Skip to main content
SpringerLink
Log in

The Effects of Financial Aid on College Success of Two-Year Beginning Nontraditional Students

  • Published:
Research in Higher Education Aims and scope Submit manuscript

Abstract

This study aims to understand the role of financial aid in college success of two-year beginning nontraditional students. By applying discrete time event history models with propensity score covariate adjustment to a nationally representative sample from BPS: 04/09, this study answers research questions centering around the effects of Pell Grants, subsidized student loans and unsubsidized student loans on six-year college outcomes of nontraditional students (i.e. degree attainment, system departure, and continuous enrollment without a degree). The results of this study suggest that these nontraditional students were most likely to drop out in the third college year and that all three types of financial aid appeared effective for reducing dropout risks, but not for encouraging timely degree completion. These findings have significant implications for policy and practice including the necessity for considering the complexity of nontraditional student pathways, backgrounds and unique needs when designing and implementing financial aid policy. The findings also contribute to discussions on ways to fund nontraditional students and provide recommendations for institutions serving large populations of nontraditional students to promote persistence to graduation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. According to the full-scale methodology report of BPS: 04/09 (Wine et al. 2011), the analysis weight significantly reduces bias across most variables except for two categories of institution undergraduate enrollment (i.e. 0–1821 and 1822–6690) for students at private for-profit two-year and above institutions.

  2. While independence of irrelevant alternatives (IIA) is considered the defining property of multinomial logit, tests of IIA (e.g. McFadden et al. 1981; Small and Hsiao 1985; Hausman and McFadden 1984) are proved to provide inconsistent results (Cheng and Long 2007), Long and Freese (2006) suggest that the multinomial logit model should only be used when the outcomes are perceived as distinctive by decision makers.

  3. According to the full scale methodological report by Wine et al. (2011), WTB000 is the longitudinal study weight for analysis of the beginning students who had sufficient data to be included in all 3 years of BPS surveys. And the authors’ correspondence with NCES also confirms the appropriate use of WTB000 weights.

  4. However, the odds ratio associated with each type of financial aid does not directly express the overall effects of respective types of financial aid, rather it represents the effects of the financial aid for the group of students with interaction terms between the financial aid and other variables equal zero.

References

  • Advisory Committee on Student Financial Assistance. (2012). Pathways to success: Integrating learning with life and work to increase national college completion. A report to the U.S. Congress and Secretary of Education. Washington, DC: U.S. Department of Education.

  • Allison, P. (1984). Event history analysis-regression for longitudinal event data. Beverly Hills, CA: Sage.

    Google Scholar 

  • Allison, P. (2001). Missing data: Quantitative applications in the social sciences. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Alon, S. (2005). Model mis-specification in assessing the impact of financial aid on academic outcomes. Research in Higher Education, 46(1), 109–125.

    Article  Google Scholar 

  • Asparouhov, T. (2006). General multi-level modeling with sampling weights. Communications in Statistics—Theory and Methods, 35(3), 439–460.

    Article  Google Scholar 

  • Asplund, R., Adbelkarim, O. B., & Skalli, A. (2008). An equity perspective on access to, enrollment in and finance of tertiary education. Education Economics, 16(3), 261–274.

    Article  Google Scholar 

  • Bailey, T. R. (2006). Research on institution level practice for postsecondary student success. Report prepared under contract for the national symposium on student success, national postsecondary education collaborative. U.S. Department of Education, Washington, DC.

  • Baum, S. (2006). Fixing the formula: A new approach to determining independent students’ ability to pay for college. Indianapolis, IN: Lumina Foundation.

    Google Scholar 

  • Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student retention. Research in Higher Education, 12, 155–187.

    Article  Google Scholar 

  • Bean, J. P. (1983). The application of a model of turnover in work organizations to the student attrition process. The Review of Higher Education, 6, 129–148.

    Google Scholar 

  • Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55, 485–540.

