Multilevel Latent Profile Analyses: A Comprehensive Guide

IF 8.7 2区 管理学 Q1 MANAGEMENT
Simon A. Houle,Alexandre J.S. Morin,Jean-François Harvey
{"title":"Multilevel Latent Profile Analyses: A Comprehensive Guide","authors":"Simon A. Houle,Alexandre J.S. Morin,Jean-François Harvey","doi":"10.1177/10944281261469432","DOIUrl":null,"url":null,"abstract":"The organizational sciences have recently seen an uptake in the use of both person-centered analyses, most typically implemented via latent profile analyses (LPA), and multilevel analyses. Yet their combination remains conceptually underdeveloped and forms the focus of the current article. On the one hand, LPA is perfectly suited to the identification of subpopulations, or profiles, of participants characterized by distinct configurations on a set of variables. On the other hand, multilevel models are aptly suited to uncover, or control for, phenomena unfolding across individual, group, and organizational levels. In combination, multilevel-LPA provides the best of both worlds, enabling researchers to identify subpopulations of individuals and/or groups corresponding to distinct prototypical states, while modeling within-group and between-group variability. In the current article, we provide a comprehensive guide for multilevel-LPA implementation to monitor group contexts, climates, and cultures. We present six types of multilevel-LPA specifications—additive, dispersion, additive-dispersion, dispersion-heterogeneity, restrictive cross-level, and full cross-level—that ought to be carefully selected based on the nature of the constructs being measured and the research questions being asked. In doing so, we aim provide researchers with the analytical and conceptual tools to select and apply the appropriate multilevel person-centered model to achieve their research objectives.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"68 1","pages":""},"PeriodicalIF":8.7000,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Organizational Research Methods","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10944281261469432","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

Abstract

The organizational sciences have recently seen an uptake in the use of both person-centered analyses, most typically implemented via latent profile analyses (LPA), and multilevel analyses. Yet their combination remains conceptually underdeveloped and forms the focus of the current article. On the one hand, LPA is perfectly suited to the identification of subpopulations, or profiles, of participants characterized by distinct configurations on a set of variables. On the other hand, multilevel models are aptly suited to uncover, or control for, phenomena unfolding across individual, group, and organizational levels. In combination, multilevel-LPA provides the best of both worlds, enabling researchers to identify subpopulations of individuals and/or groups corresponding to distinct prototypical states, while modeling within-group and between-group variability. In the current article, we provide a comprehensive guide for multilevel-LPA implementation to monitor group contexts, climates, and cultures. We present six types of multilevel-LPA specifications—additive, dispersion, additive-dispersion, dispersion-heterogeneity, restrictive cross-level, and full cross-level—that ought to be carefully selected based on the nature of the constructs being measured and the research questions being asked. In doing so, we aim provide researchers with the analytical and conceptual tools to select and apply the appropriate multilevel person-centered model to achieve their research objectives.
多层次潜在剖面分析:综合指南
组织科学最近看到了以人为中心的分析(最典型的是通过潜在剖面分析(LPA)实现)和多层次分析的使用。然而,它们的结合在概念上仍然不发达,是本文的重点。一方面,LPA非常适合于识别在一组变量上具有不同配置特征的参与者的亚种群或概况。另一方面,多层模型适合于揭示或控制跨个人、组和组织级别展开的现象。结合起来,多层lpa提供了两全其美的效果,使研究人员能够识别个体和/或群体对应于不同原型状态的亚种群,同时建模组内和组间的可变性。在当前的文章中,我们为多层lpa实现提供了一个全面的指南,以监测群体背景、气候和文化。我们提出了六种类型的多水平lpa规格——加性、分散、加性-分散、分散-异质性、限制性交叉水平和完全交叉水平——应该根据所测量结构的性质和所提出的研究问题仔细选择。在此过程中,我们的目标是为研究人员提供分析和概念工具,以选择和应用适当的多层次以人为中心的模型来实现他们的研究目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书