The application of latent profile analysis in organizational behavior research

doi:10.3724/SP.J.1042.2020.01056

Abstract

Abstract:

A person-centered approach views divergent variables as an interdependent system, and divides the sample into subgroups according to certain participant characteristics to analyze the antecedents and outcomes. This approach has earned much attention because it more closely approximates practice and easier to understand. Latent profile analysis (LPA) is one analytical tool typically used for such analysis. After comparing and contrasting person-centered and variable-centered approaches, and then LPA and similar analysis techniques, we systematically illuminate the domains of the field of organizational behavior where LPA can be applied. We then provide guidance for using LPA as a research method with regard to research theme, sample requirements, the use of theory, and the determination of profile number. Finally, we offer suggestions for the future development of LAP.

Key words: person-centered, latent profile analysis, heterogeneity

CLC Number:

B849: C93

YIN Kui, PENG Jian, ZHANG Jun. The application of latent profile analysis in organizational behavior research[J]. Advances in Psychological Science, 2020, 28(7): 1056-1070.

Figures/Tables 5

References 93

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	以变量为中心的路径	以个体为中心的路径
研究目的	描述变量间关系用某些变量解释特定变量的方差	识别在变量系统中共有相似关系/水平模式的子群体
前提假设	样本和总体同质, 即变量间关系可以推广到总体中	样本或总体中包含不同子群体根据变量间的组合方式区分子群体
优势	能够清晰识别方差的解释能够得出可以推广到总体的结论	能够分析多个变量之间的复杂组合模式识别子群体, 并将群体类型作为变量
特定主题的应用	前因、结果、相关叠加和交互作用	在不同群体中检验变量间关系; 探索或者检验未知群体
常用分析方法	描述性统计、相关、多元回归、SEM、跨层次分析、元分析	均值分割、聚类分析、LPA、潜剖面增长模型、潜在剖面/类别转移分析
管理实践意义	因素单一、可操作性较差	综合考虑、容易理解、直觉上更有吸引力

	以变量为中心的路径	以个体为中心的路径
研究目的	描述变量间关系用某些变量解释特定变量的方差	识别在变量系统中共有相似关系/水平模式的子群体
前提假设	样本和总体同质, 即变量间关系可以推广到总体中	样本或总体中包含不同子群体根据变量间的组合方式区分子群体
优势	能够清晰识别方差的解释能够得出可以推广到总体的结论	能够分析多个变量之间的复杂组合模式识别子群体, 并将群体类型作为变量
特定主题的应用	前因、结果、相关叠加和交互作用	在不同群体中检验变量间关系; 探索或者检验未知群体
常用分析方法	描述性统计、相关、多元回归、SEM、跨层次分析、元分析	均值分割、聚类分析、LPA、潜剖面增长模型、潜在剖面/类别转移分析
管理实践意义	因素单一、可操作性较差	综合考虑、容易理解、直觉上更有吸引力

	均值分割	聚类分析	LPA
优势	简单; 事先确定分组, 有利于指导假设提出。	相比于均值分割较为灵活; 对于总体样本, 基于客观指标的分类效率高。	采用更加严格稳健的统计标准来确定分类数量, 更加客观; 适用于分析不同类型量表的数据, 不需要进行数据转换; 基于概率将个体分布在各个子群体上。
劣势	(1)不同样本均值不同, 难以进行跨样本比较; (2)简单划分高低组过于简单化了个体差异, 难以满足群体内同质性; (3)强制划分群体可能不符合实际; (4)可能遗漏潜在的子群体。	(1)通过最小化组内差异、最大化组间差异确定分组数量; (2)模型选择、组数量确定有较强的主观性; (3)假设变量间彼此独立、分类变量服从多项分布、连续变量服从正态分布。	(1)对样本量敏感, 大样本可能会提取更多类别; (2)在非线性情况下, 可能会存在过量提取数量; (3)可能出现各个拟合指标冲突, 难以确定最终剖面数量。

