This paper adds to an important aspect of Partial Least Squares (PLS) path modeling, namely the convergence of the iterative PLS path modeling algorithm. Whilst conventional wisdom says that PLS always converges in practice, there is no formal proof for path models with more than two blocks of manifest variables. This paper presents six cases of non-convergence of the PLS path modeling algorith...

The potential effect on respondent burden is a major consideration in the evaluation of survey design options, so the ability to quantify the burden associated with alternative designs would be a useful evaluation tool. Furthermore, the development of such a tool could facilitate more systematic examination of the association between burden and data quality. In this study, we explore the applic...

Partial least squares (PLS) is sometimes used as an alternative to covariance-based structural equation modeling (SEM). This paper briefly reviews currently available SEM techniques, and provides a critique of the perceived advantages of PLS over covariance-based SEM as commonly cited by PLS users. Specific attention is drawn to the primary disadvantage of PLS, namely the lack of consistency of...

The Global Information Technology Report published by World Economic Forum used Networked Readiness Index (NRI) to measure the global competitiveness of a country’s information and communication technologies (ICT). The NRI covers three subindexes with nine pillars, which are treated with equal weights. It does not explore the causal relationships. In order to provide more information to the pol...

Predictive model selection metrics are used to select models with the highest out-of-sample predictive power among a set of models. R 2 and related metrics, which are heavily used in partial least squares path modeling, are often mistaken as predictive metrics. We introduce information theoretic model selection criteria that are designed for out-of-sample prediction and which do not require cre...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bia...

Several applications of continuum regression to non-contaminated data have shown that a significant improvement in predictive power can be obtained compared to the three standard techniques which it encompasses (Ordinary least Squares, Principal Component Regression and Partial Least Squares). For contaminated data continuum regression may yield aberrant estimates due to its non-robustness with...