    Article  Google Scholar 

  • Becker, G. (1964). Human capital: A theoretical and empirical analysis. New York: Columbia University Press.

    Google Scholar 

  • Becker, G. (1975). Human capital: A theoretical and empirical analysis with special reference to education (2nd ed.). New York: National Bureau of Economic Research.

    Google Scholar 

  • Blossfeld, H. P., Golsch, K., & Rohwer, G. (2012). Event history analysis with Stata. New York, NY: Psychology Press.

  • Boatman, A., & Long, B. T. (2016). Does Financial Aid Impact College Student Engagement? Research in Higher Education. doi:10.1007/s11162-015-9402-y.

  • Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York: Greenwood Press.

    Google Scholar 

  • Bourdieu, P. (1998). Practical reason. Stanford, CA: Stanford University Press.

    Google Scholar 

  • Bourdieu, P. (2006). Cultural reproduction and social reproduction. In D. B. Grusky & S. Szelnyi (Eds.), Inequality: Classic readings in race, class and gender (pp. 257–271). Boulder, CO: Westview Press.

    Google Scholar 

  • Bourdieu, P., & Passeron, J. (1977). Reproduction in education, society and culture. London: Sage.

    Google Scholar 

  • Bourguignon, F., & Verdier, T. (2005). The political economy of education and development in an open economy. Review of International Economics, 13(3), 529–548.

    Article  Google Scholar 

  • Braxton, J. M. (Ed.). (2000). Reworking the student departure puzzle. Nashville: Vanderbilt University Press.

    Google Scholar 

  • Braxton, J. M., Sullivan, A. S., & Johnson, R. T. (1997). Appraising Tinto’s theory of college student departure. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (pp. 107–158). New York: Agathon.

    Google Scholar 

  • Burdman, P. (2005). The student debt dilemma: Debt aversion as a barrier to college access. Berkeley, CA: The Institute for College Access and Success.

    Google Scholar 

  • Cabrera, A. F., & La Nasa, S. M. (2000). Understanding the college choice process. In A. F. Cabrera & S. M. La Nasa (Eds.), Understanding the college choice of disadvantaged students: New directions for institutional research (pp. 5–22). San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Cabrera, A. F., Nora, A., & Castaneda, M. B. (1993). College persistence: Structural equations modeling test of an integrated model of student retention. The Journal of Higher Education, 64, 123–139.

    Article  Google Scholar 

  • California Community Colleges: Chancellor’s Office. (2013). California community colleges board of governors supports student success. Sacramento, CA: California Community Colleges. Retrieved from http://www.californiacommunitycolleges.cccco.edu/Portals/0/DocDownloads/PressReleases/NOV2013/PRESS_RELEASE_BOGGrants_111213_FINAL.pdf.

  • Campaigne, D. A., & Hossler, D. (1998). How do loans affect the educational decisions of students? Access, aspirations, college choice and persistence. In R. Fossey & M. Bateman (Eds.), Condemning students to debt, college loans and public policy. New York: Teachers College Press.

    Google Scholar 

  • Castellano, M., & Overman, L. T. (2009). Community college access and affordability for occupational and non-traditional students. Louisville, KY: National Research Center for Career and Technical Education, University of Louisville.

    Google Scholar 

  • Chapman, B. (2005). Income-contingent loans for higher education: International reform. CEPR Working Paper No. 491. Center for Economic Policy Research, Australian National University, Canberra, Australia.

  • Chen, R. (2007). Socioeconomic and racial/ethnic gaps in student dropout risks: A longitudinal model to examine the differential effects of financial aid. Doctoral dissertation, Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3276111).

  • Chen, R. (2008). Financial aid and student dropout in higher education: A heterogeneous research approach. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 23, pp. 209–239). Dordrecht, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Chen, R. (2011). Institutional characteristics and college student dropout risks: A multilevel event history analysis. Research in Higher Education, 53(5), 487–505.

