Enrico Ciavolino

Comparing generalised maximum entropy and partial least squares methods for structural equation models

The generalised maximum entropy (GME) method is presented for estimating structural equation models, where a real data set of the Service & Motor Vehicle Industry in Sweden is used to show the implementation of the method. Monte Carlo simulation comparisons are also made between GME and partial least squares (PLS) methods in the presence of messy data. Three cases are considered: outliers, missing data and multicollinearity. It is shown that this method can be considered a valid alternative to the conventional method of PLS, where the results of GME, in terms of mean squared error, outperform the PLS results in some respects.

Simultaneous Equation Model based on the generalized maximum entropy for studying the effect of management factors on enterprise performance

The aim of this paper is to study the effect of management factors on enterprise performance, considering a survey that the University Consortium in Engineering for Quality and Innovation has led. The relationships between management factors and enterprise performance are formalized by a Simultaneous Equation Model based on the generalized maximum entropy (GME) estimation method. The format of this paper is as follows. In Section 2, the data collected, the questionnaire evaluation, and the management model analytical formulation are introduced. In Section 3, the GME formulation is specified, showing the main characteristics of the estimation method. In Section 4, the results and a comparison among GME, partial least squares (PLS), and maximum likelihood estimation (MLE) is shown. In Section 5, concluding remarks are discussed.

Analyzing psychotherapy process as intersubjective sensemaking: An approach based on discourse analysis and neural networks

Abstract The authors propose a method for analyzing the psychotherapy process: discourse flow analysis (DFA). DFA is a technique representing the verbal interaction between therapist and patient as a discourse network, aimed at measuring the therapist–patient discourse ability to generate new meanings through time. DFA assumes that the main function of psychotherapy is to produce semiotic novelty. DFA is applied to the verbatim transcript of the psychotherapy. It defines the main meanings active within the therapeutic discourse by means of the combined use of text analysis and statistical techniques. Subsequently, it represents the dynamic interconnections among these meanings in terms of a “discursive network.” The dynamic and structural indexes of the discursive network have been shown to provide a valid representation of the patient–therapist communicative flow as well as an estimation of its clinical quality. Finally, a neural network is designed specifically to identify patterns of functioning of the discursive network and to verify the clinical validity of these patterns in terms of their association with specific phases of the psychotherapy process. An application of the DFA to a case of psychotherapy is provided to illustrate the method and the kinds of results it produces.

Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models

The aim of this paper is to define a new approach, called Hybrid Two-Step, to estimate the parameters of a second-order latent variable (LV) model in the case of formative relationships between the first-order and the second-order LVs. In this respect, we introduce the two main approaches to the estimation of second-order constructs through the partial least squares-path modelling: the so-called Repeated Indicators approach and the Two-Step approach. Some criticisms of these methodologies are highlighted and a solution to the issue of the identification of formative second-order constructs is suggested through the adoption of a Hybrid Two-Step approach. A Monte Carlo simulation study aimed at comparing the approach proposed with the traditional ones was performed. Finally, a case study about the passenger satisfaction is presented to show the implementation of the method and to give some comparative empirical results.

Simulation Study for PLS Path Modelling with High-Order Construct: A Job Satisfaction Model Evidence

The aim of the paper is to present a study on the high-order latent variables for the partial least squares path modelling (PLS-PM).

Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches

The objective of this paper is to describe and analyse with appropriate statistical models the links between work quality latent factors. Due to the complexity of the task, the analysis is carried out through a two-step approach: In the first step, we construct some multidimensional measures of the subjective quality of work, using nonlinear principal component analysis (NPCA) and Rasch analysis with the Rating Scale Model (NPCA-RSM); In the second step, we adopt a Structural Equation Model based on generalized maximum entropy (SEM-GME) to integrate the measures achieved with the previous step and to evaluate the relationships between the subjective work quality latent factors. Therefore, the novel aspects of this paper are the following: (i) The integration between the NPCA-RSM and SEM-GME, which allows reduction of the variables analysed and evaluation of the measurement errors; (ii) The formalization of a Job Satisfaction Model for the study of the relationships between the subjective work quality latent factors in the Italian social services sector.

