Structural Bias in Stylized Items with Point-Valued Categories

Clicks: 5
ID: 333772
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
Steady

Ranked #3 of 3 articles by views in Journal of Survey Statistics and Methodology

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 Survey research frequently relies on stylized questions with discrete, ordered response categories to measure behaviors or attitudes. While these items simplify responses and facilitate standardization, they introduce a structural bias component (SBC) arising from the discrepancy between actual behavior and the assigned category values. This paper investigates SBC in the context of religious practice, using data from the European Values Study across multiple waves and countries. The analysis shows that the discrete nature of point-value items can generate distortions. A calibration model is proposed to estimate and adjust for this bias, assuming a uniform distribution of behaviors within each interval. Findings suggest that, while SBC can reach substantial levels in some domains—particularly where the accuracy of absolute values is crucial—in practice, however, its impact on theoretical interpretations or explanatory factors is typically limited. The study underscores the importance of carefully designing response categories and considering alternative scaling methods, such as stylized items with interval-valued categories, to reduce measurement error and enhance survey validity.
Reference Key
openalex_W7223459469 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal Journal of Survey Statistics and Methodology
Year 2026
DOI
10.1093/jssam/smag043
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.