the relationship between students’ preferences for CUNY colleges and retention. (4 pts). b. How do engineering or pre-med majors fare here compared with other majors at the College? (4 pts).c. Describe how student preferences and area of major might jointly explain low retention at City College

(hint: return to your hypothesis about background preparation for college). (5 pts).

4. Begin the fourth paragraph of the paper with these sentences: “One limitation of the report by The Learning Alliance is in the research strategy they used, which focused on associations between retention rates and a set of academic factors such as demographics and performance.”

a. Name three variables not considered in this report that you think would have a strong relationship with retention at City College. For each variable, describe how you would collect the data and what relationship to retention you hypothesize. (6 pts).

b. Explain the weakness in the research strategy used by The Learning Alliance. Why is it difficult to explain the high dropout rate at City College when relying exclusively on the relationships among variables (6 pts).

c. What alternative research strategy would you recommend that obviates the problems of the one used by The Learning Alliance. Why is your recommended research strategy better? (6 pts).

5. Suppose you hypothesize from The Learning Alliance report that the high dropout rate at City College might be alleviated if at-risk students could be identified early with immediate intervention. Bob agrees to test your hypothesis using the current class of students enrolled in PSY 102. You divide students in the class into two groups: (1) Intervention Group: Sections in which the teaching assistants meet individually each week with any student who misses a class or an assignment; and (2) Baseline Group: Sections in which teaching assistants post grades and absences on Blackboard, but don’t meet specially with at-risk students.

Begin the final paragraph of the paper with this sentence: “I have designed a study to test a hypothesis intended ultimately to improve the retention rate at City College.”

a. Describe the study, including how and when you plan to measure retention and how you plan to control for any preexisting differences between the groups. (3 pts).

b. How can you tell whether any improvement in retention in the Intervention Group is due to at-risk students getting more attention from teaching assistants, developing better college learning skills, or something else entirely? How would you control for the different alternative explanations? (7 pts).

c. Describe the statistical test you would perform to test the difference in retention between the two groups. What is the numerator of your statistical test? The denominator? (4 pts).

d. Create a chart in Excel to show what you expect to find. Label the independent and dependent variables. Paste the chart into your fun paper. Write a concluding statement that summarizes your results from the chart and their implications for students entering City College this year. (7 pts).

A fifth of your grade will be based on the following:a. Effective written communication (4 pts)b. Critical thinking and logical reasoning ability (4 pts)c. Ability to formulate questions, hypotheses, and research designs (4 pts)d. Proper use of psychological concepts and theories (4 pts)e. Competence in quantitative reasoning and analysis of research findings (4 pts)

Due by 5:00 pm on MONDAY, OCTOBER 3 rd . Late papers will not be accepted.

All papers need to be submitted electronically using the Assignment section in Blackboard (click on YOUR SECTION, then click on Course Tools → go to assignments → go to fun papers → click on the link corresponding to Fun Paper #1. Once there, scroll down and where it says “Attach local file” browse your computer for the finished paper and add it. Then click submit, and you are done).

With the exception of the instructed sentences, the entire paper must be in your own words, in essay format and typewritten (double spaced) using Microsoft Word.

Quoted, paraphrased, or borrowed sentences or phrases are not allowed. DO NOT USE ANY OF THE TEXT FROM THE LEARNING ALLIANCE REPORT, EVEN IN QUOTES. These will be regarded as plagiarism, which will be penalized by a zero on the assignment and a report filed with the Office of the Academic Integrity Official. Plagiarism software will be used to analyze your paper prior to grading.

Do not use external references outside of lecture notes, the retention report, and the textbook. The paper should not exceed 4 pages.

TOWARD UNDERSTANDING PERSISTENCE

A Report on Undergraduate Retention at

The City College of New York

submitted by

The Learning Alliance for Higher Education at the University of Pennsylvania

April 2011

The Learning Alliance 1

Undergraduate Retention

The Issue

The City College of New York (CCNY), concerned about its ability to retain andgraduate the students who enter as full-time undergraduates, asked The Learning Alliance to conduct a study of student retention. Just about half of the first-time full-timefreshmen leave CCNY before completing a degree, and nearly half of the students who enter as full-time transfer students stop attending before they finish their courses of study.This report examines the factors that contribute to the non-persistence at CCNY. It focuses more specifically on who leaves, when they leave, and what appears to causethem to leave.