A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration

The paper provides a procedure aimed at obtaining more interpretable second-order models estimated with the partial least squares-path modeling. Advantages in interpretation stem from the separation of the two sources of influence on the data. As a matter of fact, in hierarchical models effects on manifest variables (MVs) are assigned to both first-order (specific) factors and second-order (general) factors. In order to separate these overlapping contributions, MVs are deflated from the effect of the specific latent variables (LVs) and used as indicators of the second-order LV. A case study is presented in order to illustrate the application of the proposed method.

Generalized cross entropy method for analysing the SERVQUAL model

The aim of this paper is to define a new approach for the analysis of data collected by means of SERVQUAL questionnaires which is based on the generalized cross entropy (GCE) approach. In this respect, we firstly give a short review about the important role that SERVQUAL plays in the analysis of service quality as well as in the assessment of the competitiveness of public and private organizations. Secondly, we provide a formal definition of GCE approach together with a brief discussion about its features and usefulness. Finally, we show the application of GCE for a SERVQUAL model, based on a patients’ satisfaction case study and we discuss the results obtained by using the proposed GCE-SERVQUAL methodology.

A Generalized Maximum Entropy (GME) estimation approach to fuzzy regression model

Graphical abstractDisplay Omitted HighlightsWe consider two fuzzy regression models from fuzzy least squares tradition.We rewrite these models within the Generalized Maximum Entropy Approach of estimation.We compare LS and GME approaches in the multicollinearity problem.Monte Carlo studies show increasing multicollinearity GME outperforms LS in efficiency.Empirical evidence shows some applicative advantages of GME. Fuzzy statistics provides useful techniques for handling real situations which are affected by vagueness and imprecision. Several fuzzy statistical techniques (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Among these, fuzzy regression can be considered an important tool for modeling the relation between a dependent variable and a set of independent variables in order to evaluate how the independent variables explain the empirical data which are modeled through the regression system. In general, the standard fuzzy least squares method has been used in these situations. However, several applicative contexts, such as for example, analysis with small samples and short and fat matrices, violation of distributional assumptions, matrices affected by multicollinearity (ill-posed problems), may show more complex situations which cannot successfully be solved by the fuzzy least squares. In all these cases, different estimation methods should instead be preferred. In this paper we address the problem of estimating fuzzy regression models characterized by ill-posed features. We introduce a novel fuzzy regression framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform two Monte Carlo experiments and we analyze a real case study.

An automated method of content analysis for psychotherapy research: A further validation

Abstract Objective: The aim of the study is to validate the ability of ACASM (Automated Co-occurrence Analysis for Semantic Mapping) to provide a representation of the content of the therapeutic exchange that is useful for clinical analysis. Method: We compared the clinical case analyses of a good outcome psychodynamic therapy performed by a group of clinicians (n = 5) based on the verbatim transcripts (transcript-based analysis) with the clinical case analyses performed by another group of clinicians (n = 5) based on the ACASM representation of the same sessions (ACASM-based analysis). Comparison concerned two levels: the descriptive level and the interpretative level of the clinical case analysis. Results: Findings showed that, inconsistently with our hypothesis, ACASM-based descriptions of the case obtained worse evaluations than transcript-based descriptions of the case (on all 3 criteria adopted). On the contrary, consistently with our hypothesis, ACASM is undistinguishable from the verbatim transcripts as regards the case interpretation (on 2 out of 3 criteria adopted). Conclusions: ACASM provides a description of the case that, though different from the one provided by the transcripts, enables clinicians to elaborate clinical interpretations of the case which approximate those produced by clinicians working directly on verbatim transcripts.