Improving Anchoring Vignette Methodology in Health Surveys with Image Vignettes Article Swipe
Mengyao Hu
,
Sung-Hee Lee
,
Hongwei Xu
,
Roberto Melipillán
,
Jacqui Smith
,
Arie Kapteyn
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.12758/mda.2022.02
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.12758/mda.2022.02
The anchoring vignette method is designed to improve comparisons across population groups and adjust for differential item functioning (DIF). Vignette questions are brief descriptions of hypothetical persons for respondents to rate. Although this method has been adopted widely in health surveys, there remain challenges. In particular, vignettes are complex, increasing survey time and respondent burden. Further, the assumptions underlying this method are often violated. To overcome such challenges, this paper introduces an innovative technique, namely image anchoring vignettes, conveying vignette information with varying health levels in images. We conducted a cross-cultural experimental study to examine the performance of image and standard text vignettes in terms of response time, how well they satisfy the assumptions, and their DIF-adjusting quality using a confirmatory factor analysis. The study revealed that respondents can better differentiate the intensity levels of the three vignettes in the image vignette condition, compared to text vignettes. Response consistency assumption appears to be better satisfied for image vignettes than text vignettes. Using well-designed image vignettes greatly reduces survey time without losing the DIF-adjustment quality, indicating the potential of image vignettes to improve overall efficiencies of the anchoring vignette method. Improving vignette equivalence (i.e., minimizing different interpretations of vignettes by different groups), remains a challenge for both text and image vignettes. This study generates new insights into the design and use of image anchoring vignettes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/40520957
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411358301Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.12758/mda.2022.02Digital Object Identifier
- Title
-
Improving Anchoring Vignette Methodology in Health Surveys with Image VignettesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-05Full publication date if available
- Authors
-
Mengyao Hu, Sung-Hee Lee, Hongwei Xu, Roberto Melipillán, Jacqui Smith, Arie KapteynList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/40520957Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/12165436Direct OA link when available
- Concepts
-
Vignette, Anchoring, Psychology, Image (mathematics), Computer science, Social psychology, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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