Structural equation modeling (SEM) applications routinely employ a trilogy of significance
tests that includes the likelihood ratio test, Wald test, and score test or modification index …

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

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

The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model
fit of structural equation models for ordinal variables with multiply imputed data. However …

Missing data and nonnormality are two common factors that can affect analysis results from
structural equation modeling (SEM). The current study aims to address a challenging …

The use of item responses from questionnaire data is ubiquitous in social science research.
One side effect of using such data is that researchers must often account for item level …

Although structural equation modeling software packages use maximum likelihood
estimation by default, there are situations where one might prefer to use multiple imputation …

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 …

In structural equation modeling, when multiple imputation is used for handling missing data,
model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under …

The performance of multiple imputation in questionnaire data has been studied in various
simulation studies. However, in practice, questionnaire data are usually more complex than …