Psychometric Properties of the Short Version of the Successful Ageing at Work Scale in the Chinese Context

Clicks: 1
ID: 314131
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #1 of 5 articles by views in Work Aging and Retirement

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract China faces dual challenges of population ageing and labor force restructuring, necessitating a scientific assessment of successful ageing at work to optimize the management of older employees. This study localized and validated the Short Form of the Successful Ageing at Work Scale (SAW-S) using Classical Test Theory (CTT), Item Response Theory (IRT), and Network Analysis. Data from 865 employees were collected. Results demonstrated SAW-S's strong reliability/validity. Confirmatory factor analysis supported a two-factor structure (Experience and Strategies). IRT confirmed item discriminability but identified disordered response thresholds for some items. Network analysis revealed "Work Flexibility" (S1) as the most central node, bridging individual adaptability and organizational strategies. The study verifies SAW-S's applicability in China, offering organizations an evidence-based tool to design ageing workforce policies.
Reference Key
openalex_W7161690951 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hongxing Meng, Lidan Liu, Hanying Tang, Hongyu Ma
Journal Work Aging and Retirement
Year 2026
DOI
10.1093/workar/waag005
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.