Final Paper

Here it is, a culmination of a semester of work. I hope you enjoyed following my posts and reading my paper. Professor Arvan, I'll send a copy directly to you, is there anything else you need me to do? Thank you so much for your help and guidance.

Analysis Rough Draft

A rough draft of my analysis can be found here, although it is a little barren. I included most of what I wanted to write and most of the graphs I want to use, but there are none of the tables I want to include. I decided to analyze UIUC transfers from Illinois Community Colleges, and the graduation rate, transfer rate, and graduation plus transfer rate of Illinois community colleges. I decided to leave out the Pathways sections for now, as the data for that section was not as good. I can always include it later if I feel the paper needs more.
My plan is to retype my whole paper in Latex for the final draft, so hopefully it will look a lot nicer and more organized. If you want some more updates on my progress, I'd be happy to give them, but otherwise I don't plan on any blog posts until I retype the whole thing. As always, any suggestions would be appreciated. Thanks.

Progress Report: April 21, 2015

This past week, I have been using the data I have collected in Stata to see what I could find. I made a model for estimating how many uiuc transfer students an IL community college would have, but every model had Parkland Community College as an outlier, so I made one for all schools besides Parkland. After that, I tried to create a model of IL community colleges' graduation and transfer rates based on school characteristics. My transfer model without Parkland had an Adjusted R^2 of around .85, while the school performance model had an adjusted R^2 around .60, so I was relatively happy with them.

Then I tried to use the data to check how the Engineering Pathways program effects engineering transfers to UIUC, with much less success. First, I tried to model which schools would qualify for the program, but my best model predicted less than half of the schools. This suggests that school qualification for the program is based factors other than school characteristics, perhaps state politics or personal relationships. And while Pathways schools tend to have more engineering transfers, the effect is small, averaging a little over 2 more transfers, and Pathways status has no effect on percent of engineering transfers to uiuc out of total uiuc transfers. If I were to do a section on Pathways in my final paper, it would probably reflect more on how ineffective it is.

My goal now is to start writing up my analysis in paper form, and hopefully get a rough draft within the next week or two. I'll let you know its progress in future posts.

Literature Overview and Data Set update

I have updated my data set with a variables page, it is the second one. The updated data set is here. I've started trying to use Stata to see if I can find any interesting relationships. I'll let you know how that has been going next week.

I also updated my Literature review. If you have the time, could you skim it for about 5 minutes, just to see if the format is correct. I'm worried it may sound too much like an essay, rather than a literature review. The update can be found here.

If there's anything more I should be doing, please let me know. Thanks.

Data Update

My updated data set can (hopefully) be found here. It contains all the data I used, most of it summarized on sheet 1, titled "All IL Public." All the data is from 2012. Illinois Transfer information is from the university and can be found on their website under 2012. Driving distances were found using Google Maps. Overall college data was found using College Measures. As a note, Google uses slightly different formulas, so variance and standard deviation do not show up. If you want the original copy, please comment.
I have decided to broaden my focus away from Engineering Pathways specifically and towards Illinois Community College Students overall. I want to see what makes them successful individually and specifically at getting students transferred into UIUC. My plan is to upload this data into Stata, which will hopefully make analysis easier. I will let you know how this turns out in latter posts.

Literature Overview (Draft)

Literature Review Rough Draft

The above links to a rough draft of my Literature Review. You cannot edit this one, but if you want one to edit, please email me.

Criticism would obviously be appreciated, especially on substance and formatting. Is this more or less the style you want and are there any topics I should cover more?

Thanks.

Progress Report: March 20

I think I have come up with a potential thesis topic: the UIUC Engineering Pathways program. I want to study how it is the same and/or different from the normal transfer process. I would focus on: if and how their backgrounds and pre-UIUC results differ from other transfer and native students, and try to find strengths and weaknesses of the program. Some downsides that I can see are that the university does not track transfer students differently after they arrive on campus, and potential difficulty getting data on Pathways students directly. But I think it would be useful to know how designated programs may help or hurt students.

To start I collected data from College Measures (collegemeasures.org) about the Community Colleges that are connected to the Pathways program, as well as general information about Illinois community colleges . I also found information on UIUC transfer students overall (http://www.pb.uillinois.edu/Documents/transferchars/2012/2012-Transfer-Characteristics.pdf) . Both sets of data were for 2012, and can be found summarized here.

