Mariangela Nitti

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.

Automated method of content analysis: A device for psychotherapy process research

Abstract The work presents a computer-aided method of content analysis applicable to verbatim transcripts of psychotherapy: the Automated Co-occurrence Analysis for Semantic Mapping (ACASM). ACASM is able to perform a context-sensitive strategy of analysis aimed at mapping the meanings of the text through a trans-theoretical procedure. The paper is devoted to the presentation of the method and testing its validity. To the latter end we have compared ACASM and independent blind human coders on two tasks of content analysis: (a) estimating the semantic similarity between two utterances; (b) the semantic classification of a set of utterances. Results highlight that: (a) ACASMs estimates of semantic similarity are consistent with the corresponding estimates provided by coders; (b) coders’ agreement and coder-ACASM agreement on the task of semantic classification have the same magnitude. Results lead to the conclusion that the content analysis produced by ACASM is indistinguishable from that performed by human coders.

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).

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.

Women Resignation During Maternal Leave

Abstract Pregnant women and mothers are one of the most vulnerable parts of the workforce. Despite legal provisions are intended to safeguard the motherhood and prevent the mothers’ discrimination, the phenomenon of the voluntary resignation during maternity leave is a current issue in the Southern-Italy society. The multifaceted aspects characterizing the reasons leading the mothers to abandon the workplace are explored through a complex statistical model, namely a hierarchical structural equation model.