Linguistic Analysis of Archival Human Factors Jobs Data

Abstract

To continually track changing skills and knowledge requirements for human factors professionals in the labor market, an archival human factors jobs database was created. The database contains relevant information from human factors job announcements, including required degree and field of education, required and preferred experience, and information from the job descriptions. The primary purpose of the database is to facilitate longitudinal analyses of employers’ expectations. Results from these analyses should be used in the design of academic human factors programs and curricula to better prepare future human factors professionals meet such expectations upon their graduation. In this paper we describe a natural language processing approach to analysis of human factors jobs data. Several trends emerged from our analyses. Although we defer drawing conclusions from these trends until later, until the database contains much more data that are more evenly distributed in time, our methods are detailed here for others to make use in analyses of the jobs data freely available in the database.