    Article  Google Scholar 

  • Chen, J. (2013). Traditional ways of meeting nontraditional needs: A longitudinal investigation of the effects of financial aid on nontraditional student college success. Doctoral dissertation, Retrieved from Proquest Dissertation and Theses Global database. (UMI No. 3607004).

  • Chen, R., & DesJardins, S. L. (2008). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49, 1–18.

    Article  Google Scholar 

  • Chen, R., & DesJardins, S. L. (2010). Investigating the impact of financial aid on student dropout risks: Racial and ethnic differences. Journal of Higher Education, 81, 179–208.

    Article  Google Scholar 

  • Cheng, S., & Long, S. (2007). Testing for IIA in the multinomial logit model. Sociological Methods and Research, 35(4), 583–600.

    Article  Google Scholar 

  • Choitz, V., & Widom, R. (2003). Money matters: How financial aid affects nontraditional students in community colleges. New York: MDRC.

    Google Scholar 

  • Choy, S. P. (2002). Nontraditional undergraduates: Findings from “The Condition of Education, 2002”. Rep. No. ERIC Document Reproduction Service No. ED 471077. National Center for Education Statistics, Washington, DC.

  • Cofer, J., & Somers, P. (2000). Within-year persistence of students at two-year colleges. Community College Journal of Research and Practice, 24, 785–807.

    Article  Google Scholar 

  • Cohen, J., & Cohen, P. (1985). Applied multiple regression and correlation analysis for the behavioural science (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Suppl.), S95–S120.

    Article  Google Scholar 

  • de Leeuw, J., & Kreft, I. G. (1995). Questioning multilevel models. Journal of Educational Statistics, 20(2), 171–189.

    Article  Google Scholar 

  • DesJardins, S. L. (2003). Event history methods: Conceptual issues and an application to student departure from college. Higher education: Handbook of theory and research (pp. 421–471). Dordrecht: Springer.

    Chapter  Google Scholar 

  • DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (1999). An event history model of student departure. Economics of Education Review, 18, 375–390.

    Article  Google Scholar 

  • DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (2002). Simulating the longitudinal effects of changes in financial aid on student departure from college. Journal of Human Resources, 37, 653–679.

    Article  Google Scholar 

  • Dowd, A. C., & Coury, T. (2006). The effect of loans on the persistence and attainment of community college students. Research in Higher Education, 47, 33–62.

    Article  Google Scholar 

  • Dowd, A. C., & Shieh, L. T. (2013). Community college financing: Equity, efficiency, and accountability. The NEA 2013 Almanac of Higher Education, 37–65.

  • Drake, C. (1993). Effects of misspecification of the propensity score on estimators of treatment effects. Biometrics, 49(4), 1231–1236.

    Article  Google Scholar 

  • Dynarski, S. M. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 91, 279–288.

    Article  Google Scholar 

  • Goldrick-Rab, S., Harris, D. N., & Trostel, P. A. (2009). Why financial aid matters (or does not) for college success: Towards a new interdisciplinary perspective. Higher education: Handbook of theory and research (Vol. 24, pp. 1–45). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Guo, S., & Fraser, W. M. (2010). Propensity score analysis: Statistical methods and applications. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Hahs-Vaughn, D. L. (2005). A primer for using and understanding weights with national datasets. The Journal of Experimental Education, 73(3), 221–248.

    Article  Google Scholar 

  • Hausman, J. A., & McFadden, D. (1984). Specification tests for the multinomial logit model. Econometrica, 52, 1219–1240.

    Article  Google Scholar 

  • Hearn, J. C., & Holdsworth, J. M. (2004). Federal student aid: The shift from grants to loans. In E. P. St. John & M. D. Parsons (Eds.), Public funding of higher education: Changing contexts and new rationales (pp. 40–59). Baltimore, MA: The John Hopkins University Press.

    Google Scholar 

  • Heck, R. H., & Thomas, S. L. (2015). An introduction to multilevel modeling techniques: MLM and SEM approaches using Mplus. New York, NY: Routledge.

