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Illuminating the Post-Graduation Impact of Undergraduate Participation in High-Impact Practices Using Propensity Score Analysis with Structural Equation Modeling

Author

Listed:
  • Joanna L. Dickert

    (Kent State University
    Carnegie Mellon University)

  • Jian Li

    (Kent State University)

Abstract

As colleges and universities grapple with uncertainty around current and future enrollment as well as increasingly vocal questions about the value of postsecondary education, it is critically important that institutions ascertain and invest in the elements of campus learning and engagement that add value to the undergraduate experience. This study examines the relationship between cumulative participation in high-impact practices (HIPs) and the perceived importance of postsecondary experience in preparation for adult life using the Educational Longitudinal Study of 2002 (ELS) dataset. Employing a methodology proposed and tested by Leite et al. (Struct Equ Model Multidiscip J 26(3):448–469, 2019. https://doi.org/10.1080/10705511.2018.1522591 ), this analysis incorporated the ability to account for self-selection into HIPs using propensity score (PS) analysis with a multiple-group structural equation model (SEM) design to examine differences between students who participated in two or more HIPs and those who did not (n = 3105). Results offered evidence of benefit to participation in two or more HIP experiences with positive and statistically significant differences in the perceived importance of postsecondary education in preparation for adult life across the analytic sample with doubly robust estimation techniques. Interaction effects for female students, students from low SES backgrounds, and students who are members of minoritized racial/ethnic populations were also identified. The findings offered evidence of post-graduation impact of cumulative participation in HIPs that can inform program development and student decision-making as well as the future use of analytic techniques such as PS analysis, doubly robust estimation, and sensitivity analysis to enhance measurement precision.

Suggested Citation

  • Joanna L. Dickert & Jian Li, 2024. "Illuminating the Post-Graduation Impact of Undergraduate Participation in High-Impact Practices Using Propensity Score Analysis with Structural Equation Modeling," Research in Higher Education, Springer;Association for Institutional Research, vol. 65(5), pages 943-964, August.
    • Handle: RePEc:spr:reihed:v:65:y:2024:i:5:d:10.1007_s11162-023-09767-2
    DOI: 10.1007/s11162-023-09767-2
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    References listed on IDEAS

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    1. Tricia A. Seifert & Benjamin Gillig & Jana M. Hanson & Ernest T. Pascarella & Charles F. Blaich, 2014. "The Conditional Nature of High Impact/Good Practices on Student Learning Outcomes," The Journal of Higher Education, Taylor & Francis Journals, vol. 85(4), pages 531-564, July.
    2. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
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