Quantile composite-based path modeling: algorithms, properties and applications

TL;DR: In this article , the authors provide statistical models, algorithms and quantitative evidence regarding environmental effect evaluation (EEE), and apply a quantile-type path-modeling algorithm in the developed EEE model at different quantile levels.

TL;DR: Quantile-based PLS-SEM with bag of little bootstraps explores the quantile-based structural equation model and proposes a quantile-based partial least square algorithm with bag of little bootstraps.

TL;DR: This article introduces a varying-coefficients latent variable model, expanding classical latent variable models, and develops a local-polynomial estimation method for it, with comparisons to existing quantile varying-coefficients models through simulation studies and real data analysis.

TL;DR: In this paper , the authors used lasso penalized quantile regression first and then proposed a comprehensive evaluation indicator and modified partial least square algorithms based on the quantile-based third-order latent variable model.

TL;DR: The Second Edition of this practical guide to partial least squares structural equation modeling is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.

TL;DR: Although structural equation modelling (SEM) is a popular statistical technique for multivariate data analysis in social and behavioural sciences, its use in education has massified more recently as mentioned in this paper.

TL;DR: A comprehensive overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting can be found in this paper, where the authors provide an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLSSEM.

TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.