Latent Profile Analysis (LPA) is a person-centered statistical method based on finite mixture modeling that identifies unobserved subgroups (latent profiles) within a population using continuous indicator variables. LPA assumes that the observed data arise from a mixture of K distinct subpopulations, each characterized by a unique pattern of means across the indicators. Each individual receives a posterior probability of belonging to each profile, and the optimal number of profiles is determined by comparing model fit indices (e.g., BIC, BLRT) alongside theoretical interpretability.

Sample Paper: Wang, C. K. J., Ng, B. L. L., Liu, W. C., & Wang, L. (2017). Latent profile analysis of students' motivation and outcomes in mathematics: An organismic integration theory perspective. Heliyon, 3(6), e00308. https://doi.org/10.1016/j.heliyon.2017.e00308