	均值分割	聚类分析	LPA
优势	简单; 事先确定分组, 有利于指导假设提出。	相比于均值分割较为灵活; 对于总体样本, 基于客观指标的分类效率高。	采用更加严格稳健的统计标准来确定分类数量, 更加客观; 适用于分析不同类型量表的数据, 不需要进行数据转换; 基于概率将个体分布在各个子群体上。
劣势	(1)不同样本均值不同, 难以进行跨样本比较; (2)简单划分高低组过于简单化了个体差异, 难以满足群体内同质性; (3)强制划分群体可能不符合实际; (4)可能遗漏潜在的子群体。	(1)通过最小化组内差异、最大化组间差异确定分组数量; (2)模型选择、组数量确定有较强的主观性; (3)假设变量间彼此独立、分类变量服从多项分布、连续变量服从正态分布。	(1)对样本量敏感, 大样本可能会提取更多类别; (2)在非线性情况下, 可能会存在过量提取数量; (3)可能出现各个拟合指标冲突, 难以确定最终剖面数量。

作者(年份)	研究主题	结果	样本来源	样本量
O’Neill等(2014)	人格	上网磨洋工、工作投入	美国	N = 148
Conte等(2017)	人格	留职率、损耗	美国	N = 4763
Isler等(2017)	人格	主观幸福感、自我提升价值观、改变开放性等	新西兰	N = 6518
Fisher和Robie, (2019)	人格	生活满意度、工作自信、工作热情等	全球	N = 3137694
Moran等(2012)	动机	需要满意度、角色内绩效	中国	N = 226
Valero和Hirschi (2016)	动机	人岗匹配、工作投入、工作满意度	德国	N₁ = 577; N₂ = 949
Chambel等(2016)	心理契约	工作投入	葡萄牙	N₁ = 1821; N₂ = 1046
Bouckenooghe等(2019)	心理资本	工作投入、工作绩效等	澳大利亚	N₁ = 171, N₂ = 190
巢琳等(2017)	心理资本	组织公民行为、心理抑郁	中国	N = 283
Bouckenooghe等(2019)	心理资本	工作投入、工作绩效	巴基斯坦、乌克兰	N₁ = 171, N₂ = 190
Moazami-Goodarzi等(2019)	工作-家庭冲突	工作控制、离职意愿	芬兰	N₁₂₃ = 789
M?kikangas等(2015)	幸福感	—	芬兰	N = 402, 连续三年
Benitez等(2019)	幸福感	—	西班牙	N = 396
Bujacz等(2019)	幸福感	—	欧洲	N = 3461
薛晓州等(2017)	指导关系	—	中国	N = 381
Wasti (2005)	组织承诺	工作退缩行为、离职意愿、组织公民行为、组织认同工作压力	土耳其	N₁ = 914, N₂ = 336
Meyer等(2013)	组织承诺	心理幸福感、留职意愿、沮丧、焦虑、工作搜寻活动	加拿大	N = 6501
Meyer等(2018)	组织承诺	工作压力、工作满意度等	土耳其	N₁ = 346, N₂ = 797
Morin等(2015)	组织承诺	离职意愿、幸福感	香港	N = 1096
Gabriel et al. (2015)	情绪劳动	情绪耗竭、工作满意度等	美国和新加坡	N₁ = 692, N₂ = 480
Diefendorff等(2019)	情绪劳动事件	幸福感	中国	N = 246
Fouquereau等(2019)	情绪劳动	工作满意度、工作绩效等	法国	N₁ = 311, N₂ = 311
Gabriel et al. (2019)	恢复体验	工作投入、工作倦怠等	美国	N₁ = 520, N₂ = 536
Lee (2018)	工作与家庭冲突	工作满意度、组织公民行为等	——	N = 823
O’Neill等(2018)	团队冲突	团队效能、合作型冲突管理	加拿	N₁ = 195; N₂ = 92
Stanley等(2017)	企业分类	企业绩效	德国	N = 314
Kollitz等(2019)	企业分类	人-岗匹配、人-组织匹配、组织绩效	德国	N = 259