For spring break, it intend to look more into the Engineering Pathways program: checking it's requirements, finding out how many students utilize it, etc. I will also try to find similar programs both at UIUC and other universities to compare and contrast their set up. Perhaps there has been some scholarship already on guaranteed transfer programs, so I will try to find what others have done. Finally, I want to use the data I collected to see if the chosen Community Colleges are any different from others in Illinois, and if Pathway students are any different from other transfer students.

Hopefully this is a reasonable topic for my senior thesis. Any comments would be appreciated.

Progress Report: March 1-March 7

Through my readings and interview with Joe Kulapan, I decided I wanted to do something on the topic of transfer students, especially from community colleges. I would like to see how community colleges help and/or hurt student's chances at bachelor degrees. To do this, I would need data that follows individual students from school to school. Several of the articles I have read have used the National Education Longitudinal Study: 1998-2000, which would work because it tracked students across all their schools, and could give a full picture of student's lives. I hoped that I would be able to use it, so I borrowed the data from the library. It seems, however, that either the data is only cross-sectional and does not give individual results, or I am unable to extract this individual data due to technological incompetence. Either way, it has been disheartening not being able to use that data, and I am not sure where I can find good data on this subject.

So I see my options as trying to find other data, to approach this subject from a more theoretical perspective, or to switch to some other sub-topic in graduation. Any advice would be appreciated. Thanks.

Alternative paths to college completion: Effect of attending a 2-year school on the probability of completing a 4-year degree: Jonathan Sandy, Arturo Gonzalezb, Michael Hilmer

This paper tries to find the effect of attending a 2-year college before attending a 4-year university has on Bachelor degree rates. It uses the National Longitudinal Survey, class of 1972 (NLS72); the 1994 round of the Beginning Postsecondary Study (BPS); and the 1992 round of the sophomore cohort of the High School and Beyond (HSB) as separate data sources to see if their results are consistent. It also uses Oaxaca's method to attempt to separate any potential differences between those who start at 2- year institutions and those who start at 4-year institutions by whether it is due to variations in student quality or variations in institution quality.

The most interesting part for my purposes was the analysis of the HSB data, as this was the data used by Adelman in his first toolbox study. He found transferring from a 2-year to a 4-year institution while earning more than 10 credits at both increased graduation rates. Would these researchers find the same results? They did not, in fact, they found that chances of graduating were 19.3% lower for those who attended 2-year institutions first, and that about 48% of this was due to lower student quality while about 52% was due to lower institution quality. Why the opposite results?

For Sandy, et al., mother some college, father some college, gpa above a B average, female, verbal and quantitative SAT scores, and hours worked were all significant controls. For Adelman, high school performance, having children, continuous enrollment, 1st year grades, dropping many courses first year and overall, and grade trend were all significant controls. He also found sex, whether a student worked or not, and starting at a 4-year university were insignificant, while Socioeconomic status (SES), which Sandy, et al. try to represent with parental education, was only significant at the .1 level.

Given the differences in the findings despite seeming to use the same data, it appears that one of the controls must explain the differences between these findings. In my eyes, the two major factors that Adelman accounted for that Sandy, et al. did not are the subject having a child and continuous enrollment. Between these, it seems like continuous enrollment, one of the biggest factors of Adelman's model, is the likely culprit. Perhaps 2-year students who go directly into 4-year programs have more momentum, or have lower opportunity costs, or were more motivated in the first place, or some other factor that means they are more likely to graduate than those who wait between finishing at a 2-year and attending a 4-year institution. Another potential factor is in the way transfer is described; Adelman requires at least 10 credits from each institution. Perhaps students struggle from the transition from 2-year to 4-year, and thus never complete even 10 credits from the later, still including them in Sandy, et al.'s analysis, but not Adelman's.

Either way, this shows how tangled all this data is and how seemingly similar assessments of  the same data can show wildly different results.

Interview with Joe Waranyuwat, Part II

This is a continuation of the correspondence I have had with Kulapan (Joe) Waranyuwat, Academic Advisor and Coordinator of Transfer Programs at UIUC, about his work with engineering transfer students. He was kind enough to answer some of my follow up questions.

Scott Spitze (SS): How much do you track students once they have transferred here? Is it the same as other students, or do you pay closer attention to them? Are you quicker to intervene if transfer students seem to be struggling? 

Joe Waranyuwat (JW): 
Post-transfer:

We track the transfer students once they have transferred here, but no more than what we do with the other students. Because transfer students do just as well (as measured by GPA) as the non-transfer students, we decided not to provide more support or resources for the transfer students than we do for the non-transfer students and to put our resources elsewhere (pre-transfer).

