Collecting job analysis ratings for a large number of jobs via surveys, interviews, or focus groups can put a very large burden on organizations. In this study, we describe and evaluate a streamlined, natural language processing-based approach to estimating (a) the importance of various knowledges, skills, abilities, and other characteristics (KSAOs) to jobs, and (b) how descriptive various interests are of work on a job. Specifically, we evaluate whether we can train a machine to accurately estimate KSAO ratings for jobs using job description and task statement text as the sole input. Data for 963 occupations from the U.S. Department of Labor’s Occupational Information Network (O*NET) system and an independent set of 229 occupations from a large organization provided the basis for the evaluation. Our approach produced KSAO predictions that had cross-validated correlations with subject matter expert (SME) ratings of knowledges, skills, abilities, and interests of .74, .80, .75, and .84, respectively (on average, across the 126 KSAOs examined). We found clear evidence for the validity of machine-based predictions based on (a) convergence among machine-based and SME-furnished ratings, (b) conceptually meaningful patterns of prediction model regression coefficients among the KSAOs examined, and (c) conceptual relevance of top predictor models underlying related clusters of KSAOs. We also found that prediction models developed on O*NET data produced meaningful results when applied to an independent set of job descriptions and tasks. Implications of this work, as well as suggested directions for future job analysis research and practice, are discussed.

通过调查、访谈或焦点小组收集大量工作的工作分析评级会给组织带来非常大的负担。在这项研究中，我们描述和评估了一种基于自然语言处理的流线型方法来估计 (a) 各种知识、技能、能力和其他特征 (KSAO) 对工作的重要性，以及 (b) 各种兴趣的描述性的工作。具体来说，我们评估我们是否可以训练机器使用工作描述和任务说明文本作为唯一输入来准确估计工作的 KSAO 评级。来自美国劳工部职业信息网络 (O*NET) 系统的 963 个职业的数据和来自大型组织的 229 个职业的独立集合为评估提供了基础。我们的方法产生了 KSAO 预测，这些预测与主题专家 (SME) 对知识、技能、能力和兴趣的评分分别为 0.74、0.80、0.75 和 0.84（平均在 126 KSAOs 检查）。我们发现了基于机器的预测有效性的明确证据，基于 (a) 基于机器的评级和 SME 提供的评级之间的收敛，(b) 所检查的 KSAO 中预测模型回归系数的概念上有意义的模式，以及 (c) 概念相关性KSAO相关集群的顶级预测模型。我们还发现，基于 O*NET 数据开发的预测模型在应用于一组独立的工作描述和任务时会产生有意义的结果。这项工作的意义，以及对未来工作分析研究和实践的建议方向，