James M. LeBreton is a Professor of Psychology and Social Data Analytics at Pennsylvania State University. Over the last 20 years he has been involved with developing, testing, and revising the Conditional Reasoning Theory of Personality. This theory is anchored to the concept of motivated reasoning. Specifically, individuals with strong personality motives (e.g., motive to aggress) develop cognitive biases (e.g., hostile attribution bias) that they use to help rationalize the pursuit of behaviors satisfying the underlying motives (e.g., harming others). As part of this research program, James has helped develop and validate measures assessing the motive to aggress, the motive to achieve, and the motive for power. Using these new measures, he has tested hypotheses linking personality to an array of organizational outcomes including: counterproductive work behavior, leadership, team processes & performance, and job attitudes. In addition, James’ methodological research program has examined issues related to variable importance (e.g., relative weights analysis), multilevel research (e.g., data aggregation, dyadic analysis), and general topics in measurement (e.g., test development & validation, measurement invariance, test bias & fairness). James is a former associate editor (2010-2013) and editor-in-chief (2014-2018) of Organizational Research Methods; and, he also co-edited the APA Handbook of Multilevel Theory, Measurement, and Analysis (2019). James is a fellow of the American Psychological Association, the Association for Psychological Science, the Society for Industrial and Organizational Psychology, and the Consortium for the Advancement of Research Methods and Analyses. Recently, James was awarded the 2023 Distinguished Career Contributions Award by the Research Methods Division of the Academy of Management.
The concept of multilevel analysis is deeply ingrained in the organizational sciences. Researchers acknowledge that data often exhibit a nested structure (e.g., individuals nested within teams within organizations). Rooted in the multilevel paradigm is the idea that relationships between variables frequently span different levels. In order to measure variables across levels, multilevel scholars often rely on the aggregation of lower-level data (e.g., individual-level perceptions of justice) to serve as measures for higher-level constructs (e.g., team-level justice climate). Despite the prevalence of data aggregation in multilevel research, a brief literature review revealed that there is substantial variability how data aggregation decisions are described and what information is reported by authors. This variability in reporting practices may impede the transparency and reproducibility of research. The purpose of this presentation is to offer a set of reporting recommendations for multilevel scholars designed to improve the transparency and reproducibility of data aggregation in multilevel research.