Pre-transfer:

I should mention that we have two groups of transfers: Pathways and non-Pathways. We do not provide resources for the non-Pathways prospective transfer students beyond what you’d typically see in a college office: advising and admissions counseling. Most of our resources are put into the Engineering Pathways program: http://pathways.engineering.illinois.edu.

SS: What does the University do to help transfer students adjust socially, or is that something you have to let happen naturally? For the students who struggle here, do problems seem to be more social or more academic?

JW: The problems seem to be more social. We have social events spread across the first semester. The first semester is usually the hardest so we try to get them connected then. After that first semester, most of them are OK. We also have an orientation course that really helps them make the transition. Topics include: study skills, expectations, time and stress management, campus resources, etc.

SS: I do not know how much you take high school performance into account, but if you do, about what percentage of students could have been accepted to the University if they had applied as freshman, and what percent probably would not have been accepted, but their college career makes them qualified? 

JW: I have not measured this so I cannot say conclusively. But my hunch tells me that most of them would not have been admitted as a freshmen. For the majority of them, their college career makes them qualified.

Here’s my personal opinion with the caveat that I do know much about this area so I’m commenting purely out of observation:

To be admitted as a freshmen, you have to be really good at taking standardized tests since most of our admitted students fall in the top 1% of the country in terms of test scores. These tests have merit but major flaws too. They measure processing speed (how fast you can crunch out those math problems, and it’s not about whether you have the capability to think abstractly which is what is often required for advanced undergraduate study), knowledge base (the better the high school you go to, the better your knowledge base), and income level (if you’re really low income, you can’t afford to take these tests). So the students that miss out on admission might be slower thinkers (and thus can’t score in the top 1%) but has an ability to do complex mathematics and reasoning, or students who have potential to learn but didn’t attend the most resourced high schools, and those who simply cannot afford the exams.

We see many transfer students who fall into these categories.

Progress Report, Week 2/15-2/21

On advice from a friend who transferred here, I contacted Joe Waranyuwat, Academic Advisor and Coordinator of Transfer Programs at UIUC (interview here). He was very helpful; giving me an outline of how the transfer process works here at UIUC and answering my questions about transfer students. I'd once again like to thank Mr. Waranyuwat for his time.

This week, I've been reading a lot on transfer students. I haven't been summarizing them formally, but I have added them to my bibliography. These papers focused on the process transfer students go through and how they fair at their new universities, and how community colleges can best help their students.  

The second topic was addressed in Juan Carlos Calcagno, et. al's "Community College Student Success: What Institutional Characteristics Make a Difference?" Which compared individual student results based on the community college they attended. They found that smaller size and lower proportion of part-time faculty both correlated with higher success rates. 

The first topic was addressed in a number of papers I read, one of which, "Academic Performance of Community College Transfer Students and 'Native' Students at a Large State University," was written by J. Conrad Glass Jr. and Anthony Harrington. It compared 100 transfer students and 100 'native" students all attending a 4-year public university students for 2 years starting junior year. Some transfer students saw gpa fall the first semester of junior year, but final grades were not statistically different from non-transfers. Transfer students were more likely to drop out and graduate late, but were in many ways similar to "native" students. The sample size, however, was quite small.

A more qualitative look at the process of transferring came from Barbara Townsend's "Community College Transfer Students: A Case Study of Survival," which interviewed community college students from one school who transferred to one 4-year university. The students mostly reported that they transferried on their own, with more help from the 4-year university than the community college. They found the academics more rigorous  at the 4-year university, and many of their biggest wishes were that the community college would emphasize more writing.

Townsend worked with Nancy McNerny and Allen Arnold to produce a quantitative measurement of transfer students in "Will this Community College Transfer Student Succeed? Factors Affecting Transfer Student Performance," which followed transfer students from one community college to one 4-year university. They focused mainly on high school and community college performance as predictors, and found that both predict higher gpas at the 4-year university. But it also found that of the students, about 50% would not have been accepted based on high school performance alone, and many were able to achieve satisfactory gpas at the 4-year university.

From the readings this week, I think I have a few questions:

1. Part-time faculty correlate with lower success rates for students of community colleges. Is it justifiable for these colleges to increase tuition so they can higher full time staff, or would this expense not justify any improved results that might be seen?

2. The success of community college students still largely depends on background and academic preparation. But many community colleges employ open enrollment and have a mission of serving everyone. Is this mission hurting the students who end up moving on to 4-year colleges and would benefit from more rigorous teaching? Should community colleges be divided into those for students who want associates or below and those who want bachelors or above?

