projectLSA: A Shiny Application for Integrated Latent Structure Analysis

Latent structure analysis methods, including latent profile analysis (LPA), latent class analysis (LCA), item response theory (IRT), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), are widely used in psychological and educational research to model unobserved constructs and identify heterogeneity across individuals. However, applying these methods often requires advanced statistical expertise and the use of multiple specialized software packages with different workflows, which can limit accessibility and increase analytical complexity. This paper introduces projectLSA, a Shiny-based application designed to provide an integrated and user-friendly platform for conducting latent structure analyses. The application enables users to upload data, specify models, estimate parameters, compare model fit using standard indices, and visualize results within a single interface. By integrating several established R packages, projectLSA supports a unified analytical workflow without requiring users to write code. The practical utility of the application is illustrated using built-in simulated datasets that support multiple analytical procedures, including LPA, LCA, IRT, EFA, and CFA. These examples demonstrate how users can estimate, compare, and interpret models efficiently within a consistent workflow. Overall, projectLSA enhances accessibility, consistency, and efficiency in latent structure analysis by reducing technical barriers and supporting interactive and reproducible data analysis.