The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …

To ensure meaningful comparison of test scores across groups or time, measurement
invariance (ie, invariance of the general factor structure and the values of the measurement …

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had
evaluated the performance of different techniques when all observed variables were either …

Interpreting regional differences is crucial in promoting regional and cohesion policies. The
different levels of development of the regions, their various historical and geographical …

Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However,
missing data are also common in empirical research, and multiple imputation (MI) is often …

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 …

In many applied situations, the questionnaire items in measurement instruments do not
approximate continuous, normally distributed variables but instead are ordinal. Properties of …

Purpose Random item effects item response theory (IRT) models have received much
attention for more than a decade. However, more research is needed on random item effects …

Social science research often utilizes measurement instruments that generate ordinal data
(eg, Likert scales). Many empirical studies also face the challenge of missing data, which …

We propose and demonstrate a new two-stage maximum likelihood estimator for parameters
of a social relations structural equation model (SR-SEM) using estimated summary statistics …