To date, in gifted education and related fields various conventional factor analytic and clustering techniques have been used extensively for investigation of the underlying structure of data. Latent profile analysis is a relatively new method in the field. In this article, we provide an introduction to latent profile analysis for gifted education researchers. We briefly trace the history of this method, focusing particularly on advancements of latent class models and their advantages over traditional clustering approaches. This is followed by the overview of statistical indicators that can be used to choose an optimal model. We illustrate use of latent profile analysis in the field through a sample study on the Big Five personality types of gifted students.