Abstract This chapter sets the stage by focusing on the concept of validity, documenting key changes over time in how the term is used and examining the specific ways in which the concept is instantiated in the domain of personnel selection. It then moves from conceptual to operational and discusses issues in the use of various strategies to establish what is termed the predictive inference, namely, that scores on the predictor measure of interest can be used to draw inferences about an individual’s future job behavior or other criterion of interest. Finally, the chapter addresses a number of specialized issues aimed at illustrating some of the complexities and nuances of validation.
Abstract Despite predictions of the “death of jobs” due to economic shifts and technological changes, job analysis remains the foundational “backbone” of human resource management. This chapter demonstrates how traditional job analysis methods are evolving to make use of machine learning and artificial intelligence (AI) while maintaining their core purpose: systematically documenting job tasks and identifying required knowledge, skills, abilities, and other characteristics. The chapter outlines diverse applications of job analysis, from creating position descriptions and developing selection processes to supporting career exploration and succession planning. Two primary methodological approaches are examined: inductive methods (job/task analysis and the critical incident technique) that generate job-specific information, and deductive methods (like the U.S. Department of Labor’s Occupational Information Network, or O*NET) that apply standardized taxonomies across occupations for comparison purposes. The chapter also presents an eight-stage AI-enhanced job analysis lifecycle that extends from strategic planning through ongoing maintenance, demonstrating how technology complements rather than replaces traditional methods. Human oversight remains essential for contextual understanding, bias detection, and ensuring methodological rigor. Recent innovations in job analysis include using machine learning to predict occupational profiles and developing frameworks for capturing technology-related worker requirements. The future of job analysis lies in thoughtfully integrating human expertise with technological capabilities, maintaining the systematic foundation necessary for informed talent management decisions, while dramatically improving efficiency and scalability in our rapidly evolving work environment.
Bibliometric reviews have increased in popularity as a way to summarize literature areas using quantitative, network-based methods. When conducting such reviews, it is assumed that bibliometric data are sufficiently accurate that errors associated with author names and references are unlikely to influence the results. We challenge this notion and discuss the types of errors (usually hidden) that compromise bibliometric data as well as the consequences of those errors. We provide a tutorial and R code for cleaning bibliometric data that focuses on identifying and fixing various types of errors. We demonstrate this with an illustrative example and also a re-analysis of a previously published bibliometric review. In both examples, we show that typical approaches to the handling of bibliometric data can produce inaccurate results and conclusions. The alternative that we develop and illustrate allows the bibliometrician to avoid these inaccuracies.
IntroductionEmotional intelligence (EI) is widely recognized as critical to supervisory effectiveness. However, because EI is enacted and interpreted within relationships, it should be studied not only as individual ability but also as behavior and perception. This study examines how alignment or misalignment between supervisors’ and subordinates’ perceptions of supervisor emotionally intelligent behavior (EIB) relates to subordinate well-being, above and beyond supervisors’ actual EI ability.MethodsData were collected from 202 supervisors and 2,055 subordinates across five hospitals, a high-stakes, emotionally charged environment particularly valuable for studying EI-related dynamics. We measured supervisor ability EI using the MSCEIT and assessed perceptions of supervisor EIB from both supervisors and subordinates. Multilevel polynomial regression was used to analyze perceptual alignment effects on subordinate engagement and burnout.ResultsControlling for supervisor ability EI, perceptual alignment on supervisor EIB predicted significantly higher subordinate engagement and lower burnout. Conversely, misalignment, particularly supervisor overestimation of their EIB, related to poorer subordinate outcomes.DiscussionThese findings highlight the relational and interpretive nature of EIB in management, demonstrating that how supervisors’ emotional intelligence is perceived, and whether those perceptions align, matters for subordinate well-being.
