Journal ArticleDOI
The use of multiple imputation for the analysis of missing data.
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TLDR
The idea behind MI, the advantages of MI over existing techniques for addressing missing data, how to do MI for real problems, the software available to implement MI, and the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI are discussed.Abstract:
This article provides a comprehensive review of multiple imputation (MI), a technique for analyzing data sets with missing values. Formally, MI is the process of replacing each missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI.read more
Citations
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A Fully Conditional Specification Approach to Multilevel Imputation of Categorical and Continuous Variables.
TL;DR: The purpose of this manuscript is to describe a flexible imputation approach that can accommodate a diverse set of 2-level analysis problems that includes any of the aforementioned features.
Journal ArticleDOI
Missing in action: A case study of the application of methods for dealing with missing data to trauma system benchmarking
TL;DR: Missing data methods can influence the assessment of trauma care performance and need to be reported in all analyses and it is important that validated standardized approaches to dealing with missing data are universally adopted and reported.
Journal ArticleDOI
The Performance of Multiple Imputation for Likert-type Items with Missing Data
TL;DR: In this paper, the performance of multiple imputation (MI) for missing data in Likert-type items assuming multivariate normality was assessed using simulation methods, and MI was robust to violations of continuity and normality.
Journal ArticleDOI
Vocational interests of intellectually gifted and highly achieving young adults.
TL;DR: At the time around graduation from high school, gifted young adults show stable interest profiles, which strongly differ between gender and intelligence groups, which are relevant for programmes for the gifted and for vocational counselling.
Journal ArticleDOI
Counseling services for Asian, Latino/a, and White American students: Initial severity, session attendance, and outcome.
TL;DR: Findings highlight the need to devote greater attention to the counseling experiences of racial/ethnic minority clients, especially certain Asian American groups.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
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Analysis of Incomplete Multivariate Data
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