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- W4313509105 abstract "A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study presents a series of Monte Carlo computer simulations that investigates Bayesian and multiple imputation strategies based on factored regressions. When the model’s distributional assumptions are satisfied, these methods generally produce nearly unbiased estimates and good coverage, with few exceptions. Severe misspecifications that arise from substantially non-normal distributions can introduce biased estimates and poor coverage. Follow-up simulations suggest that a Yeo–Johnson transformation can mitigate these biases. A real data example illustrates the methodology, and the paper suggests several avenues for future research." @default.
- W4313509105 created "2023-01-06" @default.
- W4313509105 creator A5024721220 @default.
- W4313509105 creator A5044951394 @default.
- W4313509105 date "2023-01-05" @default.
- W4313509105 modified "2023-10-03" @default.
- W4313509105 title "An Investigation of Factored Regression Missing Data Methods for Multilevel Models with Cross-Level Interactions" @default.
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- W4313509105 doi "https://doi.org/10.1080/00273171.2022.2147049" @default.
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