The Data

For the analysis, CCNY provided the records for all 14,428 students who started CCNY as full-time undergraduates in fall 2004 though fall 2009 (Admissions Files). Consisting of data for 9,245 freshmen and 5,183 transfer students, the file includes demographic and admissions information. (See Appendix B for the data elements.)

In addition, CCNY provided academic profiles of all enrolled undergraduates for every semester from fall 2004 through spring 2010 (Academic Files). These files were merged with the Admissions Files so that each entering student has a profile of his or her experience at CCNY. The information in the Academic Files includes grades and credits, among other data. (See Appendix C for data elements.)

A file containing all undergraduates who received bachelor’s degrees from CCNY between spring 2005 and spring 2010 enabled us to flag those who had completed their degrees, and a list of those enrolled in fall 2010 allowed us to flag those who were continuing to pursue a degree.

The Analysis

The analysis is divided into two parts: freshmen and transfers. For freshmen there is good information about academic preparation, with high school GPA and SAT scores for most incoming students. For transfers there is information about the institutions from which they transferred and the credits1 they carried forward to CCNY. The freshmenanalysis comprises matriculating students from fall 2004-2006, while the transfer analysis includes students who entered in fall 2007 as well. In addition to statistical profiles and statistical significance tests of the differences between students who failed to continue or complete their studies and those who did continue or complete their studies, logistic

1As will be discussed later in this report, the transfer credits were not recordedconsistently.


regression models were built to help quantify the odds of a student with a particular profile failing to be retained.

FRESHMEN

Highlights

• Half of all entering freshmen stop attending CCNY. Freshmen who fail to persist tend to do so early: about one-third of the non-persisters are off the rolls in or after the first year, two-thirds of all non-persisters stop attending by the end of the second year.

• Freshmen who stop attending begin to develop academic problems in the first semester. Those students earn fewer credits on average than persisting students and have significantly lower GPAs on average than those who persist, and particularly those who graduate.

• The later the admissions phase in which a freshman is admitted, the more likely he or she is to stop attending.

• Freshmen who chose CCNY as their first choice school are more likely to persist.

• Freshmen who persist for at least four semesters, but ultimately leave without a degree, attend school part-time in a larger proportion of semesters than do students who continue to persist.

• Freshmen who select a math-based STEM major (excluding those in the biological sciences) are somewhat more likely to be non-persisters.

• SAT scores are correlated with academic performance, so it is no surprise that students with lower entering SAT scores, on average, are less likely to persist.

• Similarly, students with lower high school grade point averages are less likely to persist.

General Findings

Any freshman that matriculated as a full-time student at CCNY in the fall of 2004, 2005, or 2006 is included in this analysis. Students are considered “Not Enrolled,” that is, non- persisters, if they did not enroll in fall 2010. If they are included in a list of graduates from 2004 through 2010, then they are considered “Graduated.” Everyone else is “Still Enrolled.”

As Figure 1 shows, more than half of all students who enrolled as freshmen in 2004 and2005, and nearly half of those who entered in 2006 left CCNY before completing their degrees. Because students tend to take more than four years to complete their programs,


the data for the students who entered in 2006 is less complete than the data for 2004 and2005. It can be expected that a number of those who are still enrolled will be off the rollsbefore they can graduate.

Figure 1. Full-time Freshmen by Status as of Fall 2010

Fall of First Freshman EnrollmentF2004 F2005 F2006

Not Enrolled 612 665 698Still Enrolled 105 246 718Graduated 451 367 113Total 1168 1278 1529% Non-Persisting 52% 52% 46%

Freshmen who fail to persist tend to leave CCNY early in their academic careers. Among those who leave CCNY, between 8 and 11 percent are gone after just one semester. For example, of the 612 freshmen that entered CCNY in fall 2004, but did not persist, 62 or10.1% attended for no more than one semester. At the end of two semesters around one- third of those who ultimately leave are not registered, and after only two years the vast majority—around two-thirds of those who ultimately drop out—are no longer registered.

