Enrico Gori is a researcher and data analyst with deep expertise in the Rasch model, a probabilistic framework for measurement grounded in the principles of specific objectivity and fundamental measurement. His work focuses on constructing and validating instruments that allow for meaningful comparisons between individuals and items, independent of the specific sample or test form used. Unlike other statistical models, the Rasch model uniquely ensures specific objectivity—the property that comparisons between persons are independent of the particular items administered, and vice versa. Enrico Gori is particularly interested in this foundational feature, which makes the Rasch model especially powerful and suitable for applications requiring invariant and interpretable measures. He has applied the Rasch model across a variety of fields, including education, psychometrics, health measurement, and social sciences, contributing to the development of robust tools for assessment, evaluation, and research. His work includes detecting item bias, constructing scales that measure unidimensional constructs, and supporting the use of Rasch-based instruments in both theoretical and applied settings. Enrico Gori also provides training and methodological support to researchers and institutions, promoting the use of the Rasch model as a rigorous and principled approach to measurement in complex real-world contexts.