projectLSA: A Shiny Application for Integrated Latent Structure Analysis

Area of Science:

• Psychometrics and Quantitative Psychology
• Educational Measurement
• Statistical Software Development

Background:

• Latent structure analysis methods (LPA, LCA, IRT, EFA, CFA) are crucial for modeling unobserved constructs in psychological and educational research.
• These methods often demand advanced statistical expertise and multiple software packages, posing accessibility challenges.

Purpose of the Study:

• To introduce projectLSA, an integrated Shiny-based application designed to streamline latent structure analysis.
• To provide a user-friendly, unified platform for data analysis, model specification, estimation, and visualization.

Main Methods:

• Development of a Shiny application integrating established R packages for latent structure analysis.
• Implementation of a unified workflow for procedures including LPA, LCA, IRT, EFA, and CFA.
• Inclusion of built-in simulated datasets for demonstrating practical application.

Main Results:

• projectLSA offers a single interface for uploading data, specifying models, estimating parameters, and comparing model fit.
• The application supports a consistent analytical workflow without requiring users to write code.
• Demonstrated efficient estimation, comparison, and interpretation of latent structure models.

Conclusions:

• projectLSA significantly enhances accessibility, consistency, and efficiency in latent structure analysis.
• The application reduces technical barriers, promoting interactive and reproducible research in psychometrics and education.
• projectLSA empowers researchers to conduct complex analyses with greater ease and confidence.