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 ...

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press. Kline, R. B. (2020). Becoming a behavioral science researcher ...

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 ...

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, ...

Ecology and Society, Vol. 20, No. 1 (Mar 2015) (14 pages) ABSTRACT. Underuse of nonindustrial private forests in developed countries has been interpreted mostly as a consequence of the prevailing ...

Developed by one of the world's leading authorities on the subject, Dr. Peter M. Bentler, EQS provides researchers and statisticians with a simple method for conducting the full range of structural ...

Regulators need a method that is versatile, is easy to use and can handle complex path models with latent (not directly observable) variables. In a first application of partial least squares ...

Statistical model infrastructures at financial institutions are often developed using a piecemeal approach to model building, in which different components of complex interrelated statistical models ...