Introduction to measureR

In measureR: Tools for Educational and Psychological Measurement

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.

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Installation

install.packages("measureR")
library(measureR)

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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.

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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.

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Once inside the GUI:

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

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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.

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Citation

Please cite this package as:

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

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Session Info

sessionInfo()

measureR documentation built on May 15, 2026, 9:06 a.m.