Model-based Analysis of Diversity in Higher Education

U.S. higher education is an example of a large multi-organizational system within the service sector. Its performance regarding workforce development can be analyzed through the lens of industrial and systems engineering. In this three-essay dissertation, we seek the answer to the following question: How can the U.S. higher education system achieve an equal representation of female and minority members in its student and faculty populations? In essay 1, we model the education pipeline with a focus on the system's gender composition from k-12 to graduate school. We use a system dynamics approach to present a systems view of the mechanisms that affect the dynamics of higher education, replicate historical enrollment data, and forecast future trends of higher education's gender composition. Our results indicate that, in the next two decades, women will be the majority of advanced degree holders. In essay 2, we look at the support mechanisms for new-parent, tenure-track faculty in universities with a specific focus on tenure-clock extension policies. We construct a unique data set to answer questions around the effectiveness of removing the stigma connected with automatic tenure-clock policies. Our results show that such policies are successful in removing the stigma and that, overall, faculty members that have newborns and are employed by universities that adopt auto-TCE policies stay one year longer in their positions than other faculty members. In addition, although faculty employed at universities that adopt such policies are generally more satisfied with their jobs, there is no statistically significant effect of auto TCE policies on the chances of obtaining tenure. In essay 3, we focus on the effectiveness of training underrepresented minorities (e.g., African Americans and Hispanics) in U.S. higher education institutions using a Data Envelopment Analysis approach. Our results indicate that graduation rates, average GPAs, and post-graduate salaries of minority students are higher in selective universities and those located in more diverse towns/cities. Furthermore, the graduation rate of minority students in private universities and those with affirmative action programs is higher than in other institutions. Overall, this dissertation provides new insights into improving diversity within the science workforce at different organizational levels by using industrial and systems engineering and management sciences methods.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Andalib, Maryam Alsadat

Chair dc:contributor.committeechair

• Ghaffarzadegan, Navid

Committee members dc:contributor.committeemember

• Hawley, Joshua D.
• Van Aken, Eileen M.
• Hosseinichimeh, Niyousha
• Triantis, Konstantinos P.

• Diversity
• Gender
• Race
• Education, Higher
• System Dynamics
• Statistical Analysis
• Data Envelopment Analysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:15694

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/96221