No abstract

Occupations are typically characterized in nominal form, a format that limits options for hypothesis testing and data analysis. We drew upon ratings of knowledge, skills, and abilities for 966 occupations listed in the US Department of Labor’s Occupational Classification Network (O*NET) database to create an accessible, standardized multidimensional space in which occupations can be quantitatively localized and compared. Principal components analysis revealed that the occupation space comprises three main dimensions that correspond to: 1) the required amount of education and training, 2) the degree to which an occupation falls within a science, technology, engineering, and mathematics (STEM) discipline versus social sciences and humanities, and 3) whether occupations are more mathematical or health-related. Data-driven groupings of related occupations were obtained with hierarchical cluster analysis (HCA). We provide a freely accessible tool: Visualization of Latent Components Assessed in o*Net Occupations (VOLCANO), a shiny app for users to extract quantitative scores along the relevant dimensions, bringing much-needed standardization to unwieldy occupational data. Moreover, VOLCANO can also be to create new occupational spaces customized to specific research domains.