Data Analysis for Professional Engineering Credentialing Inequity

Engineering licensure is pivotal for ensuring the credibility of the profession and empowering engineers to fulfill their duty of prioritizing public health and safety. However, licensure requirements in the United States vary, with a common framework involving an engineering education degree and the completion of the Fundamentals of Engineering (FE) and Professional Engineering (PE) exams.

Concerns arise within the engineering realm due to disparate licensure opportunities. Engineers, engineering technologists, and engineering technicians constitute the three primary groups in traditional fields like civil and mechanical engineering. Certain state licensing boards impose restrictions on technologists obtaining PE licenses and hinder the reciprocity of these licenses across jurisdictions.

This study seeks to pinpoint U.S. colleges offering accredited or technologist degrees, exploring correlations with factors like tuition, racial demographics, enrollment, and financial aid. A graphical analysis will then unveil potential disparities, particularly focusing on whether specific demographic groups face greater challenges with different accreditation types. Anticipated outcomes include the expectation that marginalized communities, characterized by minority races and lower-income backgrounds, may predominantly hold technologist degrees, potentially leading to inferior overall outcomes. The study’s results will serve as an updated statistical reference, shedding light on imbalances in degree production and student success among engineers and engineering technologists in Civil and Mechanical fields.

I’m Brian Mensah and the title of my research paper is a data analysis for professional engineering credentialing inequity. For background, the basis of this study is that typically different colleges offer either engineering technology degrees or simply engineering degrees. For example, the title of the degree might be Mechanical Engineering Technology or simply Mechanical Engineering.

The difference between these two degrees are vast in terms of outcome, coursework, and cost. Technologist degrees have less technical and mathematical components. For example, these students won’t take calculus, but instead will have more applied and hands on courses. As a result, technologist degrees do not prepare students for engineering positions.

Instead, they prepare them for engineering technician. Or engineering technologist positions. Additionally, there’s a difference in price. The U. S. Department of Education has found that on the lower range, accredited engineering programs are 30, 000 and can be up to 50, 000 a year in cost versus for just technology accredited programs being 5, 000 to 15, 000.

Due to these differences, the entry and outcomes of success are entirely different for engineering versus engineering technology graduates.

In many states, people with engineering technology degrees are not allowed to get engineering licensure. As a result, engineering technology graduates have been found by the American Society of Engineers to have completely different roles and lower salaries and job outlooks than engineering graduates.

The intent of the study is that by identifying and addressing the inequities within engineering credentialing, we can help to ensure that all individuals have the opportunity to pursue successful and rewarding careers in this field. Knowing the previous tuition data, I assessed different colleges for racial and gender demographics of degrees awarded in search of a trend between the factors.

This data was accessed through the integrated post secondary education data system aka IPEDS, which is run by the National Center of Educational Statistics. Through them, I was able to identify which colleges provided what type of degree, Within the civil and mechanical fields, then through the American Society of Engineering Education’s degree data, I was able to find the racial demographics of degrees awarded through each reported year.

These were both large scale self reported institutional surveys. This is an example of an exported Excel file from iPads that I analyzed. As you can see, this data set provided the school name, city, state, country, website of the institution, and then most importantly, the program name and the type of accreditation.

Data was taken and aggregated into institutions that provided only technical degrees. Versus engineering accredited degrees. Some institutions provided both, but we’re left out of the current data analysis. The key on the right shows the categories of race use taken from the American society of engineering education, looking at the national data for both engineering technology and engineering.

White males were the majority, but there seemed to be slight trends within smaller groups. When looking closely from engineering technology to just engineering, the trends noticed are that the percentage of Hispanic and Black male degrees awarded almost halved going from EAC to ETAC. It was also found that white females and males had an increased percentage in EAC colleges.

When looking at just Virginia data, the trend was shifted. The only engineering technology institution in Virginia is VSU, a historically black college. So although a large number of race was unreported, 28 percent of engineering technology degrees consisted of black males. Percentages appeared to be similar to the national data for just engineering accreditation.

Despite the slight trends in the current data set, due to the lack of national technologist data available through the American Society of Engineering Education, and the large portion of unknown reported degrees awarded, I cannot yet confirm any correlation of racial bias for people heading into technologist degrees.

But as the Virginia data was completed, a possible conclusion based on this is that institutions with generally lower income demographics may be unable to provide the caliber of education required to become accredited by a review board or cannot afford accreditation due to lower funding from lower tuition costs or other factors.

In order to strengthen the data, new sources for degree data to complete the institutions that were missing Along with completing geographic and income analysis could be utilized.

This study was completed under the mentorship of Dr. Girum Urgessa ,along with funding from the GMU Oscar URSP. And the following are my works cited.