Skip to main content
Log in

Understanding the Link Between Noncognitive Attributes and College Retention

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

Abstract

The attention to students’ noncognitive attributes has recently flourished within academic research and public discourse. This paper adds to the literature by examining the interrelationships among several key noncognitive attributes as well as exploring direct and indirect relationships between noncognitive attributes and second-year retention. Within a multi-institutional sample of 10,622 students, academic self-efficacy, academic grit, self-discipline, and time management all load onto a single noncognitive factor with strong inter-item correlations and internal reliability. Moreover, structural equation modeling analyses indicate a sizable and positive indirect effect of noncognitive attributes on college retention, which is mediated by social adjustment, institutional commitment, and college grade point average.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Akos, P., & Kretchmar, J. (2017). Investigating grit as a non-cognitive predictor of college success. Review of Higher Education, 40(2), 163–186.

    Article  Google Scholar 

  • Astin, A. W. (1970). The methodology of research on college impact, part one. Sociology of Education, 43(3), 223–254.

    Article  Google Scholar 

  • Astin, A. W., & antonio, a. l. (2012). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education (2nd ed.). New York: Rowman and Littlefield/American Council on Education.

    Google Scholar 

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavior change. Psychological Review, 84, 191–215.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Bean, J., & Eaton, S. (2000). A psychological model of college student retention. In J. M. Braxton (Ed.), Reworking the student departure puzzle (pp. 48–61). Nashville, TN: Vanderbilt University Press.

    Google Scholar 

  • Berger, J. B., Ramirez, G. B., & Lyons, S. (2012). Past to present: A historical look at retention. In A. Seidman (Ed.), College student retention: Formula for student success (2nd ed., pp. 7–34). Lanham, MD: Rowman and Littlefield.

    Google Scholar 

  • Bowman, N. A., Hill, P. L., Denson, N., & Bronkema, R. (2015). Keep on truckin’ or stay the course? Exploring grit dimensions as differential predictors of educational achievement, satisfaction, and intentions. Social Psychological and Personality Science, 6, 639–645.

    Article  Google Scholar 

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

    Google Scholar 

  • Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.

    Google Scholar 

  • Cabrera, A. F., Nora, A., & Castañeda, M. B. (1992). The role of finances in the persistence process: A structural model. Research in Higher Education, 33, 571–593.

    Article  Google Scholar 

  • Caspi, A., Roberts, B. W., & Shiner, R. L. (2005). Personality development: Stability and change. Annual Review of Psychology, 56, 453–484.

    Article  Google Scholar 

  • Chen, R. (2012). Institutional characteristics and college student dropout risks: A multilevel event history analysis. Research in Higher Education, 53(5), 487–505.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Connelly, B. S., & Ones, D. S. (2010). An other perspective on personality: Meta-analytic integration of observers’ accuracy and predictive validity. Psychological Bulletin, 136, 1092–1122.

    Article  Google Scholar 

  • Credé, M., & Niehorster, S. (2012). Adjustment to college as measured by the Student Adaptation to College Questionnaire: A quantitative review of its structure and relationships with correlates and consequences. Educational Psychology Review, 24, 133–165.

    Article  Google Scholar 

  • Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113, 492–511.

    Article  Google Scholar 

  • Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087–1101.

    Article  Google Scholar 

  • Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit-S). Journal of Personality Assessment, 91, 166–174.

    Article  Google Scholar 

  • Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237–251.

    Article  Google Scholar 

  • Eskreis-Winkler, L., Shulman, E. P., Beal, S. A., & Duckworth, A. L. (2014). The grit effect: Predicting retention in the military, the workplace, school and marriage. Frontiers in Psychology, 5(36), 1–12.

    Google Scholar 

  • Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., et al. (2012). Teaching adolescents to become learners. The role of noncognitive factors in shaping school performance: A critical literature review. Chicago: University of Chicago Consortium on Chicago School Research.

    Google Scholar 

  • Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269–314). Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Furr, R. M., & Bacharach, V. R. (2008). Psychometrics: An introduction. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Heck, R. H., & Thomas, S. L. (2009). An introduction to multilevel modeling techniques (2nd ed.). New York: Routledge.

    Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

    Article  Google Scholar 

  • Hunter, M. S., & Linder, C. W. (2005). First-year seminars. In M. L. Upcraft, J. N. Gardner, & B. O. Barefoot (Eds.), Challenging and supporting the first-year student: A handbook for improving the first year of college (pp. 275–291). San Francisco: Jossey-Bass.

    Google Scholar 

  • Integrated Postsecondary Education Data System. (2016). IPEDS data center: Custom data files (data file). https://nces.ed.gov/ipeds/datacenter/InstitutionByGroup.aspx.

  • Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. Journal of Higher Education, 77(5), 861–885.