Figure 2. Distribution of Non-Persisting Freshmen by Semesters Attended BeforeLeaving CCNY

SemestersEnrolled

Fall of First Freshman EnrollmentCumulative Number No Longer Enrolled

F2004 F2005 F20061 62 55 772 199 212 2533 288 316 3544 406 439 484

5 or more 612 665 698

SemestersEnrolled

Cumulative Percent of All Non-Persisters

F2004 F2005 F20061 10.1% 8.3% 11.0%2 32.5% 31.9% 36.2%3 47.1% 47.5% 50.7%

4 66.3% 66.0% 69.3%5 or more 100.0% 100.0% 100.0%

Demographics

The demographic profile of freshmen who stop attending reflects the conventional wisdom: men are more likely to be non-persisters than are women, and traditionally underrepresented minorities—black and Hispanic freshmen (who are nevertheless not underrepresented at CCNY)—are more likely to stop attending than are others. The differences between men and women, across ethnic groups, and citizenship, are statistically significant every year.

Figure 3A. Percent of Freshmen Who Did Not Persist by Gender

Fall of First Freshman Enrollment

Gender

F2004

Total % NotFreshman Enrolled

Cohort

F2005


Cohort

F2006


CohortFemale

Male

531 48.2%

637 55.9%

592 48.1%

686 55.4%

760 43.7%

769 47.6%

p = <.0001 <. 003 <.02

Figure 3B. Percent of Freshmen Who Did Not Persist by Ethnicity

Fall of First Freshman EnrollmentF2004

Total % NotNumber Enrolled

F2005


F2006


Asian Black Hispanic White

305 49.2%318 56.3%387 53.5%

158 48.1%

325 40.0%313 55.9%460 57.6%

179 52.5%

378 42.3%337 44.5%573 49.4%

241 43.6%

p = 0.03 <.0001 0.09

Figure 3C. Percent of Freshmen Who Did Not Persist by Ethnicity and Citizenship Status

Fall of First Freshman EnrollmentF2004


F2005Total % Not

Number Enrolled

F2006Total % Not

Number Enrolled

Asian Black HispanicNon-U.S. Citizen

White

252 48.4%270 60.0%357 53.8%152 46.7%

137 47.4%

263 42.2%278 57.2%419 58.5%153 38.6%

164 54.9%

316 41.1%303 45.2%511 51.7%175 38.9%

224 44.2%

p= 0.011 <.0001 0.028

Figure 4. Percent of Freshmen Who Did Not Persist by Residency

Fall of First of Freshman EnrollmentF2004


F2005Total % Not

Number Enrolled

F2006Total % Not

Number EnrolledNew York City New York State Non-U.S. Citizen

U.S.A.*

927 52.9%

60 51.7%

152 46.7%29 69.0%

1019 54.5%

78 42.3%

153 38.6%28 64.3%

1199 47.4%

107 37.4%

175 38.9%48 45.8%

p = NS 0.0007 0.005*small numbers

When ethnicity, citizenship, and gender are combined the group that stands out for high persistence across entering years is female non-U.S. citizen. For students who entered in Fall 2004, the ones who were by far most likely not to be retained were (surprisingly) Asian-American and male, while the least successful freshmen that entered in Fall 2005 and 2006 were male and Hispanic.

Admissions Considerations

Freshman admission at CCNY occurs in phases by date from early to late. Freshmen who were admitted in the earliest admission phases are the most likely to be retained. Figure 5 shows the increasing percentage of non-enrolled as students are admitted in each subsequent band of “Phases”. Note, however, that the largest proportion of students is admitted in the earliest phases.

Figure 5. Percent of Freshmen Who Did Not Persist by Admissions’ Phase

Fall of First of Freshman Enrollment

Phase

F2004


F2005


F2006

Total Number % NotEnrolled

Phases 1-3Phases 4-6

Phases 7-9Phases Alpha(10 or higher)

382 50.8%443 49.4%

167 56.3%95 67.4%

683 46.0%323 58.2%

85 57.6%101 66.3%

617 38.7%515 48.5%

188 51.1%93 65.6%

p = 0.0188 <.0001 <.0001

Freshmen admitted under the special SEEK program are also less likely to persist than those admitted under regular admission. SEEK students enter with substantially lower high school grades and SAT scores on average than regular admission students. As will be demonstrated, grades and SAT scores predict persistence.

