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使用因子混合模型测试工作类型和识别高危亚人群。

Testing job typologies and identifying at-risk subpopulations using factor mixture models.

机构信息

Department of Psychology, Michigan State University.

Department of Psychology, University of Bern.

出版信息

J Occup Health Psychol. 2017 Oct;22(4):503-517. doi: 10.1037/ocp0000038. Epub 2016 Apr 25.

DOI:10.1037/ocp0000038
PMID:27111428
Abstract

Research in occupational health psychology has tended to focus on the effects of single job characteristics or various job characteristics combined into 1 factor. However, such a variable-centered approach does not account for the clustering of job attributes among groups of employees. We addressed this issue by using a person-centered approach to (a) investigate the occurrence of different empirical constellations of perceived job stressors and resources and (b) validate the meaningfulness of profiles by analyzing their association with employee well-being and performance. We applied factor mixture modeling to identify profiles in 4 large samples consisting of employees in Switzerland (Studies 1 and 2) and the United States (Studies 3 and 4). We identified 2 profiles that spanned the 4 samples, with 1 reflecting a combination of relatively low stressors and high resources (P1) and the other relatively high stressors and low resources (P3). The profiles differed mainly in terms of their organizational and social aspects. Employees in P1 reported significantly higher mean levels of job satisfaction, performance, and general health, and lower means in exhaustion compared with P3. Additional analyses showed differential relationships between job attributes and outcomes depending on profile membership. These findings may benefit organizational interventions as they show that perceived work stressors and resources more strongly influence satisfaction and well-being in particular profiles. (PsycINFO Database Record

摘要

职业健康心理学的研究往往侧重于单一工作特征或各种工作特征结合成一个因素的影响。然而,这种以变量为中心的方法并没有考虑到员工群体中工作属性的聚类。我们通过使用以个体为中心的方法来解决这个问题:(a) 调查感知工作压力源和资源的不同经验组合的发生情况;(b) 通过分析它们与员工幸福感和绩效的关系来验证配置文件的意义。我们应用因子混合建模在包括瑞士(研究 1 和 2)和美国(研究 3 和 4)的员工的 4 个大样本中识别出配置文件。我们在 4 个样本中识别出 2 个横跨的配置文件,其中 1 个反映了相对较低的压力源和较高的资源的组合(P1),另一个则反映了相对较高的压力源和较低的资源(P3)。这些配置文件主要在组织和社会方面存在差异。与 P3 相比,P1 的员工报告的工作满意度、绩效和一般健康水平显著更高,而精疲力竭的程度则更低。进一步的分析表明,工作属性和结果之间的关系因配置文件成员而异。这些发现可能有益于组织干预,因为它们表明感知工作压力源和资源对特定配置文件的满意度和幸福感的影响更大。

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