Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.

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Publication Details

Journal
Psychological Methods
Published
2024-09-30
DOI
https://doi.org/10.1037/met0000694
Citations
3
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
2.13
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article

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.

Simon Grund, Oliver Lüdtke, Alexander Robitzsch
3 citations
Psychological Methods
Statistical Methods and Bayesian Inference
2.13
article

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.

Simon Grund, Oliver Lüdtke, Alexander Robitzsch
article en
3 citations

Abstract

No abstract available for this paper.
Psychological Methods
Universität Hamburg (DE), Leibniz Institute for Science and Mathematics Education (DE)
Openalex Percentile: Top 12%
Statistical Methods and Bayesian Inference
2.13
DOISourceOpenAlex
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Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares. — Simon Grund, Oliver Lüdtke, et al. · Psychological Methods (2024) | TGRS Research Map | TGRS