Welcome to DiscrimHub

Your professional platform for advanced item discrimination analysis.

What type of data do you want to analyze?

AI Integration Setup

Select your preferred AI provider and enter your API key to unlock AI-powered insights.


Privacy Note: Your key is stored ONLY in your browser session. We do not save or transmit it to any external server other than the selected AI provider's API.

Key Features

Dichotomous

Analyze 1-0 binary data with Brennan's Index, Classic DI, and IRT parameters.

Also supports nominal data (ABCD) with automatic conversion (answer key required).

Polytomous

Work with Likert scales using the novel Slope Coefficient and correlation indices.

Mixed Format

Seamlessly analyze exams containing both multiple-choice and open-ended questions.

AI-Powered Capabilities

Smart Insights

Get instant, context-aware interpretations of your psychometric results.

Multilingual

Ask questions and receive detailed explanations in any language you prefer.

Academic Reports

Generate results formatted for academic papers with automatic APA 7 citations.



1. Data & Settings


View Example Data Format

Parameter Distributions

Upload a file containing the correct answers (A, B, C, etc.) in a single row. Order must match data.

Analysis Parameters




Download Data (CSV)

Download Scored Data (0/1 CSV)

1. Data & Settings


View Example Data Format

Parameter Distributions

Analysis Parameters

Standard Item Analysis (Slope, Item-Rest Correlation)




Download Data (CSV)

1. Data & Settings


View Example Data Format

Remaining items will be Polytomous.
Parameter Distributions



Analysis Parameters




Download Data (CSV)

What is DiscrimHub?

DiscrimHub is an advanced, open-source psychometric analysis platform designed to bridge the gap between complex statistical theories and practical educational measurement. It provides educators and researchers with robust tools to evaluate item quality, test reliability, and student performance across various data types.

What DiscrimHub Can Do

  • Analyze Exam Data: Process 1-0 (Dichotomous), Likert (Polytomous), or Mixed format datasets effortlessly.
  • Evaluate Item Quality: Calculate classic indicators like Difference Index (DI), Point-Biserial Correlation, and Item Response Theory (IRT) parameters.
  • Adaptive Reliability: Switch between Cronbach's Alpha and McDonald's Omega for accurate reliability estimation.
  • Generate Reports: Produce downloadable, academic-style HTML reports with automated interpretations.
  • Educational Simulation: Create synthetic datasets to demonstrate psychometric concepts.

Development Team

Academic References

The methodologies implemented in this application are based on the following literature:

AERA, APA, & NCME. (2014). Standards for educational and psychological testing. American Educational Research Association.
Arikan, S., & Aybek, E. C. (2022). A special case of Brennan's index for tests that aim to select a limited number of students: A Monte Carlo simulation study. Educational Measurement: Issues and Practice, 41 (4), 35-49. https://doi.org/10.1111/emip.12528
Brennan, R. L. (1972). A generalized upper-lower item discrimination index. Educational and Psychological Measurement, 32 (2), 289-303. https://doi.org/10.1177/001316447203200206
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48 (6), 1-29.
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., & Borges, B. (2024). shiny: Web Application Framework for R. R package. https://CRAN.R-project.org/package=shiny
Crocker, L. M., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16 (3), 297-334.
Çelen, Ü., & Aybek, E. C. (2022). A novel approach for calculating the item discrimination for Likert type of scales. International Journal of Assessment Tools in Education, 9 (3), 772-786. https://doi.org/10.21449/ijate.1173356
Ebel, R. L. (1965). Measuring educational achievement. Prentice-Hall.
Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30 (1), 17-24.
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
Revelle, W. (2024). psych: Procedures for psychological, psychometric, and personality research. R package. https://CRAN.R-project.org/package=psych
Revelle, W., & Condon, D. M. (2019). Reliability from alpha to omega: A tutorial. Psychological Assessment, 31 (12), 1395-1411.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34 (4, Pt. 2), 1-97.

How to Cite This App

Dogac, A., Aybek, E. C., Arikan, S., & Coskun, S. (2024). DiscrimHub: A Comprehensive Item Analysis Platform [Shiny Application]. Hacettepe University.

DiscrimHub AI Assistant

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