The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from the major perspectives on this topic (including applied, academic, and professional). Competency models are defined, and their key advantages are explained. Then, the many uses of competency models are described. The bulk of the article is a set of 20 best practices divided into 3 areas: analyzing competency information, organizing and presenting competency information, and using competency information. The best practices are described and explained, practice advice is provided, and then the best practices are illustrated with numerous practical examples. Finally, how competency modeling differs from and complements job analysis is explained throughout.The purpose of this article is to present a set of best practices for competency modeling based on the experiences and lessons learned from all the major perspectives on this topic including two major companies, a major consulting firm, a major university, and the Society for Industrial and Organizational Psychology (SIOP) taskforce on competency modeling. From all the different perspectives, we will delineate a set of 20 best practices and then illustrate them with practical examples from actual organizations. For the interested reader, we also link the practices to the existing literature which consists mostly of writings based on practical experience (e.g., case studies, commentaries) because little empiricalCorrespondence and requests for reprints should be addressed to Michael A. Campion,

The world is awash in data. Data is being created and stored at ever-increasing rates through a variety of new methods and technologies. Data is accumulating in all sorts of accessible places. Much of that data is of great interest to industrial–organizational (I-O) psychologists, often in ways never anticipated by those who develop technologies and processes that generate and store that data. I-O psychologists also generate data in the course of research and practice in ways that, especially if joined with data originating from other sources, create giant datasets. This abundance of data—variables, measurements, observations, facts—can be used to inform a vast number of issues in research and practice. This is the new “big data” world, and beyond opportunities, this new world also presents challenges and potential hazards.

Purpose The purpose of this paper is to address the barriers to the rapid development of effective HR analytics capabilities in organizations. Design/methodology/approach Literature and conceptual review of the current state of HR analytics. Findings “HR analytics” is used to refer to a too-wide array of measurement and analytical approaches, making strategic focus difficult. There is a misconception that doing more measurement of HR activities and human capital will necessarily lead to actionable insights. There is too much focus on incremental improvement of existing HR processes, detracting from diagnosing the problems with business performance. Too much time is spent on mining existing data, to the detriment of model building and testing, including collecting new more appropriate data. Too much energy is consumed with basic tasks of data management. Stakeholders avoid action by nitpicking the details of the data. Practical implications Practitioners who follow the guidance provided should find that their application of HR analytics leads to more relevant and actionable insights. Social implications More effective application of HR analytics should lead to better decision making in organizations and more effective resource allocation. Originality/value A new look at the field of HR analytics that synthesizes the research literature and current practice in organizations.

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The purpose of this article is to explore empirically issues and attitudes surrounding the assignment of credit for authorship in psychological research. A survey consisting of research tasks, vignettes, and questions relating to collaborations between faculty and students was completed by 203 individuals (23.3% response rate) from a national, random sample of faculty and graduate students. Analysis indicated that tasks related to manuscript writing, developing research ideas, and research design were important criteria for assigning credit of authorship. Status, seniority, and data collection were rated as unimportant to the assignment of credit. The issues surrounding collaborations between faculty and students were also examined.