It's a Match, Isn't It? Developing a Checklist on Matching Secondary Data at the Example of Job Control

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.