3. Transfer students face two types of challenges when arriving at new schools: social and academic. How are these problems different from "native" students? What can universities do to help transfer students meet these challenges? 

4. As shown before, high school success is a huge indicator of college success. But some transfer students succeed at community colleges and 4-year universities despite shaky high school records. Is this the case of students under-performing in high school, but they know most of the information needed for college, they just don't signal so, or do they learn the gaps in their knowledge at community colleges that allow them to succeed at 4-year universities? Can community colleges take students who are not qualified for 4-year universities and transform them into students who are? 

Discussion with Kulapan (Joe) Waranyuwat, Academic Advisor and Coordinator of Transfer Programs at UIUC

I was privileged to be in contact with Joe Waranyuwat, Academic Advisor and Coordinator of Transfer Programs at UIUC. He mainly works with engineering studetns, and was gracious enough to answer some of my questions about engineering transfer students at University of Illinois, Urbana-Champaign.

General Statement:
"Transfer process is straightforward. Students apply through the Admissions site, and we select them primarily based on their college GPA. A big chunk of the students transfer from community colleges but a decent number transfer from 4-year institutions as well. We have programming set up for students before they transfer to Illinois and after they transfer."

"The programming is different for each group since each group has different needs. For the former, it's mostly advising and teaching. For the ones who have successfully transferred over to Illinois, programming revolves mostly around orientation classes and social activities. Success rates, as measured by the Illinois GPA, between the regular students and the transfer students are more or less the same."

Q & A:

Scott Spitze (SS): Are there any differences in background between the average transfer student and the average student who started here?

Joe Waranyuwat (JW): It depends what you mean. But yes, I’d say so. We see more older students among the transfer population, perhaps more low-income students too, and some of them were less prepared for a 4-year school right out of high school.

SS: About what percentage of desired transfers do you accept?

JW: We admit approximately 300 applicants out of 1,000. There is no exact percentage or number. We just want to admit qualified students.

SS: Are there any common problems experienced by transfer students?

JW:  Transition to a new campus, courses, and social life.

SS: What are the most important steps to assure smooth transitions for transfer students?

JW: Addressing #3 (previous question).  We do this primarily through advising, teaching an orientation class, and providing opportunities for students to engage with each other and the larger campus community.

SS: Would you advise a potential student wanting to go to a school like uiuc to apply directly, or to take classes at a cheaper school before transferring?

JW: This really depends on the student’s individual circumstances and needs. I certainly recommend some to apply directly and others to start at a community college first.

I have asked some more questions about transfer students and am waiting for a response to them, so this post will be updated when I hear back. 

Potential Data Sources

1. UIUC- 6 year raduation rates of incoming freshman who entered from 1994-2006, includes college, race/Hispanic, gender, and whether the person changed colleges while at Illinois
http://www.dmi.illinois.edu/stuenr/index.htm#gradrates

2. University of Wisconsin- Madison- 6 year graduation rates for freshman who entered from 1996-2005, including at UW-Madison, at any UW school, and any institution, female rates, and "targeted minority" rates
 https://apir.wisc.edu/retentionandgraduation/NSC_Enhanced_Graduation_2012.pdf

3. UIC- 6 year graduation rates for incoming fall freshman from 2004-2008, by race/Hispanic and gender, includes underlying numbers
http://www.oir.uic.edu/students/pdfs/IPEDS_GradRate_RE_GEN_FR_FERPA.pdf

4. North Carolina University system- 6 year graduation rates for incoming freshman from 2001-2005 for 17 schools in North Carolina system, includes whether graduated from starting institution or any institution within the system.
https://www.northcarolina.edu/sites/default/files/retention_graduation_report_2012.pdf

5. Oregon State University- 6 year graduation rates for incoming freshman from 2000-2007, includes gender, race/Hispanic, in-state vs out-of-state, no underlying numbers
http://oregonstate.edu/admin/aa/ir/sites/default/files/retention-graduation-gender-ethnicity-residency.pdf

4. College Measures- Potential website to use

Positives- Data on lots of schools, especially useful for smaller universities and community colleges, which are hard to find good data for; break down by race/Hispanic; backed by American Institutes for Research, which seems reliable; some past data; two year institutions include transfer rates

Negatives- Does not say how they got data; does not give underlying numbers, just percentages; unsure over what time period they measure graduations; race/Hispanic data only goes back one year, overall data only goes back five; no info on what happened to transfer students

Overall, I'd be very hesitant to use this data, but other than this source, I haven't found good data other than for large, public, four year universities, and the data I have found for other schools has been just as simplistic. Should I be more worried about false inferences from only looking at big schools, or with errors due to sub-optimal data over a larger sample. My gut says the later, and that if I can only make inferences that apply to large schools, so be it, but I would love your opinion.
http://collegemeasures.org/

5. National Education Longitudinal Study, 1988-2000 This is the data used by Adelman in "Revisiting the Toolbox." I have requested the cd from the library and will update with how good the data is when received. 

