ArticleEducational and psychological measurement2022
Measuring Response Style Stability Across Constructs With Item Response Trees.
Article in Educational and psychological measurement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond patient education: fall prevention knowledge, health literacy, and implementation gaps in Chinese hospitals-a patient-caregiver study.Frontiers in public health · 2026Article
- Modeling the response style in continuous bounded responses: Model development and validation.Behavior research methods · 2025Article
- Posterior predictive checks for the detection of extreme response style.Behavior research methods · 2025Article
- A Monte Carlo study of IRTree models' ability to recover item parameters.Frontiers in psychology · 2023Article
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Authors and funding
1 author.
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Abstract
Individual response style behaviors, unrelated to the latent trait of interest, may influence responses to ordinal survey items. Response style can introduce bias in the total score with respect to the trait of interest, threatening valid interpretation of scores. Despite claims of response style stability across scales, there has been little research into stability across multiple scales from the beneficial perspective of item response trees. This study examines an extension of the IRTree methodology to include mixed item formats, providing an empirical example of responses to three scales measuring perceptions of social media, climate change, and medical marijuana use. Results show extreme and midpoint response styles were not stable across scales within a single administration and 5-point Likert-type items elicited higher levels of extreme response style than the 4-point items. Latent trait of interest estimation varied, particularly at the lower end of the score distribution, across response style models, demonstrating as appropriate response style model is important for adequate trait estimation using Bayesian Markov chain Monte Carlo estimation.
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