Overview

measureR provides a unified Shiny-based environment for educational and psychological measurement, including:

Content Validity (CV)
Exploratory Factor Analysis (EFA)
Confirmatory Factor Analysis (CFA)
Classical Test Theory (CTT)
Item Response Theory (IRT)

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("measureR")
library(measureR)

Launching the Application

library(measureR)
run_measureR()

This will open the full Shiny interface, where you can upload data, choose an analysis module, and generate results.

Modules Included

✔ Content Validity (CV)

Aiken’s V, CVR (Lawshe), I-CVI, and S-CVI/Ave computation.
Automatic critical value comparison and interpretation badges.
Clear tabular summaries and export-ready results.

✔ Exploratory Factor Analysis (EFA)

KMO, Bartlett test, parallel analysis.
Factor extraction with rotation.
Factor scores and loading matrix export.
Clean HTML summaries for clearer interpretation.

✔ Confirmatory Factor Analysis (CFA)

Lavaan model editor.
Fit measures, loadings, factor scores.
Fully customized SEM path diagrams.

✔ Classical Test Theory (CTT)

Item difficulty and discrimination indices.
Test reliability (α), SEM, and score distribution analysis.
Distractor analysis for multiple-choice items.
Comprehensive item and test-level summary outputs.

✔ Item Response Theory (IRT)

Supports dichotomous and polytomous items.
Automatically fits Rasch, 2PL, 3PL (or PCM/GRM/GPCM).
ICC plots, test information, factor scores.
Multi-dimensional visualization with 3D surfaces and heatmaps.

Once inside the GUI:

Choose a module (e.g., IRT)
Upload your dataset or select a built-in dataset
Choose variables and model settings
Fit the models and explore the outputs

Reproducibility and Reporting

measureR 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. (2026). measureR: Tools for educational and psychological measurement. https://github.com/hdmeasure/measureR. R Packages. —

Introduction to measureR