In covariance structure modeling, several estimation methods are available. The robustness of an estimator against specific violations of assumptions can be determined empirically by means of a Monte Carlo study. Many such studies in covariance structure analysis have been published, but the conclusions frequently seem to contradict each other An overview of robustness studies in covariance structure analysis is given, and an attempt is made to generalize findings. Robustness studies are described and distinguished from each other systematically by means of certain characteristics. These characteristics serve as explanatory variables in a meta-analysis concerning the behavior of parameter estimators, standard error estimators, and goodness-...
This investigation studied the behavior of fit indices in covariance structure modeling (CSM) when s...
Methods of covariance structure modeling are frequently applied in psychological research. These met...
This paper deals with models of covariance structures and methods for their analysis. First, we disc...
In covariance structure modeling, several estimation methods are available. The robustness of an est...
This thesis investigates the robustness of estimation methods in covariance structure analysis (CSA)...
Problems about whether a hypothesized covariance structure models is an appropriate representation o...
This study evaluated the sensitivity of maximum likelihood (ML)-, generalized least squares (GLS)-, ...
The robust regression analysis works on data affected by deviations from a general assumption of nor...
This study compares item and examinee properties, studies the robustness of IRT models, and examines...
Covariance structure modeling, also known as structural equation modeling or causal modeling, appear...
Meta-analysis is the synthesis of information from multiple primary studies of similar design. A use...
In a typical study involving covariance structure modeling, fit of a model or a set of alternative ...
Correlated parameters are often expected when modeling a natural system. However, correlation among ...
The vast majority of structural equation models contain no mean structure, that is, the population m...
A popular model in structural equation modeling involves a multivariate normal density with a struct...
This investigation studied the behavior of fit indices in covariance structure modeling (CSM) when s...
Methods of covariance structure modeling are frequently applied in psychological research. These met...
This paper deals with models of covariance structures and methods for their analysis. First, we disc...
In covariance structure modeling, several estimation methods are available. The robustness of an est...
This thesis investigates the robustness of estimation methods in covariance structure analysis (CSA)...
Problems about whether a hypothesized covariance structure models is an appropriate representation o...
This study evaluated the sensitivity of maximum likelihood (ML)-, generalized least squares (GLS)-, ...
The robust regression analysis works on data affected by deviations from a general assumption of nor...
This study compares item and examinee properties, studies the robustness of IRT models, and examines...
Covariance structure modeling, also known as structural equation modeling or causal modeling, appear...
Meta-analysis is the synthesis of information from multiple primary studies of similar design. A use...
In a typical study involving covariance structure modeling, fit of a model or a set of alternative ...
Correlated parameters are often expected when modeling a natural system. However, correlation among ...
The vast majority of structural equation models contain no mean structure, that is, the population m...
A popular model in structural equation modeling involves a multivariate normal density with a struct...
This investigation studied the behavior of fit indices in covariance structure modeling (CSM) when s...
Methods of covariance structure modeling are frequently applied in psychological research. These met...
This paper deals with models of covariance structures and methods for their analysis. First, we disc...