ERIC Number: EJ1390866
Record Type: Journal
Publication Date: 2023
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1536-6367
EISSN: EISSN-1536-6359
Available Date: N/A
A Tree-Based Approach to Identifying Response Styles with Anchoring Vignettes
Leventhal, Brian C.; Zigler, Christina K.
Measurement: Interdisciplinary Research and Perspectives, v21 n2 p82-100 2023
Survey score interpretations are often plagued by sources of construct-irrelevant variation, such as response styles. In this study, we propose the use of an IRTree Model to account for response styles by making use of self-report items and anchoring vignettes. Specifically, we investigate how the IRTree approach with anchoring vignettes compares to traditional approaches that either do not include anchoring vignettes or do not account for response styles. We analyze secondary data using four different models: 1) total score; 2) graded response model; 3) IRTree without the consideration of anchoring vignettes, and 4) IRTree considering anchoring vignettes. We found significant differences in trait estimates from models that account for response styles compared to those that do not. Additionally, we found differences in trait estimates between the IRTree Models when considering anchoring vignettes and when not. Model comparisons suggest that trait differences are due to adjusting for acquiescence response style.
Descriptors: Scores, Vignettes, Response Style (Tests), Item Response Theory, Models, Comparative Analysis, Likert Scales, Surveys, Item Analysis, Measurement Techniques, Personality Traits, Goodness of Fit
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A