Figure 6. Percent of Freshmen Who Did Not Persist by SEEK Status

Fall of First of Freshman Enrollment

SpecialAdmissions

F2004Total % Not

Number Enrolled

F2005Total % Not

Number Enrolled

F2006Total Number % Not

Enrolled

RegularSEEK

903 50.6%265 58.5%

992 49.8%286 59.8%

1243 44.3%286 51.4%

p = 0.004 0.002 0.004

Grades are generally considered one of the strongest predictors of success in college. A “College Admission Average” based on high school performance was available for 86 percent of students in the analysis. The overall average provides a good predictor of persistence at CCNY as do the averages for Math and English. Those who enter with the lowest grades are the least likely to complete a degree. The overall and Math data for2005 and 2006 are strictly monotonic—the means increase as students are classified as not enrolled, still enrolled, and graduated. (In addition, the 10th and 90th percentiles—not shown below—follow similar patterns.)

Figure 7. Mean Freshman College Admission Average by Persistence

Mean College Admission Average-Overall

Fall Freshman Entering Year

F2004 F2005 F2006

Not Enrolled Still Enrolled Graduated

80.780.685.0

80.581.885.0

80.882.985.2

p = <.0001 <.0001 <.0001

Mean College Admission Average-English

Fall Freshman Entering YearF2004 F2005 F2006

Not Enrolled Still Enrolled Graduated

77.176.582.5

80.079.982.4

79.582.284.4

p = <.0001 0.054 0.002

Mean College Admission Average-Mathematics

Fall Freshman Entering YearF2004 F2005 F2006

Not Enrolled

Still EnrolledGraduated

76.4

72.883.1

77.0

79.583.2

77.4

80.083.1

p = <.0001 <.0001 0.0001

SAT scores are strong predictors of academic persistence among CCNY freshmen. Those with the lowest math, verbal, and total SAT scores, on average, are most likely to drop out, while those with the next lowest scores take longer to complete their studies. Students with the highest scores are the most likely to graduate.

Figure 8. Mean SAT Scores of Freshmen by Persistence

Fall of First Freshman EnrollmentMean SAT Total F2004 F2005 F2006

Not Enrolled 948.7 948.9 946.0

Still Enrolled 955.7 977.4 981.3Graduated 1049.4 1060.6 1048.4

p = <.0001 <.0001 <.0001

Mean SAT Math Not Enrolled 494.5 494.1 488.2Still Enrolled 498.2 510.1 512.2Graduated 547.3 553.3 534.0

p = <.0001 <.0001 <.0001

Mean SAT Verbal Not Enrolled 454.2 454.8 457.9Still Enrolled 457.6 467.3 469.1Graduated 502.1 507.3 514.4

p = <.0001 <.0001 <.0001

Performance

It is possible to identify students at risk of leaving CCNY early in their academic careers. Many students who fail to persist begin to lose ground in the first semester and continue to fall behind if they stay beyond the first. Two key indicators, therefore, are the cumulative credits and the cumulative grade point average of the early terms. Since all the students in the study matriculated full-time, those with less than a full semester of credits in the first term either have had to drop courses or have failing grades. As Figure9 shows, those who fail to persist have, on average, 5 to 7 fewer credits after one term, and 8 to 12 fewer credits after two terms compared to students who graduate. Those who are still enrolled have fallen behind in credits as well, but not to the same extent as those who drop out.

Freshmen Entering Fall 2004

Fall 2004 Credits Mean CreditsEarned Cumulative

Not Enrolled 10.5

Still Enrolled 12.0Graduated 15.2

Spring 2005 Mean CreditsCredits Earned Cumulative

Not Enrolled 20.5


p = <.0001 <.0001



Not Enrolled 10.0Still Enrolled 12.1Graduated 15.6



p = <.0001 <.0001






p = <.0001 <.0001

Figure 9. Cumulative Credits After One and Two Semesters by Persistence

PROGRESS

The cumulative GPA earned at CCNY tells a story parallel to that of cumulative credits: those with the lowest GPAs, on average, are most likely to drop out. Those in the middle continue more slowly toward graduation, and those with the best grades have the best chance of graduating in a timely fashion. Once again, the trend in grades, like the trendin credits, can be found early in a student’s career and may be a marker for potential non-persistence. Later in this report, we discuss two logistic regressions that predict the odds of not persisting for students with particular characteristics, and we provide an assessment of what a change in grades or credits (high school grades and SAT scores in another model) might mean for the likelihood of staying in school.


Fall 2004 Mean CumulativeGPA Earned GPA

Not Enrolled 2.57Still Enrolled 2.64

Graduated 3.16

Spring 2006 GPA Mean CumulativeEarned GPA

Not Enrolled 2.07Still Enrolled 2.38

Graduated 3.07

p = <.0001 <.0001


Fall 2005 GPA Mean CumulativeEarned GPA

Not Enrolled 2.52



Not Enrolled 2.32


p = <.0001 <.0001


Fall 2006 GPA Mean CumulativeEarned GPA




p = <.0001 <.0001

Figure 10. Cumulative CCNY GPA After One and Two Semesters by Persistence

GRADES

Finally, an examination of the declared majors of students shows that those who are pursuing the quantitative STEM programs: engineering, math, computer and physical sciences (in this analysis, biological sciences have not been included in quantitative STEM) are often those who are less likely to persist than their peers. In Figure 11 students with majors that map into the STEM categories are compared with those who have any other major. For the purpose of this analysis students with majors labeled “Waiting,” “Pending,” or “Gateway” are included in that major. (Pending “Science,” which may include biology, is considered STEM for this analysis.)


Figure 11. Declared First Major of First Term


MAJOR STEM Other MajorsNot Enrolled 307 305Still Enrolled 45 60Graduated 188 263

Total 540 628% Not Enrolled 56.9% 48.6%


STEM Other Majors

Not Enrolled 325 340Still Enrolled 113 133Graduated 150 217Total 588 690% Not Enrolled 55.3% 49.3%


STEM Other Majors

Not Enrolled 311 387

Still Enrolled 289 429Graduated 42 71Total 642 887% Not Enrolled 48.4% 43.6%

Freshman Predictive Models

Several logistic regression models were constructed to provide a way to estimate the impact of students’ characteristics on their chances of not persisting or persisting. Two successful models are shown here: one considers admissions variables to ascertain markers for non-persistence; the second looks at CCNY performance variables. Both the “Admissions” model and the “Performance” model also include demographic characteristics.

The dependent variable in both models is student persistence (specifically, the odds of not persisting versus persisting2.) The selection of explanatory variables comes from the earlier analysis that identified characteristics that distinguish the population of students who were not enrolled from those who were retained. The models use the combinedyears profiled in the text of the report: fall 2004-2006 cohorts of full-time freshmen.

2Technically, the dependent variable is the logarithm of the odds of the ratio of astudent’s not persisting to persisting: log odds not persist/persist.

V aria b l e Ch a ng e i n Odd s R ati o

First Term GPA The lower the CCNY GPA, the greater the odds of not persisting vs. persisting.

First Term Credits The lower the number of credits earned, the greater the odds of not persisting vs. persisting.

Gender Set to Female versusMale

Odds of not persisting vs. persisting are lower for females.

The key variables in the “Admissions” model are the Calculated Admission Average (CAA)––that is the CUNY calibration of the student’s high school GPA, the math and verbal SAT scores, and whether a student was admitted in the first three admissions phases or later. The lower an applicant’s high school grades and SAT scores, the greater the odds that the student will not persist. The later a student is admitted, the greater the odds of his or her not completing the degree. In addition, applicants who are female or Asian are more likely to persist than are others.

Figure 12 shows the general impact of explanatory variables on the change in the odds of leaving without a degree versus persisting. The statistical details of the model are provided in Appendix A.

Figure 12. Admissions Model: Odds Ratio of Not Persisting/Persisting


College Admissions Average (CUNYcalibrated HS GPA)

The lower the CCA, the greater the odds of not persisting versus persisting.

SAT Math The lower the SAT M, the greater the odds of not persisting versus persisting.

SAT Verbal The lower the SAT V, the greater the odds of not persisting versus persisting.

Admissions Phase – Admitted after the first 3 phases

The odds of not persisting versus persisting are higher if admitted in phase 4 or later.

Gender Set to Female versus Male Odds of not persisting versus persisting are lower for females.

Non-US Citizen vs. citizen Odds of not persisting versus persisting are lower forNon-US Citizens.

Ethnicity Asian vs. other ethnicity Odds of not persisting versus persisting are lower forAsians.

A second model takes into account only performance at CCNY and gender. This model shows that higher early GPAs and higher credit accumulation predicts greater odds of persisting versus not persisting. Again, males have lower odds of persisting than dofemales. Figure 13 below summarizes the findings, the details can be found in Appendix A.

Figure 13. Performance Model Odds: Ratio of Not Persisting/Persisting

Beating the Odds

The logistic regression models discussed above estimate the impact of students’ characteristics on the odds that they will not persist versus persist at CCNY. Clearly the models cannot predict success or lack of success with certainty. In fact, the best models discussed in this report predicted actual enrollment status correctly between two-thirds and three-quarters of the time. Can we learn anything about those who are predicted to leave CCNY without a degree, but defy the odds and persist?

To try to understand who defies the odds, an admissions model similar to—but not identical to—the one discussed earlier was run using a subset of the student records. The subset comprised a random sample of about one-half of the students chosen from the full set of records.3

The table below shows the explanatory variables in the model and their general impact on the change in the odds of leaving without a degree versus persisting.

Figure 14. Admissions Model: Odds Ratio of Not Persisting/PersistingBased on a Random Sample of Records


College Admissions Average (CUNY calibrated HS GPA)

The lower the CCA, the greater the odds of not persisting versus persisting.

SAT Math The lower the SAT M, the greater the odds of not persisting versus persisting.

Admissions Phase – Admitted after the first 3 phases The odds of not persisting versus persisting are higher if admitted in phase4 or later.

Gender Set to Female versus Male Odds of not persisting versus persisting are lower for females.

Non- US Citizen vs. citizen Odds of not persisting versus persisting are lower for Non US Citizens.

Ethnicity Asian vs. other ethnicity Odds of not persisting versus persisting are lower for Asians.

Applying the model’s parameter estimates to the data for students that were not in the sample it is possible to identify those entering freshmen whom the model predicts to persist and those who are predicted to leave without completing a degree. Those who

3Each record of a freshman that entered CCNY in the falls 2004 through 2006 wasassigned a random number from 0 to 1 using a uniform random number generator. Thesample consists of those students whose random number was less than 0.5.

were predicted to leave but actually persisted most often (more than half the time) had the following characteristics:

• They earned a GPA of 3.0 or higher in their first semester.

• They earned more than twelve credits in their first semester.

It is difficult to see patterns among majors because many students who leave CCNY do so early in their academic careers, before they are admitted into a major, and there are some major groups, such as architecture, business, and education, for which the numbers of students are too small to draw sound conclusions. That said, those students who are “pre”-engineering (waiting, pending, gateway) and those who major in engineering, would appear to be the least likely to complete a degree.

Details

As Figure 15 shows, the higher the first term GPA, the more likely a freshman is to persist even if his or her admissions characteristics predict a greater than even likelihood of not persisting. Those who earn a GPA of 3 or higher in their first semester are more likely to stay in school at CCNY than those who have lower than a B average.

Similarly, the more credits a student earns in the first term, the more likely he or she is to persist. Those who earn more than twelve credits are most likely to succeed, even if they enter with an admissions profile that predicts non-persistence. (Figure 16.)

Finally, students whose last recorded major was in engineering or whose early major is pre-engineering have the lowest probability of persisting while those who major in other fields are consistently most likely to persist.

Figures 15 and 16 below show the percent of those actually persisting among those predicted not to persist by CCNY GPA Bands and CCNY Credits Earned Bands, respectively.

Figure 15. Percent Actually Persisting Among those Predicted NOT to Persist by CCNY GPA Bands

Freshmen Predicted Not to Persist by "Admissions" logistic ModelPercent Persisting by First Semester GPA Band

00..-------------------------------------------------------------70. +-------------------------------------------------------------

w.+---------------------------------------------------

50.

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f>lllOO f>N 2<106

lHI% 18.1%

•:LO% 43.8

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•as •7.5" 52.0% 68.1%

Figure 16. Percent Actually Persisting Among those Predicted NOT to Persist by CCNY Credits Earned Bands

General Remarks on the Findings

Limitations

Clearly, there are more factors that contribute to lack of persistence than have been explored in this report. Financial and personal issues often contribute to retention problems. Although income is among the variables provided, a large number of records had missing or zero values. Indicators of probationary status were made available after this analysis was completed. A review of majors was limited to looking at STEM versus other majors, and a more detailed analysis should be attempted at a later date. Finally, any data driven analysis fails to capture the stories that often shed the most light on retention issues.

fun paper 1 fall 2011 instructions

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