潜在剖面分析在组织行为领域中的应用

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尹奎¹, 彭坚², 张君³(

)

¹北京科技大学东凌经济管理学院, 北京 100083
²广州大学管理学院, 广州 510006
³北京石油化工学院人文社科学院, 北京 102617

收稿日期:2018-12-10出版日期:2020-07-15发布日期:2020-05-21
通讯作者:张君E-mail:zhangj@outlook.com

基金资助:* 国家自然科学基金项目(71802019);国家自然科学基金项目(71902048);教育部人文社科基金项目(18YJC630230)

The application of latent profile analysis in organizational behavior research

YIN Kui¹, PENG Jian², ZHANG Jun³(

)

¹Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
²School of Management, Guangzhou University, Guangzhou 510006, China
³Department of Human Resource Management and Public Administration, Beijing Institute of Petrochemical Technology, Beijing 102617, China

Received:2018-12-10Online:2020-07-15Published:2020-05-21
Contact:ZHANG Jun E-mail:zhangj@outlook.com

摘要/Abstract

摘要： 以个体为中心的研究路径将各个变量看作是相互依赖的一个系统, 基于多项特征(变量)将被试分为多个子群体, 分析子群体的前因与影响。以个体为中心的研究路径理解更加直观、更贴近实践, 受到越来越多的关注。潜在剖面分析(latent profile analysis, LPA)是以个体为中心研究路径的典型分析技术。在总结归纳以个体与以变量为中心两种研究路径异同、LPA与传统以个体为中心的分析技术差异后, 系统梳理了LPA在组织行为学领域的应用主题, 并从研究主题选取、样本要求、理论使用、剖面数量确定等方面归纳了LPA应用的步骤与注意事项。最后, 提出了未来研究的方向。

图/表 5

表1以变量为中心与以个体为中心研究路径的区别

	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

表1以变量为中心与以个体为中心研究路径的区别

	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

表2LPA与传统以个体为中心分析技术的差异

	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。

表2LPA与传统以个体为中心分析技术的差异

	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。

表3LPA的应用主题与样本信息

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

表3LPA的应用主题与样本信息

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

表4剖面数量确定的依据

理论依据	统计指标依据
(1) 每个剖面(子群体)在分类指标上均有差异性, 内容差异比水平差异更重要。 (2) 每个剖面有足够的个体, 一般要求占总体5%以上。 (3) 以往的直接或相关研究, 证实存在某些特征的剖面。	(1) AIC、BIC、SSABIC、CAIC比竞争模型小。 (2) BLRT对应p在0.05水平上显著, 再多一个剖面变得不显著。 (3) 绘制剖面数量与△BIC、△ABIC等指标的折线图(elbow plots), 查找拐点。

表4剖面数量确定的依据

理论依据	统计指标依据
(1) 每个剖面(子群体)在分类指标上均有差异性, 内容差异比水平差异更重要。 (2) 每个剖面有足够的个体, 一般要求占总体5%以上。 (3) 以往的直接或相关研究, 证实存在某些特征的剖面。	(1) AIC、BIC、SSABIC、CAIC比竞争模型小。 (2) BLRT对应p在0.05水平上显著, 再多一个剖面变得不显著。 (3) 绘制剖面数量与△BIC、△ABIC等指标的折线图(elbow plots), 查找拐点。

图1潜在剖面数判断过程注: 作者整理

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