It's a Match, Isn't It? Developing a Checklist on Matching Secondary Data at the Example of Job Control
4 affiliations
- 1Institute of Management & Psychology, School of Management and Technology, Leuphana Universität Lüneburg Lüneburg Germany
- 2Department of Epidemiology and Biostatistics Michigan State University East Lansing Michigan USA
- 3Independent Scholar Cincinnati Ohio USA
- 4Division of Field Studies and Engineering (retired) National Institute for Occupational Safety and Health Cincinnati Ohio USA
Abstract
Few datasets simultaneously capture occupational exposures and health outcomes in a general working population. To understand associations between occupational exposures and health, researchers often combine existing datasets. As occupation codes are available in many datasets, combining these datasets seems a straight‐forward option. However, the different purposes of these datasets and correspondingly their coding systems may complicate and bias matching procedures by dropping occupations, squeezing multiple occupations into one category, and linking them incorrectly. To develop a checklist that supports an entire research and writing process based on matching datasets, we demonstrate problems that may occur when matching three datasets based on existing crosswalks. Two of these datasets provide unique occupational information: occupational exposure (specifically, job control) from the Occupational Information Network (O*NET) and sociodemographic composition of occupations from the American Community Survey (ACS). After matching them to each other, we used them to enrich workers' health data from the General Social Survey (GSS). This matching process showed that 993 detailed O*NET‐SOC codes from 2019 (O*NET‐19 codes) matched 420 U.S. Census occupation codes (Census codes). This reduction resulted from two situations: a lack of equivalent Census codes and a consolidation of several O*NET‐19 codes under a single Census code. Although we could match occupational data to all workers in GSS, full information on occupational exposure and sociodemographic composition of their jobs was not available for 35% of them. Since our findings are limited to O*NET‐based rating of job control, we encourage other researchers to explore scoring of other aspects of occupational exposure and provide a checklist for that purpose.
Related
- Beyond the Checklist: Developing Effective Communication in a South African Multilingual Clinical Training Programme.
- Co-developing the Child and Adolescent Mentally Healthy Classroom (CAMH Class) intervention for use in routine teaching in secondary schools
- Exploring Predictive Modeling Algorithms for Good Glycemic Control in Patients With Type 2 Diabetes Mellitus: A Secondary Analysis of a Cluster Randomized Trial Cohort Further Explored by Sex
- Promoting Positive Adolescent Mental Health (PPAMH!) for School Climate: protocol for a feasibility randomised control trial in secondary schools [version 2; peer review: 1 approved, 2 approved with reservations]
- Association between anticholinergic burden and geographic atrophy secondary to age-related macular degeneration: a case-control study.
BibTeX
@article{koßler2026match,
title = {{It's a Match, Isn't It? Developing a Checklist on Matching Secondary Data at the Example of Job Control}},
author = {Franziska J. Kößler and Candice Y. Johnson and Amel Omari and Kaori Fujishiro},
journal = {American Journal of Industrial Medicine},
year = {2026},
month = oct,
doi = {10.1002/ajim.70135},
url = {https://doi.org/10.1002/ajim.70135},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - It's a Match, Isn't It? Developing a Checklist on Matching Secondary Data at the Example of Job Control
AU - Franziska J. Kößler
AU - Candice Y. Johnson
AU - Amel Omari
AU - Kaori Fujishiro
PY - 2026
DA - 2026/10/03
JO - American Journal of Industrial Medicine
DO - 10.1002/ajim.70135
UR - https://doi.org/10.1002/ajim.70135
AB - Few datasets simultaneously capture occupational exposures and health outcomes in a general working population. To understand associations between occupational exposures and health, researchers often combine existing datasets. As occupation codes are available in many datasets, combining these datasets seems a straight‐forward option. However, the different purposes of these datasets and correspondingly their coding systems may complicate and bias matching procedures by dropping occupations, squeezing multiple occupations into one category, and linking them incorrectly. To develop a checklist that supports an entire research and writing process based on matching datasets, we demonstrate problems that may occur when matching three datasets based on existing crosswalks. Two of these datasets provide unique occupational information: occupational exposure (specifically, job control) from the Occupational Information Network (O*NET) and sociodemographic composition of occupations from the American Community Survey (ACS). After matching them to each other, we used them to enrich workers' health data from the General Social Survey (GSS). This matching process showed that 993 detailed O*NET‐SOC codes from 2019 (O*NET‐19 codes) matched 420 U.S. Census occupation codes (Census codes). This reduction resulted from two situations: a lack of equivalent Census codes and a consolidation of several O*NET‐19 codes under a single Census code. Although we could match occupational data to all workers in GSS, full information on occupational exposure and sociodemographic composition of their jobs was not available for 35% of them. Since our findings are limited to O*NET‐based rating of job control, we encourage other researchers to explore scoring of other aspects of occupational exposure and provide a checklist for that purpose.
SN - 0271-3586
SN - 1097-0274
ER -