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.

One of the main challenges of this project is the computational complexity involved in implementing advanced statistical tools, such as MCMC and NUTS. These techniques require significant computational power and a deep understanding of statistical methodologies, making them difficult to apply in resource-limited contexts. Another challenge is the use of appropriate a priori distributions, especially when dealing with understudied populations. Additionally, there is a need to ensure that the developed instruments are culturally appropriate in different contexts, which requires continuous effort in adapting psychometric instruments.

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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.