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This study explores how researchers in the organizational sciences use and/or cite methodological ‘best practice’ (BP) articles. Namely, are scholars adhering fully to the prescribed practices they cite, or are they cherry picking from recommended practices without disclosing? Or worse yet, are scholars inaccurately following the methodological best practices they cite? To answer these questions, we selected three seminal and highly cited best practice articles published in Organizational Research Methods (ORM) within the past ten years. These articles offer clear and specific methodological recommendations for researchers as they make decisions regarding the design, measurement, and interpretation of empirical studies. We then gathered all articles that have cited these best practice pieces. Using comprehensive coding forms, we evaluated how authors are using and citing best practice articles (e.g., if they are appropriately following the recommended practices). Our results revealed substantial variation in how authors cited best practice articles, with 17.4% appropriately citing, 47.7% citing with minor inaccuracies, and 34.5% inappropriately citing BP articles. These findings shed light on the use (and misuse) of methodological recommendations, offering insight into how we can better improve our digestion and implementation of best practices as we design and test research and theory. Key implications and recommendations for editors, reviewers, and authors are discussed.

Big data and related technologies are radically altering our society. In a similar way, these approaches can transform the psychological sciences. The goal of this commentary is to motivate psychologists to embrace big data science for the betterment of the field. Big data sources, algorithmic methods, and a culture that embraces prediction has the potential to advance our science, improve the robustness and replicability of our research, and allow us to focus more centrally on actual behaviors. We highlight these key transformations, acknowledge criticisms of big data approaches, and emphasize specific ways psychologists can contribute to the big data science revolution.

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Advancements in computer science, specifically natural language processing (NLP), offer innovative opportunities to transform leadership assessment. This chapter first defines NLP and describes the many analytic techniques within NLP, including computer-aided text analysis, supervised machine learning, and unsupervised machine learning. For each technique, tangible examples of their use in leadership assessment research are provided. The chapter also advises scientists and practitioners interested in NLP on unique methodological considerations for its use in leadership assessment and provides resources that would be most beneficial. The chapter concludes by recommending future applications of NLP in assessing leaders, including suggestions of evaluative NLP models of leader behaviors that can offer real-time feedback to leaders.