Data

Cross-jurisdictional comparisons using workers’ compensation: illustrating collider bias due to compensability thresholds - data and code

Monash University
Tyler Lane (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26180/13140374.v3&rft.title=Cross-jurisdictional comparisons using workers’ compensation: illustrating collider bias due to compensability thresholds - data and code&rft.identifier=https://doi.org/10.26180/13140374.v3&rft.publisher=Monash University&rft.description=These data and R files are used to demonstrate the threat of collider bias, which has been largely ignored in work disability research. Data include NBA player data and aggregated workers' compensation data, plus several simulated datasets.Case-level workers' compensation data are sensitive and cannot be shared, though I have included cleaning data to document exactly how they were aggregated.&rft.creator=Tyler Lane&rft.date=2022&rft_rights=CC-BY-4.0&rft_subject=collider bias&rft_subject=r statistical language&rft_subject=workers' compensation&rft_subject=cross-jurisdictional comparisons&rft_subject=Simpson's paradox&rft_subject=Public Health and Health Services not elsewhere classified&rft_subject=Sociological Methodology and Research Methods&rft.type=dataset&rft.language=English Access the data
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These data and R files are used to demonstrate the threat of collider bias, which has been largely ignored in work disability research. Data include NBA player data and aggregated workers' compensation data, plus several simulated datasets.

Case-level workers' compensation data are sensitive and cannot be shared, though I have included cleaning data to document exactly how they were aggregated.

Issued: 2021-03-25

Created: 2022-04-04

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Subjects
Public Health and Health Services not elsewhere classified | Simpson's paradox | Sociological Methodology and Research Methods | collider bias | cross-jurisdictional comparisons | r statistical language | workers' compensation |

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Identifiers
ACN 633 798 857