Journal ArticleDOI
A Note on the Use of Missing Auxiliary Variables in Full Information Maximum Likelihood-Based Structural Equation Models
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This article showed that the inclusion of an auxiliary variable is beneficial, even if the auxiliary variable has a substantial proportion of missing data and the missing data mechanism of the auxiliary variables had little impact on bias.Abstract:
Recent missing data studies have argued in favor of an “inclusive analytic strategy” that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to ask whether the inclusion of such variables will improve the estimation of model parameters. Simulation results indicated that the proportion of missing data and the missing data mechanism of the auxiliary variables had little impact on bias. Even when an auxiliary variable was missing not at random, bias was relegated to the auxiliary variable portion of the model, and did not propagate into the model of substantive interest. The study results suggest that the inclusion of an auxiliary variable is beneficial, even if the auxiliary variable has a substantial proportion of missing data.read more
Citations
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Journal ArticleDOI
Toward best practices in analyzing datasets with missing data: Comparisons and recommendations
David R. Johnson,Rebekah Young +1 more
TL;DR: Modern missing data techniques were found to perform better than traditional ones, but differences between the types of modern approaches had minor effects on the estimates and substantive conclusions.
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Latent profile analysis: A review and “how to” guide of its application within vocational behavior research
TL;DR: Latent profile analysis (LPA) is a categorical latent variable approach that focuses on identifying latent subpopulations within a population based on a certain set of variables.
Journal ArticleDOI
Multiple imputation as a flexible tool for missing data handling in clinical research
TL;DR: The authors describes a number of practical issues that clinical researchers are likely to encounter when applying multiple imputation, including mixtures of categorical and continuous variables, item-level missing data in questionnaires, significance testing, interaction effects, and multilevel missing data.
Journal ArticleDOI
Multiple Imputation of Missing Data for Multilevel Models: Simulations and Recommendations
TL;DR: This paper provides guidance using MI in the context of several classes of multilevel models, including models with random intercepts, random slopes, cross-level interactions (CLIs), and missing data in categorical and group-level variables.
References
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Book
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.
Journal ArticleDOI
Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
Journal ArticleDOI
Statistical Analysis with Missing Data
Book
Analysis of Incomplete Multivariate Data
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.