    Google Scholar 

  • Heckman, J., & Singer, B. (1984). A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica, 52(2), 271–320.

    Article  Google Scholar 

  • Henkel, R. E. (1976). Tests of significance. Thousand Oaks, CA: Sage.

    Book  Google Scholar 

  • Horn, L. J., & Carroll, C. D. (1996). Nontraditional undergraduates: Trends in enrollment from 1986 to 1992 and persistence and attainment among 1989–90 beginning postsecondary students (Rep. No. NCES 97-578). Washington, DC: National Center for Education Statistics, U.S. Department of Education.

  • Horn, L., Nevill, S., & Griffith, J. (2006). Profile of undergraduates in U.S. postsecondary education institutions: 2003–04: With a special analysis of community college students (NCES 2006-184). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

  • Hossler, D., Schmit, J., & Vesper, N. (1999). Going to college: How social, economic, and educational factors influence the decisions students make. Baltimore, MA: The John Hopkins University Press.

    Google Scholar 

  • Hossler, D., Shapiro, D., Dundar, A., Ziskin, M., Chen, J., Zerquera, D., & Torres, V. (2012). Transfer and mobility: A national view of pre-degree student movement in postsecondary institutions. Signature Report 2. Herndon, VA: National Student Clearinghouse.

  • Hossler, D., Ziskin, M., Sooyeon, K., Osman, C., & Gross, J. (2008). Student aid and its role in encouraging persistence. In S. Baum (Ed.), The effectiveness of student aid policies: What the research tells us (pp. 101–116). Washington, DC: College Board.

    Google Scholar 

  • Ishler, J., & Upcraft, M. L. (2005). The keys to first-year student persistence. In M. Upcraft, J. Gardner, B. Barefoot, et al. (Eds.), Challenging and supporting the first-year student: A handbook for improving the first year of college (pp. 27–46). San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Jacobs, J. A., & King, R. B. (2002). Age and college completion: A life-history analysis of women aged 15–44. Sociology of Education, 75, 211–230.

    Article  Google Scholar 

  • Jenkins, P. D., & Cho, S. W. (2012). Get with the program: Accelerating community college students’ entry into and completion of programs of study. New York: Columbia University, Community College Research Center. Retrieved from http://hdl.handle.net/10022/AC:P:12665.

  • Jones, M. P. (1996). Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Statistical Association, 91(433), 222–230.

    Article  Google Scholar 

  • Kim, D. (2007). The effect of loans on students’ degree attainment: Differences by student and institutional characteristics. Harvard Educational Review, 77(1), 64–100.

    Article  Google Scholar 

  • Knapp, L. G., Kelly-Reid, J. E., & Ginder, S. A. (2009). Enrollment in postsecondary institutions, fall 2007; graduation rates, 2001 & 2004 cohorts; and financial statistics, fiscal year 2007 (Rep. No. NCES 2009-155). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

  • Kuh, G. D., Kinzie, J., Buckley, J., Bridges, B., & Hayek, J. (2006). What matters to student success: A review of the literature. Washington, DC: National Postsecondary Education Cooperative.

    Google Scholar 

  • Laanan, F. S. (2003). Older adults in community colleges: Choices, attitudes, and goals. Educational Gerontology, 29(9), 757–776. doi:10.1080/716100372.

    Article  Google Scholar 

  • Lapovsky, L. (2008). Rethinking student aid: Nontraditional students. In S. Baum, M. McPherson, & P. Steele (Eds.), The effectiveness of student aid policies: What the research tells us (pp. 141–158). New York, NY: The College Board.

    Google Scholar 

  • Leow, C., Marcus, S., Zanutto, E., & Boruch, R. (2004). Effects of advanced course-taking on math and science achievement: Addressing selection bias using propensity scores. American Journal of Evaluation, 25(4), 461–478.

    Article  Google Scholar 

  • Lin, N. (2001). Social capital, A theory of social structure and action. Cambridge, NY: Cambridge University Press.

