Analyzing the Dimensionality of O*NET Cognitive Ability Ratings to Inform Assessment Design

Abstract

The O*NET database is an online repository of detailed information on the knowledge and skill requirements of thousands of jobs across the United States. Thus, it is a valuable resource for test developers who want to target cognitive and other abilities relevant to the contemporary workforce. In this study, we used multidimensional scaling (MDS) to analyze the mean importance ratings of the cognitive abilities and selected skills included in the O*NET database to identify the dimensionality of the data regarding importance and their consistency across job zones. Using the criteria of fit and interpretability, a two-dimensional MDS solution was selected as the best representation of the data. These dimensions reflected Social Interaction/Reasoning and Verbal/Non-Verbal skills and abilities. Interestingly, the dimensionality was not consistent across job zones. Job zones relative to lower education and training requirements were sufficiently represented by the Social Interaction/Reasoning dimension, and the Verbal/Non-Verbal dimension was most relevant to job zones requiring more education and experience. The implications of the results for developing assessments for adult learners are discussed, as is the utility of using MDS for understanding the dimensionality of O*NET data.