Discipline: Computer Sciences & Information Management
Subcategory: STEM Research
Raymond Elliott - Savannah State University
Co-Author(s): Shetia Butler Lamar, Savannah State University, Savannah, GA; David Simmonds, Savannah State University, Savannah, GA; Suman Niranjan, University of North Texas, Denton, TX
Because of disabilities, people may face many types of different roadblocks or challenges. Many will go through life and manage to overcome obstacles that have the potential to stand in the way of them achieving their goals. People with disabilities tend to work in lower-skilled jobs with limited educational and experience requirements. Previous authors have evaluated the gap between disabilities and education level. Mizunoya and Mitra (2012) examined this gap in developing countries. While Brucker, Mitra, Chaitoo, and Mauro (2014) analyzed the gap between people with disabilities and poor income. We propose that the following factors will also affect people’s ability to overcome challenges related to dealing with their individual disabilities: culture in country of birth, education level, and job occupation. In addition, we believe that different types of disabilities will either have a positive or negative impact on the person’s career. We also look at the implications related to people with different types of disabilities and the highest degree they have attained. Based on Maroto and Pettinicchio (2013) who found that disability creates disconnect with workplace and women and racial minorities undergo segregation in the workplace, we go a step further by looking at the degree level attained as well as the major they went into and the job occupation. We believe that these factors will have different impacts based on country of birth. We will analyze, using OLS regression, the raw data from the Scientists and Engineers Statistical Data System (SESTAT).The total sample size for the 2017 National Survey of College Graduates (NSCG) which was used is 105,000. The 2017 Survey of Doctorate Recipients (SDR) sample consisted of different cases of types of disabilities selected systematically across strata.
Funder Acknowledgement(s): NSF:HBCU-UP: TIP- Interdisciplinary Data Analytics
Faculty Advisor: None Listed,
NSF Affiliation: HBCU-UP