Unlike multilevel data with a purely nested structure, data that are cross-classified not only
may be clustered into hierarchically ordered units but also may belong to more than one unit …

Aspects of misspecification in statistical models: Applications to latent variables,
measurement error, random effects, omitted covariates and incomplete data Aspects of …

Multiple-indicators multiple-causes (MIMIC) modeling is often employed for measurement
invariance testing under the structural equation modeling framework. This Monte Carlo study …

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 …

When data analysis tools require that every variable be observed on each case, then
missing items on a subset of variables force investigators either to leave potentially …

This dissertation addressed two issues regarding the assessment of model fit in multilevel
structural equation modeling. Study 1 investigated whether the standard procedure, the use …

In structural equation modeling, several corrections to the likelihood-ratio model test statistic
have been developed to counter the effects of non-normal data. Previous robustness studies …

In his target article (Barrett, 2007), Paul Barrett argues persuasively against continued use of
approximate fit indices in SEM in relation to current thresholds for acceptable fit, given recent …

A problem which frequently arises in regression analysis is the presence of missing values
on the explanatory variables. Imputation is a time-honoured approach to tackling it, since …

Data in social and behavioral sciences are routinely collected using questionnaires, and
each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) …