Project List

Bayesian structural equation modeling (BSEM) is receiving increasing interest primarily due to its ability to address some of the issues found in the mainstream frequentist approach (e.g., nonconvergence, Heywood cases, sample size limitations, and inadmissible solutions). Additionally, BSEM allows for the fitting of complex models that classical maximum likelihood methods might struggle to handle. A critical component of any Bayesian analysis is the prior distribution of the unknown model parameters. A key distinction between Bayesian structural equation modeling and frequentist structural equation modeling is the use of priors. Researchers may be skeptical about the subjectivity of prior distributions and their impact on Bayesian modeling. However, priors are a significant advantage of using Bayesian statistics, as they allow previously known information to be transparently and directly included in the model specification. Proper prior elicitation is essential for translating knowledge and judgment about a phenomenon into a probability distribution. Priors allow for the quantification of uncertainty and encapsulate available knowledge about the parameters before observing the data. There are several ways to translate prior knowledge into distribution parameters. Results show that researchers tend to rely on weakly informative priors (i.e., small-variance priors). However, prior elicitation in Bayesian structural equation modeling still has a long way to go in terms of development and widespread adoption.

Traditional psychometric approaches often rely on frequentist models, which can be limited in handling uncertainty and incorporating prior knowledge. By adopting a Bayesian framework, this project aims to enhance psychometric analysis through more flexible, probabilistic modeling techniques that allow for dynamic updates as new data becomes available. The project focuses on the creation and adaptation of psychometric instruments for diverse populations. Advanced computational techniques, including Markov Chain Monte Carlo (MCMC) using the No-U-Turn Sampler (NUTS), are employed for latent model estimation. The Bayesian approach provides a deeper understanding of measurement precision and contributes to the development of psychometric instruments with strong validity evidence.

The Xpanding Innovative Alliance (XiA) project is dedicated to advancing interoperability within the healthcare sector, particularly in anticipation of the European Health Data Space (EHDS) regulation. Through a comprehensive educational initiative, XiA aims to address the skills gap in advanced digital health interoperability standards among healthcare providers, digital health solution providers, and individuals. By developing high-quality educational materials and courses, XiA seeks to equip stakeholders with the necessary skills to embrace EHDS-related standards and foster a culture of interoperability. Utilizing a multi-disciplinary approach, the project will offer online educational content and immersive learning experiences tailored to the needs of healthcare professionals and organizations. This initiative will not only educate a large workforce in advanced interoperability standards but also establish partnerships with other institutions to amplify its impact. The primary objectives of XiA include developing personalized learning pathways, accrediting educational initiatives, and promoting the integration of digital transformation, interoperability, and cybersecurity skills. Through micro-credentialing and partnerships with academic networks, XiA aims to ensure the sustainability and scalability of its educational programs. By fostering cross-border collaboration and engaging external entities, XiA seeks to empower healthcare providers, enhance the competitiveness of digital health companies, and strengthen the skills of EU health professionals. Through an open approach to interoperability standards and education, XiA aims to sustain its efforts and drive lasting impact in the field of digital healthcare.

This project seeks to develop a digital ecosystem that allows interaction between various stakeholders (companies, candidates, recruiters). It's a platform that can provide companies with various responses in a gamified way. Among the various offerings are assessment and identification of the profile of candidates and employees in companies. The same platform can help with diagnostic processes and organizational change, using decision algorithms and big data management using machine learning processes to reduce the risk of the decision.

Teacher burnout is a significant psychosocial, educational, organizational & economic challenge worldwide that has been aggravated by the COVID-19 pandemic. Impacts have been reported on teachers’ occupational health/wellbeing, job satisfaction, and performance, as well as on the quality of the learning environments. Hence, contributions to prevent teacher burnout have been receiving more attention by policy makers globally, as investing in occupational health can have a return on investment of 5 times the investment, and teaching is becoming unattractive with many OECD countries facing a shortage of new teachers and high turnover rates. Yet, an emphasis has been placed on some individual-level variables (as optimism), leaving the impact of other individual (e.g., self-care) & contextual variables (as job resources, e.g., leadership practices) overlooked. This project intends to investigate teachers’ needs regarding personal & job resources and whether teachers’ and the leadership team’s perceptions of personal/job resources needs are concordant. It also investigates how personal and/or job resources relate with teachers’ occupational health/wellbeing. Lastly, it proposes to investigate how a digital platform based on a Social-Emotional Learning (SEL) approach for teachers and their leaders, can foster teacher wellbeing and performance. Study 1 employs a sequential explanatory design to assess the perceptions of teachers and leaders regarding personal and job resources. Participants will complete a survey, followed by focus group discussions with teachers and individual interviews with leaders to enrich the quantitative data. Study 2 adopts a quantitative design with teachers to examine the relationship between personal and job resources and teachers' occupational health and wellbeing. Study 3 involves developing and testing the #SELhub digital platform to promote a positive school culture and enhance teacher wellbeing and performance, through an experim...