6. A Stronger Nation through Higher Education- A report by the Lumina Foundation, gives percent of residents of American states, counties, and cites who have at least an associates degree; level of education for citizens of each state, degree attainment broken down by race; all in percentages, not hard numbers 
http://strongernation.luminafoundation.org/report/downloads/pdfs/a-stronger-nation-2014.pdf

7. National Information Center for Higher Education Policymaking and Analysis- Website with numerous data on college, including graduation rates, affordability, efficiency, state support. and workforce and economic conditions for each US state; combination of numbers and percentages
http://www.higheredinfo.org/ 

8. National Student Clearing House- Website that tracks a large number of university graduation rates, broken down by state, type of institution, full time/part time status, and age. Does not, however, have background, and some reports lack the base data sets.
http://www.studentclearinghouse.org/

Potential Questions to Answer

From my various readings, I have gotten a better idea of what affects graduation and what does not. Here are some of the more promising variables, and how they might interact, with a brief explanation. These are not all explicitly backed by data, but hopefully I can generate some ideas by listing them out.

1. Continuous Enrollment and Transfers- Adelman has shown that continuous enrollment is a huge predictor for graduation, and that transferring to 4-year schools while getting 10 or more credits from both institutions positively correlates with graduation. So it seems that students having a hard time at one institution should be encouraged to transfer to another one that may match their needs better, rather than take a break of a year or more. But this is not a panacea; Adelman also shows that more schools correlate with lower graduation rates. If a student is transferring from institution to institution aimlessly, it hurts their chances to graduate much more than a break would, Could there be a way to tell if transferring would help or hurt a particular student?

2. Pre-College Factors- Adelman showed that academic resources, an index combining high school curriculum, gpa/class rank, and test scores, is a much better predictor of graduation than socio-economic status (ses), sex, or race. High school curriculum, is an especially strong predictor, as DesJardin et al. showed. This is great, because it is possible to raise curriculum for all. Unfortunately, ses is highly correlated to academic resources, and just because a high school student is in a certain class does not mean they understand the material or that the class is teaching what it is supposed to. How can we provide the academic resources to lower ses students so they can graduate college?

3. Traditional vs Nontraditional Students- Nontraditional students are hard to study; it is unclear how to exactly define what makes a student nontraditional, and these students can be very different from one another. Their main shared trait is that their lives do not revolve around their school or its culture. While traditional students tend to live on or near campus, work part time if at all, have many of their social connections tied to the university, and usually only need to worry about taking care of themselves; nontraditional students tend to commute, often work full time, already have lives established outside the university setting, and often have to take care of others, especially children. While nontraditional students often have higher college gpas, they dropout much more frequently. What is the best way to define a nontraditional student? How are nontraditional students at  4-year institutions different from those at 2-year ones? How should institutions interact with nontraditional students compared to how they interact with traditional students?

4.  Academic vs Social Integration- For this relationship, I am mainly relying on a study on how college athletics effects college persistence by Mangold and Adams and a New York Times article by Paul Tough on how University of Texas-Austin is attempting to integrate minority students into the university's culture, both of which I have added on my bibliography page but have not formally summarized. Both seem to agree that student integration can have positive effect on academic integration by providing students emotional support and encouragement and making it clear that students belong at the institution and are capable of graduating. But Mangold and Adams note that "social integration is neither a necessary, nor sufficient condition for academic integration;" students may have their own support networks outside of the school or may invest too much energy into their social lives to the detriment of their academic performance. This is an interesting issue for me, because it also includes parts of the traditional vs nontraditional dichotomy, how to help lower ses students succeed, and even could include if an influx of foreign students affects social integration. But I could also see it as the hardest to find data for. How much should universities be concerned with non-academic matters, and how can they best improve them?

