This article compares two missing data procedures, full information maximum likelihood
(FIML) and multiple imputation (MI), to investigate their relative performances in relation to …

Multiple imputation has enjoyed widespread use in social science applications, yet the
application of imputation-based inference to structural equation modeling has received …

As with other statistical methods, missing data often create major problems for the estimation
of structural equation models (SEMs). Conventional methods such as listwise or pairwise …

This chapter describes a general approach to handling missing data in psychological
research. It provides a theoretical background in readable, nontechnical fashion. Our overall …

Conventional wisdom in missing data research dictates adding variables to the missing data
model when those variables are predictive of (a) missingness and (b) the variables …

Fraction of missing information λ j is a useful measure of the impact of missing data on the
quality of estimation of a particular parameter. This measure can be computed for all …

A wealth of options exists for analyzing structural equation models (SEM) with missing data,
including the expectation maximization (EM) algorithm, full information maximum likelihood …

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 …

The study examined the performance of maximum likelihood (ML) and multiple imputation
(MI) procedures for missing data in longitudinal research when fitting latent growth models …

A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in
structural equation models with nonnormal indicator variables. The impacts of 4 …