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 (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...

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Abstract Structural equation models (SEMs) with latent variables are routinely used in social science research, and are of increasing importance in biomedical applications. Standard practice in implementing ...

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...

In conventional structural equation models, all latent variables and indicators vary between units (typically subjects) and are assumed to be independent across units. The latter assumption is violated in multilevel settings where units are nested in clusters, leading to within-cluster dependence. Different approaches to extending structural equation models for such multilevel settings are exam...

Iterative numerical procedures for the estimation of variance and covariance components for unbalanced designs were developed in the 1980s and were implemented in software packages such as MLWIN, SAS PROC MIXED and HLM. At the same time, interest in latent variables, that is, variables that cannot be directly observed or can only imperfectly be observed, led to the theory providing for the defi...

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to...

In the behavioral sciences, response variables are often noncontinuous, common types being dichotomous, ordinal or nominal variables, counts and durations. Conventional structural equation models (SEMs) have thus been generalized to accommodate different kinds of responses.

Path Analysis is the statistical technique used to examine causal relationships between two or more variables. It is based upon a linear equation system and was first developed by Sewall Wright in the 1930s for use in phylogenetic studies. Path Analysis was adopted by the social sciences in the 1960s and has been used with increasing frequency in the ecological literature since the 1970s. In ec...