    Article  Google Scholar 

  • Kline, R. B. (2012). Assumptions in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 111–125). New York: Guilford.

    Google Scholar 

  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: Guilford.

    Google Scholar 

  • Lavaan. (n.d.). Categorical data. http://lavaan.ugent.be/tutorial/cat.html.

  • Li, D. (2010). They need help: Transfer students from four-year to four-year institutions. Review of Higher Education, 33(2), 207–238.

    Google Scholar 

  • Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Mayhew, M. J., Rockenbach, A. N., Bowman, N. A., Seifert, T. A., Wolniak, G. C., Pascarella, E. T., et al. (2016). How college affects students (vol. 3): 21st century evidence that higher education works. San Francisco: Jossey-Bass.

    Google Scholar 

  • Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38, 30–38.

    Article  Google Scholar 

  • Museus, S. D. (2014). The culturally engaging campus environments (CECE) model: A new theory of college success among racially diverse student populations. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 29, pp. 189–227). New York, NY: Springer.

    Chapter  Google Scholar 

  • Pan, Y.-J. (2010). Modeling the effects of academic and social integration on college student success: A systematic review. Unpublished Doctoral Dissertation, University of Louisville.

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco: Jossey-Bass.

    Google Scholar 

  • Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). New York: Wadsworth.

    Google Scholar 

  • Poropat, A. E. (2009). A meta-analysis of the Five-Factor Model of personality and academic performance. Psychological Bulletin, 135, 322–338.

    Article  Google Scholar 

  • Porter, S. R. (2006). What can multilevel models add to institutional research? In M. A. Coughlin (Ed.), Applications of intermediate/advanced statistics in institutional research (resources in institutional research, Vol. 16). Tallahassee, FL: Association for Institutional Research.

  • Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353–387.

    Article  Google Scholar 

  • Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130, 261–288.

    Article  Google Scholar 

  • Roberts, B. W., & DelVecchio, W. F. (2000). The rank-order consistency of personality traits from childhood to old age: A quantitative review of longitudinal studies. Psychological Bulletin, 126(1), 3–25.

    Article  Google Scholar 

  • Roberts, B. W., Lejuez, C., Krueger, R. F., Richards, J. M., & Hill, P. L. (2014). What is conscientiousness and how can it be assessed? Developmental Psychology, 50, 1315–1330.

    Article  Google Scholar 

  • Robertson-Kraft, C., & Duckworth, A. L. (2014). True grit: Trait-level perseverance and passion for long-term goals predicts effectiveness and retention among novice teachers. Teachers College Record, 116(3), 1–27.

    Google Scholar 

  • Roksa, J. (2010). Bachelor’s degree completion across state contexts: Does the distribution of enrollments make a difference? Research in Higher Education, 51(1), 1–20.

    Article  Google Scholar 

  • Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.

    Article  Google Scholar 

  • Rowan-Kenyon, H. T., Savitz-Romer, M., Ott, M. W., Swan, A. K., & Liu, P. P. (2017). Finding conceptual coherence: Trends and alignment in the scholarship on noncognitive skills and their role in college success and career readiness. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 32, pp. 141–179). New York: Springer.

    Chapter  Google Scholar 

  • Schuh, J. H., & Gansemer-Topf, A. (2012). Finances and retention: Trends and potential implications. In A. Seidman (Ed.), College student retention: Formula for student success (2nd ed., pp. 101–118). Lanham, MD: Rowman and Littlefield.

    Google Scholar 

  • Strayhorn, T. L. (2014). What role does grit play in the academic success of Black male collegians at predominantly White institutions? Journal of African American Studies, 18, 1–10.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Needham Heights, MA: Allyn and Bacon.

    Google Scholar 

  • Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.

    Google Scholar 

  • Tough, P. (2012). How children succeed: Grit, curiosity, and the hidden power of character. New York: Houghton Mifflin Harcourt.

    Google Scholar 

  • Walton, G. M. (2014). The new science of wise psychological interventions. Current Directions in Psychological Science, 23(1), 73–82.

    Article  Google Scholar 

  • Yeager, D. S., Paunesku, D., Walton, G. M., & Dweck, C. S. (2013). How can we instill productive mindsets at scale? A review of the evidence and an initial R&D agenda. In Paper presented at the White House Meeting on Excellence in Education: The importance of academic mindsets, Washington, DC.

  • Yeager, D. S., & Walton, G. M. (2011). Social–psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicholas A. Bowman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bowman, N.A., Miller, A., Woosley, S. et al. Understanding the Link Between Noncognitive Attributes and College Retention. Res High Educ 60, 135–152 (2019). https://doi.org/10.1007/s11162-018-9508-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-018-9508-0

Keywords

Navigation