Latentisti

Latentisti are scholars and practitioners who focus on latent constructs—unobserved factors inferred from observed data. The term comes from latent and the Italian suffix -isti, used to denote adherents or practitioners. In English-language discourse the label is rare; the concept is usually described through terms such as latent-variable researchers or psychometricians.

Latent variable modeling has roots in early 20th-century factor analysis and has expanded with developments in item response theory, structural equation modeling, and probabilistic graphical models. Latentisti work across disciplines including psychology, education, sociology, marketing, medicine, and data science, aiming to identify underlying dimensions that cannot be measured directly, such as intelligence, motivation, or consumer preference.

Typical methods employed include exploratory and confirmatory factor analysis, item response theory, latent class analysis, latent growth modeling, and structural equation modeling. Bayesian approaches and machine learning techniques are increasingly incorporated to handle complex latent structures and large datasets.

Challenges in latentist work include identifiability and model invariance, the risk of over- or under-extracting factors, interpretation of latent variables, and ensuring measurement equivalence across groups and cultures. Data quality, sample size, and model misspecification also affect reliability and validity.

See also: latent variable, factor analysis, structural equation modeling, item response theory, latent class analysis.