    Book  Google Scholar 

  • Long, J. S. (2005). Comparing groups using predicted outcomes (Working paper). Bloomington, IN: Indiana University.

    Google Scholar 

  • Long, B. T. (2007). The contributions of economics to the study of college access and success. Teachers College Record, 109(10), 2367–2443.

    Google Scholar 

  • Long, B. T. (2009). Financial aid and older workers: Supporting the nontraditional student. In M. Toussaint-Comeau & B. D. Meyer (Eds.), Strategies for improving economic mobility of workers: Bridging research and practice (pp. 109–126). Kalamazoon, MI: W.E. Upjohn Institute for Employment Research.

    Chapter  Google Scholar 

  • Long, J. S. (2012). Regression models for nominal and ordinal outcomes. In H. Best & C. Wolf (Eds.), The Sage handbook of regression analysis and causal inference. Thousand Oaks: Sage.

    Google Scholar 

  • Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using Stata (2nd ed.). College Station, TX: Stata Press.

    Google Scholar 

  • Martin, R. E. (2005). Cost control, college access, and competition in higher education. Cheltenham: Edward Elgar.

    Google Scholar 

  • McFadden, D., Train, K., & Tye, W. B. (1981). An application of diagnostic tests for the independence from irrelevant alternatives property of the multinomial logit model. Transportation Research Board Record, 637, 39–46.

    Google Scholar 

  • Metzner, B.S. (1983). An application and evaluation of a model of student attrition using freshmen at a public urban commuter university. Doctoral dissertation, Retrieved from Proquest Dissertation and Theses database. (UMI No. 8328080).

  • Mincer, J. (1974). Schooling, experience and earnings. New York: National Bureau of Economic Research.

    Google Scholar 

  • Murdock, T. A. (1990). Financial aid and persistence: An integrative review of the literature. NASPA Journal, 27, 213–221.

    Google Scholar 

  • Musoba, G., & Baez, B. (2009). The cultural capital of cultural and social capital: An economy of translation. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 8, pp. 151–182). Houten: Springer.

    Chapter  Google Scholar 

  • Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316.

    Article  Google Scholar 

  • Nora, A. (1990). Campus-based programs as determinants of retention among Hispanic college students. Journal of Higher Education, 61, 312–331.

    Article  Google Scholar 

  • Nora, A. (2004). The role of habitus and cultural capital in choosing a college, transitioning from high school to higher education, and persisting in college among minority and non-minority students. Journal of Hispanic Higher Education, 3(2), 180–208.

    Article  Google Scholar 

  • Nora, A., Cabrera, A. F., Hagedorn, L., & Pascarella, E. T. (1996). Differnetial impacts of academic and social experiences on college-related behavioral outcomes across different ethnic and gender groups at four-year institutions. Research in Higher Education, 37(4), 427–452.

    Article  Google Scholar 

  • Paulsen, K., & Boeke, M. (2006). Adult learners in the United States: A national profile. Washington, DC: National Center for Higher Education Management System.

    Google Scholar 

  • Paulsen, M. B., & St. John, E. P. (2002). Social class and college costs: Examining the financial nexus between college choice and persistence. The Journal of Higher Education, 73, 189–236.

    Article  Google Scholar 

  • Perkins, S. M., Tu, W., Underhill, M. G., Zhou, X., & Murray, M. D. (2000). The use of propensity scores in pharmacoepidemiologic research. Pharmacoepidemiology and Drug Safety, 9(2), 93–101.

    Article  Google Scholar 

  • Perna, L. W. (2000). Differences in the decision to attend college among African American, Hispanics, and Whites. Journal of Higher Education, 71(2), 117–141.

    Article  Google Scholar 

  • Perna, L. W. (2005). The relationship between parental involvement as social capital and college enrollment: An examination of racial/ethnic group differences. The Journal of Higher Education, 76(5), 485–518.

    Article  Google Scholar 

  • Perna, L. W. (2006). The contribution of financial aid to undergraduate persistence. Journal of Student Financial Aid, 28, 25–40.

