Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, ...

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 ...

Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates. This ...

Meta-Analytic Structural Equation Modeling (MASEM) represents a powerful and integrative statistical framework that combines the rigour of meta-analysis with the complexity of structural equation ...

R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...