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?
Saturday, January 31, 2015
Friday, January 30, 2015
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.
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.
Thursday, January 29, 2015
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.
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.
Tuesday, January 27, 2015
A Conceptual Model of Nontraditional Undergraduate Student Attrition: John Bean and Barbara Metzner
Read Here
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.
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.
Friday, January 23, 2015
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.
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.
Thursday, January 22, 2015
Bibliography
Adelman, C. (1999). Answers in the Tool Box. Academic Intensity, Attendance Patterns, and Bachelor's Degree Attainment.
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.
Baumol, W. J. (1993). Health Care, Education and the Cost Disease: A Looming Crisis for Public Choice. Public Choice, 17-28.
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of educational Research, 55(4), 485-540.
Belman, D., & Heywood, J. S. (1991). Sheepskin effects in the returns to education: An examination of women and minorities. The Review of Economics and Statistics, 720-724.
Calcagno, J. C., Bailey, T., Jenkins, D., Kienzl, G., & Leinbach, T. (2008). Community college student success: What institutional characteristics make a difference?. Economics of Education review, 27(6), 632-645.
Chen, R., & DesJardins, S. L. (2007). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49(1), 1-18.
Choy, S. (2002). Nontraditional Undergraduates: Findings from" The Condition of Education, 2002."
Clark, B. R. (1960). The" cooling-out" function in higher education. American journal of Sociology, 569-576.
Clark, B. R. (1980). The “cooling out” function revisited. New directions for community colleges, 1980(32), 15-31.
DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (2006). The effects of interrupted enrollment on graduation from college: Racial, income, and ability differences. Economics of Education Review, 25(6), 575-590.
DesJardins, S. L., McCall, B. P., Ahlburg, D. A., & Moye, M. J. (2002). Adding a timing light to the “tool box”. Research in Higher Education, 43(1), 83-114.
Doyle, W. R. (2009). The effect of community college enrollment on bachelor's degree completion. Economics of Education Review, 28(2), 199-206.
Goodman, J., Hurwitz, M., & Smith, J. (2014). College access, initial college choice and degree completion. Harvard Kennedy School Faculty Research Working Paper Series, 14-030.
Hilmer, M. J. (1997). Does community college attendance provide a strategic path to a higher quality education?. Economics of Education Review, 16(1), 59-68.
Hilmer, M. J. (2000). Does the return to university quality differ for transfer students and direct attendees?. Economics of Education Review, 19(1), 47-61.
Hungerford, T., & Solon, G. (1987). Sheepskin effects in the returns to education. The review of economics and statistics, 175-177.
Jr, J. C. G., & Harrington, A. R. (2002). Academic performance of community college transfer students and" native" students at a large state university.Community College Journal of Research &Practice, 26(5), 415-430.
Kisker, C. B. (2007). Creating and sustaining community college—university transfer partnerships. Community College Review, 34(4), 282-301.
Mangold, W. D., Bean, L., & Adams, D. (2003). The impact of intercollegiate athletics on graduation rates among major NCAA Division I universities: Implications for college persistence theory and practice. The Journal of Higher Education, 74(5), 540-562.
Pascarella, E. T., Smart, J. C., & Ethington, C. A. (1986). Long-Term Persistence of Two-Year College Students. Research in Higher Education, 47-71.
Rouse, C. E. (1995). Democratization or diversion? The effect of community colleges on educational attainment. Journal of Business & Economic Statistics, 13(2), 217-224.
Sandy, J., Gonzalez, A., & Hilmer, M. J. (2006). Alternative paths to college completion: Effect of attending a 2-year school on the probability of completing a 4-year degree. Economics of Education Review, 25(5), 463-471.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of educational research, 89-125.
Tough, P. (2014, May 15). Who Gets to Graduate? The New York Times. Retrieved January 31, 2015, from http://www.nytimes.com/2014/05/18/magazine/who-gets-to-graduate.html
Townsend, B. K. (1995). Community College Transfer Students: A Case Study of Survival. Review of Higher Education, 18(2), 175-93.
Townsend, B. K., McNerny, N., & Arnold, A. (1993). Will this community college transfer student succeed? Factors affecting transfer student performance. Community College Journal of Research and Practice, 17(5), 433-433.