Exploring the Effects of Financial Aid on the Gap in Student Dropout Risks by Income Level: Rong Chen, Stephen DesJardins

Read here

This 2007 study by Rong Chen and Stephen DesJardins attempts to find correlations between income, type of financial aid, and college dropout rates. They use data from Beginning Postsecondary Students survey (BPS:96/01) which followed students nationwide who started as post-secondary students during the 1995-1996 academic year and tracked them for six years, recording their background, financial aid received, and enrollment status, among other factors. The survey had data on those who started at 2-year and 4-year universities, but this study only looked at those who started at a 4-year institution. 

Financial aid was broken down into three categories: Pell grants, loans, and work-study aid. The data set had some holes in it, while for grants and loans it gave an exact amount in dollars for all years, it only gave exact amounts for work-study aid was only reported in year 1, the remaining years only indicated whether this type of aid was received or not. Therefore, the researchers could not test for amount of aid overall, just the type of aid. Income was measured in four categories, lowest, middle, highest, and unreported. Their analysis found that students in the lowest third drop out the most, but all types of aid help in lowering dropout rates. Work-study aid and loans lowered dropout rates about equally for all income levels, but Pell grants were most effective on low income students. 

Update: For their data set, low income meant less than $25,000, middle meant between $25,000 and $75,000, and high was anything above $75,000.

Adding a timing light to the “tool box”: Stephen DesJardins, Brian McCall, Dennis Ahlburg, and Melinda Moye

Read here

This 2002 paper by Stephen DesJardins et al. is a response to Clifford Adelman's "Answers in the Toolbox Study" (see a summary of that paper in a previous post) with a focus on adding the element of time to their analysis. The researchers believe that different factors will have greater or lesser effect as time goes on. Their model asks, if a subject has not graduated from a 4-year college by time t-1, how does a given factor correlate with graduation. While they mainly use the same factors outlined by Adelman, and agree with many of his conclusions, they show that the effect of different factors change when time is taken into account.

DesJardins et al. use the same data set as Adelman, and start by replicating his results. Afterwards, they start testing various factors, and how they change correlation to gradation over time. In their final model, for example, being male is initially negatively correlated with gradation. But over time, the effect becomes less negative, and by around year 7, being male becomes positively correlated with graduation. Their biggest critique of Adelman was his academic resources factor, which was and index of high school intensity/quality, high school gpa/class rank, and high school test scores. They found the index became less predictive overall when college gpa was included, a fact Adelman acknowledged in his paper "The Toolbox Revisited." Much more interestingly, when they separated academic resources into its three factors, they all had relatively similar, positive prediction power at year one, gpa/rank and test scores declined in influence as time went on, eventually becoming negative, while intensity/quality grew and became much more significant as time passed.

Overall, this study had many of the same findings and limitations as Adelman's "Answers in the Toolbox" study, such as the fact that it has a hard time measuring the effect of student aid due to problems with reporting. Despite its similarities, it adds more credibility to many of Adelman's arguments while highlighting areas where Adelman may be wrong.

A Conceptual Model of Nontraditional Undergraduate Student Attrition: John Bean and Barbara Metzner

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This 1985 paper by John Bean and Barbara Metzner creates a model for attrition rates of nontraditional undergraduates through a literature review of over 70 papers covering traditional 4-year universities, commuter 4-year universities, and 2-year colleges. Their model has six major variables, most of which are made up of an index of factors. These variables are background and defining variables, academic variables, academic outcomes, environmental variables, psychological results, and intent to leave. Bean and Metzner do not test their model, nor propose the significance of each factor. This is problematic, especially considering some of their factors, such as academic advising, a factor in academic variables, are showed as positively correlated to persistence in some studies, negatively correlated in others, and of no effect in others. Despite these problems with the model, I found this paper to be a very useful literature review.

The paper defines nontraditional students somewhat ambiguously: being older than 24, or living off campus and commuting, or studying part time; and not relying on the social structure of the institution and concerning themselves primarily with academic issues at the institution. In other words, nontraditional students tend to be cemented in their lives already and have strong existing social circles outside of school, and they are usually pursuing education for better career prospects. They tend to have more outside obligations, such as family and work, which means they have lower attrition rates despite often having higher gpas. Other notable findings are that, compared to traditional students, they have lower high school gpas, less social connections on campus, transfer to other schools less, and were more stressed.

The main problem with this paper is that all of the studies reviewed looked at attrition rates rather than graduation, and many only examined students for a year or less, so it may be missing important insights, especially after freshman year when many of these studies end. It is also unclear whether many of these studies could determine between students who stopped attending an institution because they were only interested in a class or two for personal enjoyment or occupational incentives, transferred to different schools, or dropped out of higher education all together, an important distinction. Still, this paper clearly shows where we know traditional and nontraditional students differ, and where more research needs to be done.

