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This article aims to highlight the advancements in multiple imputation techniques to address issues caused by missing values in multivariate datasets.

October 1, 1998Multivariate Behavioral Research

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

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This article aims to highlight the advancements in multiple imputation techniques to address issues caused by missing values in multivariate datasets.
Examines multiple imputation as a solution to missing data issues.
Discusses current software available for implementing multiple imputation procedures.
Demonstrates application using data from the Adolescent Alcohol Prevention Trial.
Multiple imputation produces meaningful estimates and incorporates missing-data uncertainty.
Recent computational algorithms enhance the flexibility of multiple imputation in complex scenarios.
Software tools significantly streamline the process of conducting multivariate analyses with missing values.

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Implication

Review discusses multiple imputation techniques for handling missing data in multivariate analysis, suggesting new computational methods and software tools.

Cite This Study

Schafer et al. (1998) studied this question.

synapsesocial.com/papers/69d96a3b8988aeabbe685472 https://doi.org/10.1207/s15327906mbr3304_5

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Implication

Review discusses multiple imputation techniques for handling missing data in multivariate analysis, suggesting new computational methods and software tools.

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

Key Points

Discussion

Implication

Cite This Study

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

Key Points

Discussion

Implication

Cite This Study

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