#MATRIX STRUCTURAL ANALYSIS EXAMPLES PLUS#

The variance in response to each item in the test reflects individual differences in verbal ability across test takers, plus some error. For example, suppose we have a test of vocabulary. The basic idea is that a latent variable or factor is an underlying cause of multiple observed behaviors. In the psychometrics literature, latent variables are also called factors, and have a rich history of statistical developments in the literature on factor analysis. cognitive ability), Type A personality, and depression. Examples in psychology include intelligence (a.k.a. SEM uses latent variables to account for measurement error.Ī latent variable is a hypothetical construct that is invoked to explain observed covariation in behavior. The main difference between the two types of models is that path analysis assumes that all variables are measured without error. Most of the models that you will see in the literature are SEM rather than path analyses.

Path analysis contains only observed variables, and has a more restrictive set of assumptions than SEM.

What is a latent variable? What is an observed (manifest) variable? How does SEM handle measurement errors? Why does SEM have an advantage over regression and path analysis when it comes to multiple indicators? What are the two submodels in a structural equation model? What are their functions? How are observed correlations linked to the parameters of a structural equation model (via the diagram, that is)? Why can't we conclude cause and effect from structural equation models where there is no manipulation of variables? What is the danger of model fiddling?

Matrix structural analysis examples