Tucker, I. B. (2004). A reexamination of the effect of big-time football and basketball success on graduation rates and alumni giving rates. Economics of Education Review, 23(6), 655-661.
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.
Baumol, W. J. (1993). Health Care, Education and the Cost Disease: A Looming Crisis for Public Choice. Public Choice, 17-28.
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of educational Research, 55(4), 485-540.
Belman, D., & Heywood, J. S. (1991). Sheepskin effects in the returns to education: An examination of women and minorities. The Review of Economics and Statistics, 720-724.
Calcagno, J. C., Bailey, T., Jenkins, D., Kienzl, G., & Leinbach, T. (2008). Community college student success: What institutional characteristics make a difference?. Economics of Education review, 27(6), 632-645.
Chen, R., & DesJardins, S. L. (2007). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49(1), 1-18.
Choy, S. (2002). Nontraditional Undergraduates: Findings from" The Condition of Education, 2002."
Clark, B. R. (1960). The" cooling-out" function in higher education. American journal of Sociology, 569-576.
Clark, B. R. (1980). The “cooling out” function revisited. New directions for community colleges, 1980(32), 15-31.
DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (2006). The effects of interrupted enrollment on graduation from college: Racial, income, and ability differences. Economics of Education Review, 25(6), 575-590.
DesJardins, S. L., McCall, B. P., Ahlburg, D. A., & Moye, M. J. (2002). Adding a timing light to the “tool box”. Research in Higher Education, 43(1), 83-114.
Doyle, W. R. (2009). The effect of community college enrollment on bachelor's degree completion. Economics of Education Review, 28(2), 199-206.
Goodman, J., Hurwitz, M., & Smith, J. (2014). College access, initial college choice and degree completion. Harvard Kennedy School Faculty Research Working Paper Series, 14-030.
Hilmer, M. J. (1997). Does community college attendance provide a strategic path to a higher quality education?. Economics of Education Review, 16(1), 59-68.
Hilmer, M. J. (2000). Does the return to university quality differ for transfer students and direct attendees?. Economics of Education Review, 19(1), 47-61.
Hungerford, T., & Solon, G. (1987). Sheepskin effects in the returns to education. The review of economics and statistics, 175-177.
Jr, J. C. G., & Harrington, A. R. (2002). Academic performance of community college transfer students and" native" students at a large state university.Community College Journal of Research &Practice, 26(5), 415-430.
Kisker, C. B. (2007). Creating and sustaining community college—university transfer partnerships. Community College Review, 34(4), 282-301.
Mangold, W. D., Bean, L., & Adams, D. (2003). The impact of intercollegiate athletics on graduation rates among major NCAA Division I universities: Implications for college persistence theory and practice. The Journal of Higher Education, 74(5), 540-562.
Pascarella, E. T., Smart, J. C., & Ethington, C. A. (1986). Long-Term Persistence of Two-Year College Students. Research in Higher Education, 47-71.
Rouse, C. E. (1995). Democratization or diversion? The effect of community colleges on educational attainment. Journal of Business & Economic Statistics, 13(2), 217-224.
Sandy, J., Gonzalez, A., & Hilmer, M. J. (2006). Alternative paths to college completion: Effect of attending a 2-year school on the probability of completing a 4-year degree. Economics of Education Review, 25(5), 463-471.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of educational research, 89-125.
Tough, P. (2014, May 15). Who Gets to Graduate? The New York Times. Retrieved January 31, 2015, from http://www.nytimes.com/2014/05/18/magazine/who-gets-to-graduate.html
Townsend, B. K. (1995). Community College Transfer Students: A Case Study of Survival. Review of Higher Education, 18(2), 175-93.
Townsend, B. K., McNerny, N., & Arnold, A. (1993). Will this community college transfer student succeed? Factors affecting transfer student performance. Community College Journal of Research and Practice, 17(5), 433-433.
Tucker, I. B. (2004). A reexamination of the effect of big-time football and basketball success on graduation rates and alumni giving rates. Economics of Education Review, 23(6), 655-661.
Tuesday, January 20, 2015
The Toolbox Revisited: Paths to Degree Completion From High School Through College; Clifford Adelman
Read here
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.
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
Read here
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.
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.
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