Exploratory Structural Equation Modeling

Exploratory Structural Equation Modeling

Exploratory factor analysis (EFA) has been said to be the most frequently used multivariate analysis technique in statistics. Jennrich and Sampson (1966) solved a significant EFA factor loading matrix rotation problem by deriving the direct Quartimin rotation. Jennrich was also the first to develop standard errors for rotated solutions although these have still not made their way into most stat...

Supplementary Materials For: Exploratory Structural Equation Modeling

Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis.

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM) have long histories in clinical research. Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differe...

Structural Equation Modeling

This chapter provides an introduction to structural equation modeling (SEM), a statistical technique that allows scientists and researchers to quantify and test scientific theories. As an example, a model from behavioral genetics is examined, in which genetic and environmental influences on a trait are determined. The many procedures and considerations involved in SEM are outlined and described...

Structural Equation Modeling

Structural equation modeling (SEM) grows out of and serves purposes similar to multiple regression, but in a more powerful way which takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indica...