    Google Scholar 

  • Perna, L. W., & Thomas, S. L. (2006). A framework for reducing the college success gap and promoting success for all. Washington, DC: National Postsecondary Education Cooperative.

    Google Scholar 

  • Perna, L. W., & Titus, M. (2005). The relaitonship between parental involvement as social capital and college enrollment: An examination of racial/ethnic group differences. Journal of Higher Education, 76, 485–518.

    Article  Google Scholar 

  • Pike, G. R., Kuh, G. D., & McKinley, R. (2008). First-year students’ employment, engagement, and academic achievement: Untangling the relationship between work and grades. NASPA Journal, 45, 560–582.

    Google Scholar 

  • Porta-Avalos, J. (2008). Factors influencing retention of non-traditional undergraduate students and effective retention strategies. Doctoral dissertation, Retrieved from Proquest Dissertation and Theses database. (UMI No. 3312646).

  • Portes, A. (1998). Social capital: Its origins and perspectives in modern sociology. Annual Review of Sociology, 243(1), 1–24.

    Article  Google Scholar 

  • Poterba, J. (1996). Government intervention in the markets of education and health care: How and why? In V. R. Funchs (Ed.), Individual and social responsibility. Chicago, IL: The University of Chicago Press.

    Google Scholar 

  • Pusser, B., Breneman, D. W., Gransneder, B. M., Kohl, K. J., Levin, J. S., Milam, J. H., et al. (2007). Returning to learning: Adults’ success in college is key to America’s future. Indianapolis: Lumina Foundation for Education. Retrieved February 11, 2012, from https://folio.iupui.edu/bitstream/handle/10244/270/ReturntolearningApril2007.pdf?sequence=1.

  • Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a two-year institution results in differences in educational attainment. Higher education: Handbook of theory and research (pp. 47–97). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Rosenbaum, P., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effect. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51, 1–17.

    Google Scholar 

  • Shapiro, D., Dundar, A., Chen, J., Ziskin, M., Park, E., Torres, V., et al. (2012). Completing college: A national view of student attainment rates. Signature™ Report 4. Herndon, VA: National Student Clearinghouse.

  • Singell, L. D., Jr. (2002). Merit, need, and student self-selection: Is there discretion in the packaging of aid at a large public university? Economics of Education Review, 21, 445–454.

    Article  Google Scholar 

  • Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.

    Book  Google Scholar 

  • Small, K. A., & Hsiao, C. (1985). Multinomial logit specification tests. International Economic Review, 26(3), 619–627.

    Article  Google Scholar 

  • Snyder, T. D. & Dillow, S. A. (2011). Digest of education statistics, 2010 (Rep. No. NCES 2011-015). Washington, DC: U.S. Department of Education.

  • St. John, E. P., Cabrera, A. F., Nora, A., & Asker, E. H. (2000). Economic influences on persistence reconsidered: How can finance research inform the reconceptualization of persistence models? In J. M. Braxton (Ed.), Reworking the student departure puzzle: New theory and research on college student retention (pp. 29–47). Nashville: Vanderbilt University Press.

    Google Scholar 

  • St. John, E., Paulsen, M., & Starkey, J. (1996). The nexus between college choice and persistence. Research in Higher Education, 37, 175–220.

    Article  Google Scholar 

  • St. John, E. P., & Tuttle, T. J. (2004). Financial aid and postsecondary opportunity for nontraditional age, pre-college students: The role of information and the education delivery systems. Indianapolis: The Education Research Institute and The Lumina Foundation.

    Google Scholar 

  • Stapleton, L. (2014). Incorporating sampling weights into single- and multi-level analyses. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis (pp. 363–388). Boca Raton, FL: CRC Press.

    Google Scholar 

  • Steele, F. (2011). Multilevel discrete-time event history models with applications to the analysis of recurrent employment transitions. Australian & New Zealand Journal of Statistics, 53, 1–20.

