ArticleEducational and psychological measurement2021
Exploring the Impact of Missing Data on Residual-Based Dimensionality Analysis for Measurement Models.
Article in Educational and psychological measurement, 2021. 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.
- Cross cultural adaptation of the TENDINopathy severity assessment - Achilles (TENDINS-A) for Spanish speaking populations.Musculoskeletal science & practice · 2026Article
- Mental Rotation Performance: Contribution of Item Features to Difficulties and Functional Adaptation.Journal of Intelligence · 2024Article
- Item-Level Analysis of a Newly Developed Interactive Nutrition Specific Physical Exam Competency Tool (INSPECT) Using the Rasch Measurement Model.Healthcare (Basel, Switzerland) · 2022Article
- Psychometric Evaluation of the Intraprofessional Collaborative Practice Survey in Entry-Level Occupational Therapy Students: A Rasch Analysis.The American journal of occupational therapy : official publication of the American Occupational Therapy AssociationArticle
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2 authors.
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Abstract
Researchers frequently use Rasch models to analyze survey responses because these models provide accurate parameter estimates for items and examinees when there are missing data. However, researchers have not fully considered how missing data affect the accuracy of dimensionality assessment in Rasch analyses such as principal components analysis (PCA) of standardized residuals. Because adherence to unidimensionality is a prerequisite for the appropriate interpretation and use of Rasch model results, insight into the impact of missing data on the accuracy of this approach is critical. We used a simulation study to examine the accuracy of standardized residual PCA with various proportions of missing data and multidimensionality. We also explored an adaptation of modified parallel analysis in combination with standardized residual PCA as a source of additional information about dimensionality when missing data are present. Our results suggested that missing data impact the accuracy of PCA on standardized residuals, and that the adaptation of modified parallel analysis provides useful supplementary information about dimensionality when there are missing data.
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