MEM and SEM in the GME framework: Statistical Modelling of Perception and Satisfaction

This paper presents a review of the original method recently developed by the authors with the Generalized Maximum Entropy (GME) estimator for the simple linear Measurement Error Model (MEM) and the Structural Equation Model (SEM). In socio-economic research, these two models often concern subjective or psychological variables (composite indicators), and represent relations between latent variables. In this review, two applications to the statistical modelling of economic perception and job satisfaction are presented

ECSI - Customer Satisfaction Modelling and Analysis: A Case Study

In this paper we analyse the European Customer Satisfaction Index (ECSI) model measured on five different customer groups. In the analysis we use Structural Equation Modelling, which is a general and convenient framework for statistical analysis, including traditional multivariate procedures such as factor analysis or multiple regression modelling. To estimate the structural coefficients, a Multilevel Regression Model is used, which facilitates the analysis of hierarchical data, where observations may be nested within higher levels of classification. In this analysis the hierarchical structure is represented by the customers nested in the companies, so we have two levels of analysis, a micro level nested in a macro level

The incidence of regional factors on "competitive performance” of universities

The performance of single universities, beyond internal determinants, is influenced by the conditions of their own territorial context, that is by a number of factors related to their local geographical area, meant as a space of territorial interactions, measurable by its previous relational dynamics. This set of factors can, directly or indirectly, affect both the organizational structure and strategic orientations of the single university, as well as the results achieved by it in the field of education and research.Through a multi-dimensional statistical model, the evaluation criteria for university performance will be compared to some territorial variables which, in scientific literature, are considered to be indexes of territorial competitiveness. The statistical model aims at measuring the impact local context has on the competitive performance of universities, explaining the nature and intensity of this relationship. With reference to the objectives of the research, data we will use refer to two different sets of indicators: on the one hand, data used to evaluate university performance, on the other hand, the ones used to measure territorial competitiveness. In more detail, university performance is based on some of the indicators used by the CENSIS in the "University Ranking 2010" referring to the following databases: MIUR-Statistical Office; CINECA; CNVSU; National LLP Agency Italy; CRUI; CORDIS. Territorial data, instead, are extracted from the "Atlas of the Provinces and Regions competitiveness” elaborated by UNIONCAMERE. For both sets of indicators, the analysis will refer to the year 2008.If the indicators of university performance are correlated to territorial conditions, they don’t really measure university productivity/competitiveness, but rather the competitiveness of its territorial context. This can lead to some distortions in the financial resources allocation and, more generally, in national supporting policies to public universities.In their conclusions, authors also tend to reverse the perspective through which the role of government intervention has been traditionally interpreted. If universities are qualifying elements of territorial competitiveness – as it is shown by the fact that they are frequently used within the set of indicators to measure it – the strengthening of university system should be one of the priority objectives of regional development policies. Consequently, national government should invest in university education and research, even where university performance, due to some specific local conditions, is not satisfactory or even below fixed national or international standards.

A tale of PLS Structural Equation Modelling: Episode I- A Bibliometrix Citation Analysis

The structure of knowledge about Structural Equation Modelling (SEM) based on the Partial Least Squares (PLS) estimator has been analysed by systematic and reproducible bibliometric citation analysis. This contribution aims to create a dynamic picture of the PLS-SEM research activity to support scholars with an enhanced understanding of the history, the present and the future directions of this fascinating modelling approach. Analysis was conducted using the Bibliometrix packageR with documents extracted (n = 3,854) from the Web of Science (WoS) database by Clarivate. Hence, we find seminal papers in the context of PLS-SEM as well as the diffusion and use in different research domains, suggesting new directions of applications. We also identify the collaboration networks involving authors and countries to highlight the new potential for cooperation from a co-authorship and international project standpoint. Furthermore, the dynamics of the sources indicate the interest of journals in this field in a dissemination role, which can assist authors in selecting a suitable publisher. Finally, the historiographic overview shows the dominant topics and the possible evolution in the citation analysis from the theoretical and application angles

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

A tale of PLS Structural Equation Modelling: Episode I— A Bibliometrix Citation Analysis

The structure of knowledge about Structural Equation Modelling (SEM) based on the Partial Least Squares (PLS) estimator has been analysed by systematic and reproducible bibliometric citation analysis. This contribution aims to create a dynamic picture of the PLS-SEM research activity to support scholars with an enhanced understanding of the history, the present and the future directions of this fascinating modelling approach. Analysis was conducted using the Bibliometrix packageR with documents extracted (n = 3,854) from the Web of Science (WoS) database by Clarivate. Hence, we find seminal papers in the context of PLS-SEM as well as the diffusion and use in different research domains, suggesting new directions of applications. We also identify the collaboration networks involving authors and countries to highlight the new potential for cooperation from a co-authorship and international project standpoint. Furthermore, the dynamics of the sources indicate the interest of journals in this field in a dissemination role, which can assist authors in selecting a suitable publisher. Finally, the historiographic overview shows the dominant topics and the possible evolution in the citation analysis from the theoretical and application angles

Economic Design of Acceptance Sampling Plans for Truncated Life Tests Using Three-Parameter Lindley Distribution

A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans