fun_paper 1 fall 2012(2)

7/31/2019 Fun_Paper 1 Fall 2012(2)

1/20

PSY 102 Psychology in the Modern World

Instructor: Bob Melara

Fall 2012

FUN PAPER # 1

Due Date: Monday, October 15th, 2012, by 5 pm on **Blackboard**

Critical Thinking using Psychological Science

The Learning Alliance for Higher Education, an educational consulting firm based at the University ofPennsylvania, 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 consultants 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 yourown 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 youshould 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. (Heres 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 reports 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 CityCollege? 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 dropout? 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 ashigh 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? (5 pts).

7/31/2019 Fun_Paper 1 Fall 2012(2)

2/20

b. Describe the relationship between retention at City College and the numbers of courses students takeand receive credit for each semester. Why would the number of courses taken affect retention? (5 pts).

c. Use the relationships you have described in the second paragraph to develop a hypothesis about the roleof 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. (5 pts).

b. How do engineering or pre-med majors fare here compared with other majors at the College? (5 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 whatrelationship to retention you hypothesize. (6 pts).

b. Explain the weakness in the research strategy used by The Learning Alliance. Why is it difficult toexplain 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 byThe 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 bealleviated 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 postgrades and absences on Blackboard, but dont 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 controlfor any preexisting differences between the groups. (6 pts).

b. How can you tell whether any improvement in retention in the Intervention Group is due to at-riskstudents 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? (7 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 yourresults from the chart and their implications for students entering City College this year. (7 pts).

A tenth of your grade will be based on the following:

a. Effective written communication (2 pts)

b. Critical thinking and logical reasoning ability (2 pts)

c. Ability to formulate questions, hypotheses, and research designs (2 pts)

d. Proper use of psychological concepts and theories (2 pts)

e. Competence in quantitative reasoning and analysis of research findings (2 pts)

7/31/2019 Fun_Paper 1 Fall 2012(2)

3/20

Due by 5:00 pm on MONDAY, OCTOBER 15th. 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 theAcademic 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.

7/31/2019 Fun_Paper 1 Fall 2012(2)

4/20

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 ofPennsylvania

April 2011

7/31/2019 Fun_Paper 1 Fall 2012(2)

5/20

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 Allianceto conduct a study of student retention. Just about half of the first-time full-time

freshmen leave CCNY before completing a degree, and nearly half of the students whoenter 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. Itfocuses 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 asfull-time undergraduates in fall 2004 though fall 2009 (Admissions Files). Consisting ofdata for 9,245 freshmen and 5,183 transfer students, the file includes demographic andadmissions 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 herexperience 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 bachelors degrees from CCNYbetween 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 goodinformation about academic preparation, with high school GPA and SAT scores for mostincoming students. For transfers there is information about the institutions from which

they transferred and the credits1

they carried forward to CCNY. The freshmen

analysis comprises matriculating students from fall 2004-2006, while the transfer analysisincludes students who entered in fall 2007 as well. In addition to statistical profiles andstatistical significance tests of the differences between students who failed to continue orcomplete 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 recorded

consistently.

7/31/2019 Fun_Paper 1 Fall 2012(2)

6/20


regression models were built to help quantify the odds of a student with a particularprofile 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 firstyear, 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 firstsemester. Those students earn fewer credits on average than persisting students andhave significantly lower GPAs on average than those who persist, and particularlythose who graduate.

The later the admissions phase in which a freshman is admitted, the more likely he orshe 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 studentswho continue to persist.

Freshmen who select a math-based STEM major (excluding those in the biologicalsciences) 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 topersist.

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 from2004 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 and

2005, and nearly half of those who entered in 2006 left CCNY before completing theirdegrees. Because students tend to take more than four years to complete their programs,

7/31/2019 Fun_Paper 1 Fall 2012(2)

7/20


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 rolls

before they can graduate.

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

Fall of First Freshman EnrollmentF2004 F2005 F2006

Not Enrolled 612 665 698

Still Enrolled 105 246 718

Graduated 451 367 113

Total 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 or

10.1% 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

majorityaround two-thirds of those who ultimately drop outare no longer registered.

Figure 2. Distribution ofNon-PersistingFreshmen by Semesters Attended Before

Leaving CCNY

Semesters

Enrolled

Fall of First Freshman Enrollment

Cumulative Number No LongerEnrolled

F2004 F2005 F20061 62 55 77

2 199 212 253

3 288 316 354

4 406 439 484

5 ormore 612 665 698

Semesters

EnrolledCumulative Percent of All Non-Persisters

F2004 F2005 F2006

1 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 ormore 100.0% 100.0% 100.0%

7/31/2019 Fun_Paper 1 Fall 2012(2)

8/20

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 minoritiesblack and Hispanic freshmen (who are nevertheless notunderrepresented 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 %Not

Freshman Enrolled

Cohort

F2005

Total %Not

Freshman Enrolled

Cohort

F2006

Total %Not

Freshman Enrolled

Cohort

FemaleMale

531 48.2%637 55.9%

592 48.1%686 55.4%

760 43.7%769 47.6%

p =

7/31/2019 Fun_Paper 1 Fall 2012(2)

9/20

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

Fall of First of Freshman EnrollmentF2004

Total %Not

Number Enrolled

F2005

Total %Not

Number Enrolled

F2006

Total %Not

Number Enrolled

New YorkCity

New YorkState

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 highpersistence across entering years is female non-U.S. citizen. For students who entered in

Fall 2004, the ones who were by far most likely notto be retained were (surprisingly)