    Article  Google Scholar 

  • Taniguchi, H., & Kaufman, G. (2005). Degree completion among nontraditional college students. Social Science Quarterly, 86, 912–927.

    Article  Google Scholar 

  • Thomas, L. (2002). Student retention in higher education: The role of institutional habitus. Journal of Educational Policy, 17(4), 423–432.

    Article  Google Scholar 

  • Thomas, S. L., & Heck, R. H. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42(5), 517–540.

    Article  Google Scholar 

  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89–125.

    Article  Google Scholar 

  • Titus, M. A. (2007). Detecting selection bias, using propensity score matching, and estimating treatment effects: An application to the private returns to a master’s degree. Research in Higher Education, 48(4), 487–521.

    Article  Google Scholar 

  • Walpole, M. (2003). Socioeconomic status and college: How SES affects college experiences and outcomes. The Review of Higher Education, 27, 45–73.

    Article  Google Scholar 

  • Weidman, J. (1985). Retention of nontraditional students in postsecondary education. Paper presented at the 69th Annual Meeting of the American Educational Research Association, Chicago, IL. (ERIC Document Reproduction Service No. ED261195).

  • Wilson, B. J. (2006). Adults must be college-ready too: ABE-to-college transition project inspires lives. Connection: The Journal of the New England Board of Higher Education, 20(4), 25–26.

    Google Scholar 

  • Wine, J., Janson, N., & Wheeless, S. (2011). 2004/09 beginning postsecondary students longitudinal study (BPS:04/09) full-scale methodology report (NCES 2012-246). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Retrieved March 16, 2012, from http://nces.ed.gov/pubsearch.

  • Yamaguchi, K. (1991). Event history analysis. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Yamauchi, F. (2005). Race, equity and public schools in post-apartheid South Africa: Equal opportunity for all kids. Economics of Education Review, 24(2), 213–233.

    Article  Google Scholar 

Download references

Acknowledgments

This study is based upon work supported by the Association for Institutional Research, the National Center for Education Statistics and the National Science Foundation under the Association for Institutional Research Grant No. G12-53. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Association for Institutional Research, the National Center for Education Statistics or the National Sciences Foundation. The authors also wish to express gratitude to Dr. Steve DesJardins from University of Michigan, and two anonymous reviewers for their comments on the preliminary versions.

Author information

Authors and Affiliations

  1. Office of the Vice Provost for Faculty and Academic Affairs, Indiana University Bloomington, Bloomington, IN, 47408, USA

    Jin Chen

  2. School of Education, Indiana University Bloomington, Bloomington, IN, USA

    Don Hossler

Authors
  1. Jin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Don Hossler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Chen.

Appendices

Appendix 1: Estimates of Full EHA Model with PS Adjustment

See Tables 6, 7 and 8.

Table 6 Estimates based on full EHA model with PS adjustment for Pell Grants
Full size table
Table 7 Estimates based on full EHA model with PS adjustment for subsidized student loans
Full size table
Table 8 Estimates based on full EHA model with PS adjustment for unsubsidized student loans
Full size table

Appendix 2: Propensity Score Model Estimates

See Tables 9, 10 and 11.

Table 9 Model estimates of propensity scores for receiving Pell Grants
Full size table
Table 10 Model estimates of propensity scores for receiving subsidized student loans
Full size table
Table 11 Model estimates of propensity scores for receiving unsubsidized student loans
Full size table

Appendix 3: Stata Scripts

figure a

Appendix 4: Outcomes of Certificate Completers

Outcome

Weighted counts

%

Degree completion

56,687

17.95

Dropout

188,372

59.65

Still enrolled

70,728

22.40

Total

315,787

100

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Hossler, D. The Effects of Financial Aid on College Success of Two-Year Beginning Nontraditional Students. Res High Educ 58, 40–76 (2017). https://doi.org/10.1007/s11162-016-9416-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-016-9416-0

Keywords

Search

Navigation