职业教育
心理学
范畴变量
描述性统计
领域(数学)
人口
集合(抽象数据类型)
潜变量
应用心理学
社会心理学
统计
计算机科学
社会学
教育学
人工智能
数学
人口学
程序设计语言
纯数学
作者
Daniel Spurk,Andreas Hirschi,Mo Wang,Domingo Campillo Valero,Simone Kauffeld
标识
DOI:10.1016/j.jvb.2020.103445
摘要
Latent profile analysis (LPA) is a categorical latent variable approach that focuses on identifying latent subpopulations within a population based on a certain set of variables. LPA thus assumes that people can be typed with varying degrees of probabilities into categories that have different configural profiles of personal and/or environmental attributes. Within this article, we (a) review the existing applications of LPA within past vocational behavior research; (b) illustrate best practice procedures in a non-technical way of how to use LPA methodology, with an illustrative example of identifying different latent profiles of heavy work investment (i.e., working compulsively, working excessively, and work engagement); and (c) outline future research possibilities in vocational behavior research. By reviewing 46 studies stemming from central journals of the field, we identified seven distinct topics that have already been investigated by LPA (e.g., job and organizational attitudes and behaviors, work motivation, career-related attitudes and orientations, vocational interests). Together with showing descriptive statistics about how LPA has been conducted in past vocational behavior research, we illustrate and derive best-practice recommendations for future LPA research. The review and "how to" guide can be helpful for all researchers interested in conducting LPA studies.
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