Asian-American and male, while the least successful freshmen that entered in Fall 2005and 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 eachsubsequent 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

Total %Not

Number Enrolled

F2005

Total %Not

Number Enrolled

F2006

Total Number %Not

Enrolled

Phases 1-3

Phases 4-6

Phases 7-9

Phases Alpha

(10 orhigher)

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

7/31/2019 Fun_Paper 1 Fall 2012(2)

10/20

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

Fall of First of Freshman Enrollment

Special

Admissions

F2004

Total %Not

Number Enrolled

F2005

Total %Not

Number Enrolled

F2006

Total Number %Not

Enrolled

Regular

SEEK

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 86percent 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 for

2005 and 2006 are strictly monotonicthe means increase as students are classified as not

enrolled, still enrolled, and graduated. (In addition, the 10th

and 90th

percentilesnotshown belowfollow 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

80.7

80.6

85.0

80.5

81.8

85.0

80.8

82.9

85.2

p =

7/31/2019 Fun_Paper 1 Fall 2012(2)

11/20

SAT scores are strong predictors of academic persistence among CCNY freshmen. Thosewith the lowest math, verbal, and total SAT scores, on average, are most likely to dropout, while those with the next lowest scores take longer to complete their studies. Studentswith 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 955.7 977.4 981.3

Graduated 1049.4 1060.6 1048.4

p =

7/31/2019 Fun_Paper 1 Fall 2012(2)

12/20

Freshmen Entering Fall 2004

Fall 2004 Credits Mean Credits

Earned Cumulative

Not Enrolled 10.5

Still Enrolled 12.0

Graduated 15.2

Spring 2005 Mean Credits

Credits Earned Cumulative

Not Enrolled 20.5

Still Enrolled 22.6

Graduated 28.6

p =

7/31/2019 Fun_Paper 1 Fall 2012(2)

13/20


Fall 2004 Mean Cumulative

GPA Earned GPA

Not Enrolled 2.57

Still Enrolled 2.64

Graduated 3.16

Spring 2006 GPA Mean Cumulative

Earned GPA

Not Enrolled 2.07

Still Enrolled 2.38

Graduated 3.07

p =

7/31/2019 Fun_Paper 1 Fall 2012(2)

14/20


Figure 11. Declared First Major of First Term


MAJOR STEM OtherMajors

Not Enrolled 307 305

Still Enrolled 45 60

Graduated 188 263

Total 540 628

% Not Enrolled 56.9% 48.6%


STEM OtherMajors



Graduated 150 217

Total 588 690



STEM OtherMajors



Graduated 42 71

Total 642 887


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 markersfor 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 ofnotpersisting versus persisting

2.) The selection of explanatory variables comes from the

earlier analysis that identified characteristics that distinguish the population of studentswho were not enrolled from those who were retained. The models use the combined

years 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 a

students not persisting to persisting: log odds not persist/persist.

7/31/2019 Fun_Paper 1 Fall 2012(2)

15/20

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

Male

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 students high school GPA, the math andverbal SAT scores, and whether a student was admitted in the first three admissions phases

or later. The lower an applicants high school grades and SAT scores, the greater the oddsthat 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 aremore likely to persist than are others.

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

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

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

College Admissions Average (CUNY

calibrated HS GPA)

The lower the CCA, the greater the odds ofnot

persisting versus persisting.

SAT Math The lower the SAT M, the greater the odds ofnot


SAT Verbal The lower the SAT V, the greater the odds of not


Admissions Phase Admitted after the first 3

phases

The odds of not persisting versus persisting are higher if

admitted in phase 4 orlater.

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 forAsians.

A second model takes into account only performance at CCNY and gender. This modelshows that higher early GPAs and higher credit accumulation predicts greater odds of

persisting versus not persisting. Again, males have lower odds of persisting than do

females. Figure 13 below summarizes the findings, the details can be found in Appendix A.

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

7/31/2019 Fun_Paper 1 Fall 2012(2)

16/20

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 modelsdiscussed 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 tobut notidentical tothe one discussed earlier was run using a subset of the student records. Thesubset 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/Persisting

Based on a Random Sample ofRecords

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

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 inphase

4 or later.

Gender Set to Female versus Male Odds of not persisting versus persisting

are lower forfemales.

Non- US Citizen vs. citizen Odds of not persisting versus persisting

are lower for Non US Citizens.

Ethnicity Asian vs. otherethnicity Odds of not persisting versus persisting

are lower forAsians.

Applying the models 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 was

assigned 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.

7/31/2019 Fun_Paper 1 Fall 2012(2)

17/20

were predicted to leave but actually persisted most often (more than half the time) hadthe 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 ofstudents 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 persisteven if his or her admissions characteristics predict a greater than even likelihood of notpersisting. Those who earn a GPA of 3 or higher in their first semester are more likely tostay 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 theyenter 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 otherfields are consistently most likely to persist.

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

respectively.

7/31/2019 Fun_Paper 1 Fall 2012(2)

18/20

Figure 15. Percent Actually Persisting Among those Predicted NOT to Persist

by CCNY GPA Bands

FreshmenPredictedNot to Persistby"Admissions" logisticModel

PercentPersistingby FirstSemesterGPABand

0 0 .. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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

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

50.

H4t

f>lllOO f > N2

7/31/2019 Fun_Paper 1 Fall 2012(2)

19/20

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

7/31/2019 Fun_Paper 1 Fall 2012(2)

20/20

General Remarks on the Findings

Limitations

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

fun_paper 1 fall 2012(2)

Documents