This article provides a review and synthesis of person-centered analytic (i.e., clustering) methods in organizational psychology with the aim of (a) placing them into an organizing framework to facilitate analysis and interpretation and (b) constructing a set of practical recommendations to guide future person-centered research. To do so, we first clarify the terminological and conceptual issues that still cloud person-centered approaches. Next, we organize the diverse kinds of person-centered analyses into two major statistical approaches, algorithmic and latent-variable approaches. We then present a literature review that quantifies how these two approaches have been used within our field, identifying trends over time and typical study characteristics. Out of this review, we construct a unifying taxonomy of the five ways in which clusters are differentiated: (1) construct-based patterns, (2) response-style patterns, (3) predictive relations, (4) growth trajectories, and (5) measurement models. We also provide a set of practical guidelines for researchers and highlight a few remaining questions and/or areas in which future work is needed for further advancing person-centered methodologies. Keywords profile analysis, cluster analysis, latent class analysis, latent class growth models The "person-centered" approach has become an increasingly used concept within the organizational sciences. Recent publications (e.g., Morin, Meyer, Creusier, & Biétry, 2016) and a 2011 special issue of Organizational Research Methods (Wang & Hanges, 2011) have either used or explicated person-centered analytic frameworks. However, this term has also been used to simultaneously