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 ...

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 ...

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 ...

来自多领域的研究人员针对结构方程模型(SEM)规范搜索难题，创新性地将机器学习中的随机森林算法与SEM结合，开发出SEM Forests方法。该研究通过模拟数据和实证案例验证了该方法在模型选择、参数估计和拟合优度检验方面的优越性，为复杂心理测量模型和生物 ...

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated ...

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 ...