Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance ...

Genome-wide expression and protein profiles provide powerful tools for large-scale analyses of gene interaction and identification of pathways underlying cells' response to perturbations. Clustering ...

Consider fitting a linear equation to two observed variables, Y and X. Simple linear regression uses the model of a particular form, labeled for purposes of discussion, as Model Form A. You can also ...

This course consists of two sections: Section 1 demonstrates linear regression to model the linear relationship between a response and predictor(s) when both the response and predictors are continuous ...

Latent factors are variables that cannot be observed directly but can be inferred from a set of observable variables. For example, in psychology, bad conduct (latent factor) can be measured by how ...

This software specialises in three areas: models with responses at several levels of a data hierarchy, multilevel structural equation models, and measurement error ...

This is a preview. Log in through your library . Abstract Model misspecifications may have a systematic effect on parameters, causing biases in their estimates. In the application of structural ...

The CALIS procedure (Covariance Analysis and Linear Structural Equations) in SAS/STAT software estimates parameters and tests the appropriateness of linear structural equation models using covariance ...