Nontraditional Undergraduates: Susan Choy

Read Here

This is a 2002 study by Susan Choy that examines the growing number of nontraditional students in US higher education using the National Postsecondary Student Aid Study- 2000 (NPSAS:2000) to examine this population and its characteristics and the Beginning Postsecondary Students Longitudinal Studies- 1996/1998 (BPS:1996/1998) to compare persistence of traditional and nontraditional students. Choy describes a nontraditional student as one who delays enrollment by a year or more, or attends part time at least part of the year, or works full time while enrolled, or is considered financially independent when calculating financial aid, or has a non-spouse dependent, or is a single parent, or does not have a high school diploma. She further breaks down the categories of non-traditional students, those who meet only one of these descriptions are minimally non-traditional, those who meet two or three are moderately nontraditional, and those who meet four or more are highly nontraditional. One problem with this description is that many of them, especially being a single parent and having dependents other than a spouse, are not mutually exclusive and in some cases are contained within each other. Choy gives no justification for why this is justifiable.

The NPSAS:2000 found that 73% of undergraduates in 1999-2000 had at least one nontraditional characteristic; about 27% were traditional, 17% minimally nontraditional, 28% moderately nontraditional and 28% highly nontraditional. Traditional students tended to enroll in public and private not-for-profit 4-year schools, minimally nontraditional students tended to enroll in public 2-year and 4-year schools, and a majority of moderately and highly nontraditional students tended to enroll in public 2-year schools, and together made up a large portion of students at private for-profit institutions.

There was also a traditional-nontraditional split in hours worked and self-identification as student or employee. For traditional students, 30% did not work, 67% worked but considered themselves primarily students, and 3% considered themselves employees. For minimally nontraditional students, those figures were 19%, 71%, and 10%; for moderately nontraditional they were 20%, 43%, and 37%; and for highly nontraditional they were 11%, 22%, and 67%. And while students of all types who worked reported about the same number of benefits of working while studying, the more nontraditional students found the challenges of working while studying to be much greater. For nontraditional students who considered themselves employees first, the main motivations for going to school were gaining skills, earning a degree/certificate, and personal enrichment/investment, each cited in about 80% of the population. Only about 33% said required for job was a motivation.

For students int the (BPS:1996/1998), who enrolled from 1995-1996 with the goal of a certificate or degree, traditional students without a degree were more likely to be enrolled three years later than nontraditional students without a degree. For associates and bachelors degree, the
more nontraditional a student was, the more likely it was that they had dropped out. For traditional and nontraditional students alike, those whose goal was and associates degree were more likely to drop out with no degree after 3 years than those aiming for a bachelors. If they left school at any time for more than four months, both traditional and nontraditional students were equally likely to return to the same institution, but traditional students were more likely to transfer downwards while nontraditional students were more likely to drop out.

Bibliography

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Adelman, C. (2006). The Toolbox Revisited: Paths to Degree Completion From High School Through College. US Department of Education.

Adelman, C., Daniel, B., & Berkovits, I. (2003). Postsecondary Attainment, Attendance, Curriculum, and Performance: Selected Results from the NELS: 88/2000 Postsecondary Education Transcript Study (PETS), 2000. ED Tabs.

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The Toolbox Revisited: Paths to Degree Completion From High School Through College; Clifford Adelman

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This is a 2006 study by Clifford Adelman where he updates his model from "Answers in the Tool Box. Academic Intensity, Attendance Patterns, and Bachelor's Degree Attainment" using the data from the NELS:88/2000 study, which again follows nationwide students, this time from 8th grade in 1988 through to 2000. While this gives slightly less time to graduate from college than the first study, it is still a useful comparison. Adelman is attempting to test whether his results hold up, how graduation rates have changed, and to address any criticisms of his first piece. (If you have not read the previous post on this paper, you should do so now.)

Academic resources is still a predictor of success, but less so. High school rank/gpa became more important than test scores, and completing Algebra 2 no longer doubled the chance of degree obtainment, now it is required to go beyond Algebra 2.  The reason this factor is weaker is because the model takes more into account of college academics. The NELS:88/2000 included more information on courses taken, which led to some interesting results, such as the fact that 5 or more credits earned over summer drastically improves graduation rates, especially for African-Americans.

Once again, transferring is a mixed bag. For those transferring from a 2 or 4-year institution to a 4-year institution and earn at least 10 credits from each, transferring increases graduation rate. But multiple schools lowers graduation rates. Adelman describes the later case as brought down by students "swirling" back and forth between 2 and 4-year systems, wandering aimlessly. These are the transfers that the system desperately needs to avoid.

