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 ...

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 ...

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 ...

Regularized estimation methods have become increasingly popular with the advent of 'Big Data' and machine learning methods. Regularized estimation purposefully introduces a small bias in order to ...

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 ...

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

Kline, R. B. (in production). Get ready for data science: A primer for social and behavioral scientists. Guilford Press. Kline, R. B. (2023). Principles and practice ...

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 ...