构造(python库)
计算机科学
集合(抽象数据类型)
数据科学
领域(数学)
潜变量
分类学(生物学)
聚类分析
口译(哲学)
管理科学
心理学
人工智能
经济
程序设计语言
纯数学
生物
植物
数学
作者
Sang Eun Woo,Andrew T. Jebb,Louis Tay,Scott Parrigon
标识
DOI:10.1177/1094428117752467
摘要
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.
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