ArticlePloS one2025
The impact of missing data rates and imputation methods on the assumption of unidimensionality.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Patient-Initiated Permanent Deletion of Their Electronic Health Record Data: implications for Artificial Intelligence and Big Data in Healthcare : Clyde T. Matava, MBChB, MMed, MHSc.Journal of medical systems · 2026Article
- Development and validation of a sepsis-specific systemic inflammation classification system for mortality prediction.Frontiers in medicine · 2026Article
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1 author.
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Abstract
Statistical models are essential tools in data analysis. However, missing data plays a pivotal role in impacting the assumptions and effectiveness of statistical models, especially when there is a significant amount of missing data. This study addresses one of the core assumptions supporting many statistical models, the assumption of unidimensionality. It examines the impact of missing data rates and imputation methods on fulfilling this assumption. The study employs three imputation methods: Corrected Item Mean, multiple imputation, and expectation maximization, assessing their performance across nineteen levels of missing data rates, and examining their impact on the assumption of unidimensionality using several indicators (Cronbach's alpha, corrected correlation coefficients, factor analysis (Eigenvalues ([Formula: see text], [Formula: see text], and [Formula: see text] cumulative variance, and communalities). The study concluded that all imputation methods used effectively provided data that maintained the unidimensionality assumption, regardless of missing data rates. Additionally, it was found that most of the unidimensionality indicators increased in value as missing data rates rose.
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