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Feature Selection of Post-graduation Income of College Students in the United States

  • Conference paper
  • First Online:
Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10899))

Abstract

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. We discuss how higher numbers of students in a cohort who grew up in Zip code areas where over 25% of the population hold a Professional Degree was predictive of more college graduates being classified as High income.

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Notes

  1. 1.

    Social capital represents trust, solidarity, and reciprocity in collective social interactions and engagement in community-based activities [16].

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Author information

Authors and Affiliations

  1. University of Hong Kong, Pokfulam, Hong Kong SAR

    Ewan Wright

  2. Western Washington University, Bellingham, WA, USA

    Qiang Hao

  3. University of Georgia, Athens, GA, USA

    Khaled Rasheed

  4. University of British Columbia, Vancouver, Canada

    Yan Liu

Authors
  1. Ewan Wright
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  2. Qiang Hao
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  3. Khaled Rasheed
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  4. Yan Liu
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Corresponding author

Correspondence to Ewan Wright .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, New York, USA

    Robert Thomson

  2. Bucknell University, Lewisburg, Pennsylvania, USA

    Christopher Dancy

  3. The Ohio State University, Columbus, Ohio, USA

    Ayaz Hyder

  4. University of Michigan–Flint, Flint, Michigan, USA

    Halil Bisgin

Appendix A

Appendix A

The dataset analyzed in this study can be accessed at https://collegescorecard.ed.gov/data/.

30 potential attributes include:

Group One: School information

  1. 1.

    School Type (e.g. private school)

  2. 2.

    Predominant Awarded Degrees (e.g., Bachelor degree)

  3. 3.

    Student Size

  4. 4.

    Instructional Expenditure per Student

  5. 5.

    Ratio between Part-time and Full-time Students

  6. 6.

    Degree Completion Rate

  7. 7.

    Average Faculty Salary

Group Two: Admission information

  1. 8.

    Admission Rate

  2. 9.

    Average SAT Score

Group Three: Cost information

  1. 10.

    In-State Tuition

  2. 11.

    Out-of-State Tuition

Group Four: Student information

  1. 12.

    Percentage of White Students

  2. 13.

    Percentage of Black Students

  3. 14.

    Percentage of Asian Students

  4. 15.

    Percentage of American Indian Students

  5. 16.

    Percentage of Hispanic Students

  6. 17.

    Percentage of Female Students

  7. 18.

    Percentage of First-Generation Students

  8. 19.

    Average Age of Entering College

  9. 20.

    Average Debt

Group Five: Family and community information

  1. 21.

    Percentage of Students whose Family Income was classified as Low

  2. 22.

    Percentage of Students whose Family Income was classified as Lower Middle

  3. 23.

    Percentage of Students whose Family Income was classified as Higher Middle

  4. 24.

    Percentage of Students whose Family Income was classified as High

  5. 25.

    Percentage of Students whose Family Income was classified as Very High

  6. 26.

    Percentage of Students whose Parents were 1st Generation College Student

  7. 27.

    Percentage of Students whose Parents Have a Middle School Degree

  8. 28.

    Percentage of Students whose Parents Have a High School Degree

  9. 29.

    Percentage of Students whose Parents Have a Post-High-School Degree

  10. 30.

    Population from Students’ Zip Codes over 25% with a Professional Degree.

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Wright, E., Hao, Q., Rasheed, K., Liu, Y. (2018). Feature Selection of Post-graduation Income of College Students in the United States. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93371-9

  • Online ISBN: 978-3-319-93372-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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