Practice of stuctural equation modeling

SEM here is for the short of structural equation model, as the so-called second generation of classical multivariate statistics methods, it can be well-used for studying the the relationship of quantitative and qualitative methods via path-modeling-graph. It can become a powerful tool to conduct comprehensive analysis on problem defining, as the important fundation of other kinds of analytics.

Two popular methods dominate SEM in practice: covariance-based SEM (CBSEM) and partial least squares SEM (PLS-SEM, also called PLS path modeling). CB-SEM is primarily used to confirm (or reject) theories and their underlying hypotheses. This approach confirms/rejects hypotheses by determining how closely a proposed theoretical model can reproduce the covariance matrix for an observed sample dataset. In contrast, PLS has been introduced as a “causal–predictive” approach to SEM (Jöreskog & Wold, 1982, p. 270), which focuses on explaining the variance in the model’s dependent variables (Chin et al., 2020)

Here are the tutorials to quickly get started with the methods

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R(2022)

PLS-SEM Book: A Primer on PLS-SEM (3rd Ed.)