Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Fagbamigbe Thesis.pdf (3.98 MB)

Digital Accessibility Report

File List


Fagbamigbe Thesis.pdf.accreport.html (7.91 KB)

ETD Abstract Container

Abstract Header

Examining Gender Equality in the United States Undergraduate Enrollment Using Hidden Markov Model

Fagbamigbe, Kehinde Victor
http://orcid.org/0009-0004-1673-6247
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169933387498171

Abstract Details

2023, Master of Science (MS), Bowling Green State University, Applied Statistics (ASOR).
This thesis delves into the intricate analysis of multidimensional enrollment records spanning two and half decades, covering various regions within U.S. academic institutions. Leveraging Hidden Markov Models (HMMs) with hypothesis testing, we seek to unearth underlying patterns and trends in higher education enrollment. The study commences with data preparation, involving the extraction and aggregation of data across institutions, majors, and genders over two and half decades, resulting in a rich, 4-dimensional dataset. To streamline the analysis, the dataset is divided into nine geographic divisions. The results offer a granular view of enrollment trends, enabling institutions and policymakers to make informed decisions regarding diversity and education equality. This research bridges the gap in modelling matrix-variate data within the HMM framework, addressing the unique challenges posed by multidimensional datasets. By adopting this innovative approach, we contribute valuable insights to the field of higher education enrollment analysis, facilitating more inclusive and data-driven educational policies. This thesis comprises four chapters. The first chapter talks about the motivation and objective of this paper, and the second chapter introduces the HMM tailored for matrix-variate time series data. The third chapter demonstrates the application of this model with hypothesis testing using undergraduate enrollment data from US institutions, and the fourth chapter concludes with the key findings and the significance of this method.
Shuchismita Sarkar, Ph.D. (Committee Chair)
Umar Islambekov, Ph.D. (Committee Member)
Yuhang Xu, Ph.D. (Committee Member)
62 p.
Education; Education Policy; Statistics
Clustering Algorithm; Hidden Markov Model; EM Algorithm; Hypothesis Test;

Recommended Citations

Citations

  • Fagbamigbe, K. V. (2023). Examining Gender Equality in the United States Undergraduate Enrollment Using Hidden Markov Model [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169933387498171

    APA Style (7th edition)

  • Fagbamigbe, Kehinde. Examining Gender Equality in the United States Undergraduate Enrollment Using Hidden Markov Model. 2023. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169933387498171.

    MLA Style (8th edition)

  • Fagbamigbe, Kehinde. "Examining Gender Equality in the United States Undergraduate Enrollment Using Hidden Markov Model." Master's thesis, Bowling Green State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169933387498171

    Chicago Manual of Style (17th edition)

bgsu169933387498171
84
© 2023, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.
Release 3.2.12