Compared to the original model, this one added multiple schools, whether a student was ever part time, and percent of classes dropped after deadline as negatives and summer term credits and cumulative college math credits as positives. Parenthood was no longer a factor, nor was earning less than 20 credits in the first full year. Overall, the model was about as predictive as before, this time continuous enrollment, first year grades, and percent of classes dropped after the deadline were the biggest predictors of graduation. The model still finds race and gender insignificant.

Over these two papers, Adelman's main points are that college success comes from a strong high school foundation and continuous enrollment in school. If we want to study graduation rates, we need to examine students, not institutions, and we need to use hard evidence, not survey data. The system needs to focus on students, setting high expectations, encouraging summer credits, making sure they have a plan and the resources needed to carry it out.

Answers in the Tool Box. Academic Intensity, Attendance Patterns, and Bachelor's Degree Attainment: Clifford Adelman

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This 1999 study is one of the most cited studies on the subject of college graduation, and for good reason. Clifford Adelman uses data from High School & Beyond/Sophomore cohort files, a study that followed a national selection of students from 10th grade in 1980 through to 1993. Researchers tested these students in 10th and 12th grade, asked  them about family background and future ambitions in both these grades, asked about degree obtainment and college experiences throughout the period, and, most importantly, tracked the high school and college transcripts of these participants over the length of the study. Adelman is interested in examining what are the main factors in college graduation, especially for bachelor's degrees, and what society can do to improve these rates.

This study is important for a number of reasons. First, it reconciles what participants report against what the records say. Only asking students leads to faulty results, either because students do not know answers, as is often the case with parental education or salary, because they misunderstand the question, such as those who study abroad for a semester claiming they've only ever attended one school, or they lie by over-reporting the good, such as grade point average (gpa) or under-reporting the bad, such as remedial classes taken. Second, it track students as they move throughout the higher education system, not just how they fair at any particular institution, and it tracks them for an extended period of time. Many surveys, especially before this one, focused only on first or second year performance at the same institution where the participant started, but the goal is not persistence, but graduation. Universities may want to focus on retention rates, but for students, what matters is whether the receive their degree or not.

The first factor Adelman considers is academic resources, a term he borrows from previous research, which attempts to measure academic performance in high school and how it affects college graduation rates. This factor, which is an index obtained by combining intensity of high school classes, test scores, and class rank, becomes one of the major determinants of graduation. Students in the lowest two socio-economic status (SES) quintiles but highest academic resources quintile averaged higher graduation rates than those in the highest SES quintile overall. A great high school education is a strong predictor of college success. Especially important is math; completing Algebra 2 in high school more than doubles the odds of receiving a bachelors degree. SES still has some significance in the model, but academic resources is a much more powerful indicator.

At the time of the study, the phenomena of attending multiple institutions was growing, as it does to this day, and researchers were still behind on how they measured its effects. Transfers can have very different effects on graduation rates depending on the schools being transferred from and to, how many credits earned at each, and whether the original school is transferred back to. For example, students who attend a community college where they earn 10 or more credits, then transfer to a 4-year institution where they earn more than 10 credits have higher graduation rates than those who start of attending a 4-year institution, even though those who attend a 4-year institution first have higher graduation rates overall. Transferring to a school in a different state also increases graduation rates over transfers within a state. But no matter where the student is, the biggest factor is that they are continuously in school, never missing more than 2 semesters or 3 quarters, and this is the second big factor in graduation rates.

Other factors that they study found were significant were whether they had children before 1986 (age 22 to 23), completed less than 20 credits in first complete year, or transferred and did not return to the same institution all of which had negative effects, while improved gpa from freshman year to senior year and freshman gpa, as well as previously mentioned continuous enrollment, academic resources, SES quintile, and transferred from a 2-year college to 4-year college and earned 10 credits at each all had positive effects. Overall, the model had about a 43% explanatory power, mainly coming from continuous enrollment and academic resources. What is interesting is that race and gender have no significant affect on the model, despite numerous attempts to include it, and while SES quintile matters, it is much weaker than expected.      

Overview

This is a blog to track my progress through my Senior Thesis. Right now, I have no specific topic, but I'll be examining college graduation rates and trying to find less explored areas to observe. So for the first few weeks, most of my posts should be summaries of the major research I've read, and perhaps some specific ideas for topics. I will also give a general progress post every two weeks or so. Hope it goes well, and you enjoy reading.