Related Experiment Video
Updated: Mar 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Testing job typologies and identifying at-risk subpopulations using factor mixture models
Anita C Keller1, Ivana Igic2, Laurenz L Meier3
1Department of Psychology, Michigan State University.
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
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Clearance Models: Noncompartmental Models
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Mechanistic Models: Overview of Compartment Models
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Stereotype Content Model
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...

