On the use of Structural Equation Models and PLS Path Modeling to build composite indicators

Nowadays there is a pre-eminent need to measure very complex phenomena like poverty, progress, well-being, etc. As is well known, the main feature of a composite indicator is that it summarizes complex and multidimensional issues. Thanks to its features, Structural Equation Modeling seems to be a useful tool for building systems of composite indicators. Among the several methods that have been developed to estimate Structural Equation Models we focus on the PLS Path Modeling approach (PLS-PM), because of the key role that estimation of the latent variables (i.e. the composite indicators) plays in the estimation process. In this work, first we present Structural Equation Models and PLS-PM. Then we provide a suite of statistical methodologies for handling categorical indicators in PLS-PM. In particular, in order to take categorical indicators into account, we propose to use a modified version of the PLS-PM algorithm recently presented by Russolillo [2009]. This new approach provides a quantification of the categorical indicators in such a way that the weight of each quantified indicator is coherent with the explicative ability of the corresponding categorical indicator. To conclude, an application involving data taken from a paper by Russet [1964] will be presented.PLS Path Modeling,Categorical Indicators,Structural Equation Modeling,Composite Indicators

Partial Least Squares Methods for Non-Metric Data

Partial Least Squares (PLS) methods embrace a suite of data analysis techniques based on algorithms belonging to PLS family. These algorithms consist in various extensions of the Nonlinear estimation by Iterative PArtial Least Squares (NIPALS) algorithm, which was proposed by Herman Wold as an alternative algorithm for implementing a Principal Component Analysis. The peculiarity of this algorithm is that it calculates principal components by means of an iterative sequence of simple ordinary least squares regressions. This feature allows overcoming computational problems due to missing data or landscape data matrices, i.e. matrix having more columns than rows. PLS methods were born to handle data sets forming metric spaces. This involves that all the variables embedded in the analysis are observed on interval or ratio scales. In this work we evidenced how NIPALS based algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had been until now totally unexplored, allowed us to device a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. NM-PLS methods can be used with different aims: - to analyze at the same time variables observed on different measurement scales; - to investigate non linearity; - to discard the hard assumption of linearity in favor of a milder assumption of monotonicity. In particular, these methods generalize standard NIPALS, PLS Regression and PLS Path Modeling in such a way to handle variables observed on a variety of measurement scales, as well as to cope with non linearity problems. Three new algorithms are been proposed to implement NM-PLS methods: the Non-Metric NIPALS algorithm, the Non-Metric PLS Regression algorithm, and the Non-Metric PLS Path Modeling algorithm. All these algorithms provide at the same time specific PLS model parameters as well as scaling values for variables to be scaled. Scaling values provided by these algorithms are been proved to be optimal, in the sense that they optimize the same criterion of the model in which they are involved. Moreover, they are suitable, since they respect the constraints depending on which among the properties of the original measurement scale we want to preserve

About the influence of quantification in PLS-PM for customer satisfaction

Due to the need to formalize models relating latent concepts, Partial Least Squares Path Modeling has been widely used in marketing research for the quantitative analysis of customer satisfaction. However, in marketing applications latent concepts are expressed as a synthesis of variables that cannot be measured strictu sensu. Typically, in fact, the consumer is asked to express the level of agreement to a statement, or a judgment about particular characteristics of the offered product or service, choosing one out of a set of ordered response levels. Variables observed in such a way, however, cannot be considered numerical, as they are not measured on an interval scale. Nearly always, in order to directly obtain quantitative values, the interviewer asks the interviewee to associate the agreement level to one of the values on a certain scale (e.g. 1-10 or 1-100). As a matter of fact, this procedure implies an a priori quantification of non-metric variables, which follows two rules:•Quantifications are different for each level and equally spaced•Metric is the same for all the variables in the model. Through a sensitivity study, we investigate how the choice of different quantifications affects model quality and parameter estimation

An integrated PLS Regression-based approach for multidimensional blocks in PLS path modeling

International audienceL'approche PLS aux modèles à équations structurelles (PLS Path Modeling, PLS-PM) est couramment considérée comme une approche basée sur les composantes. Cette méthode a été récemment revisitée en tant que cadre général pour l'analyse des tableaux multiples. Nous proposons ici deux nouvelles méthodes d'estimation des poids externes dans le cadre de la PLS-PM: le Mode PLScore et le Mode PLScow. Chaque mode est fondé sur l'utilisation de la régression PLS pour l'étape d'estimation externe. Toutefois, en Mode PLScore une régression PLS est exécutée sous les contraintes classiques de la PLS-PM de variance unitaire pour les scores des variables latentes ; tandis que dans le Mode PLScow les poids externes sont contraints d'avoir une norme unitaire. Cette dernière contrainte est la contrainte classique de normalisation dans le cadre de la régression PLS. Nous montrons comment les deux nouveaux modes sont liés aux méthodes d'estimation externe classiques de la PLS-PM, c.-à-d. au Mode A et au Mode B, ainsi qu'au Nouveau Mode A récemment proposé par Tenenhaus & Tenenhaus (2009)

Évaluation de la présence des capacités marketing. Proposition d’un index multidimensionnel et hiérarchique

International audienceNotre ambition est de proposer un instrument multidimensionnel permettant de décrire le degré de présence des principales capacités marketing sur trois niveaux d’abstraction. Après avoir présenté le cadre théorique relatif aux capacités marketing, l’article souligne tout d’abord les limites des principales échelles proposées par Vorhies et al. (1999 ; 2009), Vorhies et Harker (2000), et Vorhies et Morgan (2003 ; 2005). Ensuite, les étapes nécessaires au développement et à la validation d’un index multidimensionnel formatif de troisième ordre sont détaillées. Sur la base d’une collecte de données réalisée auprès d’un échantillon de 199 PME françaises, la phase d’analyse de la validité convergente et discriminante de l’instrument est réalisée à l’aide de l’approche PLS aux modèles à variables latentes (PLS-PM). Enfin, la validité nomologique de l’instrument proposé est confirmée via l’étude de l’influence des capacités marketing sur la performance organisationnelle

MID1 mutations in patients with X-linked Opitz G/BBB syndrome

Mutations in the MID1 gene are responsible for the X-linked form of Opitz G/BBB syndrome (OS), a disorder that affects the development of midline structures. OS is characterized by hypertelorism, hypospadias, laryngo-tracheo-esophageal (LTE) abnormalities, and additional midline defects. Cardiac, anal, and neurological defects are also present. The expressivity of OS is highly variable, even within the same family. We reviewed all the MID1 mutations reported so far, in both familial and sporadic cases. The mutations are scattered along the entire length of the gene and consist of missense and nonsense mutations, insertions and deletions, either in-frame or causing frameshifts, and deletions of either single exons or the entire MID1 coding region. The variety of described mutations and the lack of a strict genotype-phenotype correlation confirm the previous suggestion of the OS phenotype being caused by a loss-of-function mechanism. However, although a specific mutation cannot entirely account for the observed phenotype, we observed preferential association between some types of mutation and specific clinical manifestations, e.g., brain anatomical defects and truncating mutations. This may suggest that the pathogenetic mechanism underlying the OS phenotype is more complex and may vary among the affected organs

A PLS Path Model for Predicting Impact of Job Characteristics on Work-Related Stress

8th International Conference on Partial Least Squares and related MethodsWe propose to use PLS path modeling to predict stressors requiring priority action from managers to reduce work-related stress of their company employees