Introduction to projectLSA
Overview
projectLSA provides a unified Shiny-based environment
for conducting Latent Structure Analysis (LSA),
including:
- Latent Profile Analysis (LPA)
- Latent Class Analysis (LCA)
- Latent Trait Analysis (LTA / IRT)
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis (CFA)
The package is designed for users who prefer a graphical workflow
without writing code, while still leveraging robust statistical
methodologies implemented in well-established R packages.
Installation
install.packages("projectLSA") # CRAN version (once released)
library(projectLSA)
Launching the Application
library(projectLSA)
run_projectLSA()
This will open the full Shiny interface, where you can upload data,
choose an analysis module, and generate results.
Modules Included
1. Latent Profile Analysis (LPA)
- Fit multiple models with varying profile numbers
- Compare AIC, BIC, entropy, and class sizes
- Visualize best model with custom profile names
2. Latent Class Analysis (LCA)
- Designed for categorical indicators
- Simulated and uploaded datasets supported
- Class probability tables and interactive visualizations
3. Latent Trait Analysis (LTA / IRT)
- Supports dichotomous and polytomous items
- Rasch / 2PL / 3PL and graded models
- Item information curves, test information, and multi-dimensional
visualizations
4. Exploratory Factor Analysis (EFA)
- KMO test, Bartlett test, and parallel analysis
- Loading matrices, rotation, and factor scores
- Auto-generated interpretation summaries
5. Confirmatory Factor Analysis (CFA)
- Lavaan syntax editor
- Fit indices, loadings, modification indices
- SEM path diagram with customizable styles
Example Workflow
Below is a simple workflow using the built-in datasets.
library(projectLSA)
# Launch the GUI
run_projectLSA()
Once inside the GUI:
- Choose a module (e.g., LPA)
- Upload your dataset or select a built-in dataset
- Choose variables and model settings
- Fit the models and explore the outputs
Built-in Example Datasets
The package includes several example datasets:
- pisaUSA15 — student motivation indicators
- curry_mac — moral relevance & judgment
(simulated)
- id_edu — longitudinal educational identity
(simulated)
These are accessible from within the Shiny interface.
Reproducibility and Reporting
projectLSA provides:
- Exportable tables (CSV, Excel)
- Downloadable graphics (PNG)
- Reproducible summaries and model comparisons
This ensures results produced through the GUI can be published or
documented with confidence.
Citation
Please cite this package as:
Djidu, H., Retnawati, H., Hadi, S., & Haryanto (2025).
projectLSA: An R Shiny application for latent structure analysis
with a graphical user interface.