ArticlePloS one2022
The effectiveness of weighted least squares means and variance adjusted based fit indices in assessing local dependence of the rasch model: Comparison with principal component analysis of residuals.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and initial validation of the health-related quality of life pictorial inventory for children (HEALTH-PIC).Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Validation of the Arabic Version of Asthma Numeracy Questionnaire (ANQ) Among Parents of Asthmatic Children: Factor and Rasch Analyses Study.Journal of asthma and allergy · 2025Article
- Proposed new definition for hospital-acquired SARS-CoV-2 infections: results of a confirmatory factor analysis.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
- Health literacy in adolescents and young adults in Benin: French translation and validation of the health literacy measure for adolescents (HELMA).Frontiers in psychology · 2024Article
- Evaluating the psychometric properties of the Chronic Time Pressure Inventory using Rasch analysis.PeerJ · 2023Article
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Abstract
Local independence is a principal assumption of applying latent variable models. Violations of this assumption might be stemmed from dimensionality (trait dependence) and statistical independence of item responses (response dependence). The purpose of this study is to evaluate the sensitivity of weighted least squares means and variance adjusted (WLSMV) based global fit indices to violations of local independence in Rasch models, and compare those indices to principal component analysis of residuals (PCAR) that is widely used for Rasch models. Dichotomous Rasch model is considered in this simulation study. The results show that WLSMV-based fit indices could detect trait dependence, but are to be limited with regard to response dependence. Additionally, WLSMV-based fit indices have advantages over the use of PCAR since WLSMV-based global fit indices are consistent regardless of sample size and test length. Though it is not recommended to apply exact benchmarks for those indices, they would provide practitioners with a method for evaluating the degree to which assumption violation is problematic for their data diagnostic purpose.
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