irt-psychometrics-edu

Estimate 2PL/3PL IRT item parameters and compute ICC, IIF, TIF, and SEM.

33|6|Updated Mar 17, 2026

Search Tags:#psychometrics #model calibration #irt #item response theory #differential item functioning #test equating #educational measurement

Ready to run

npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill irt-psychometrics-edu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: irt-psychometrics-edu
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/20-education/irt-psychometrics-edu
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill irt-psychometrics-edu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you build and validate educational measurement models by estimating IRT parameters, quantifying measurement precision, and flagging items that behave differently across groups.

Core Features & Use Cases

Fit IRT (2PL/3PL) models: implement item characteristic curves (ICC) and item information functions (IIF) for ability testing.
Quantify measurement quality: compute test information functions (TIF) and derive standard error of measurement (SEM) across the ability scale.
Detect differential item functioning (DIF): use Mantel-Haenszel DIF detection with DIF category thresholds and p-values.
Understand test equating (Stocking-Lord): support concepts needed to convert scores across different test forms.

Quick Start

Use the irt-psychometrics-edu Skill to calibrate 2PL/3PL item parameters from a binary response matrix, compute ICC/IIF/TIF/SEM, and run Mantel-Haenszel DIF screening on selected demographic groups.

Frequently Asked Questions about irt-psychometrics-edu

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I estimate item parameters using 2PL or 3PL item response theory?▼

You can estimate item parameters using 2PL or 3PL item response theory by fitting logistic IRT probability functions to a binary response matrix. This Skill calculates discrimination, difficulty, and guessing parameters to generate item characteristic curves for ability testing.

What is Mantel-Haenszel DIF detection and how does it identify biased test items?▼

Mantel-Haenszel DIF detection identifies biased test items by computing stratified odds-ratio statistics on response and group arrays. This Skill classifies differential item functioning using category thresholds and p-values to screen for group comparability issues.

How do I compute test information functions and standard error of measurement in IRT?▼

To compute test information functions and standard error of measurement in IRT, this Skill aggregates item information functions across the ability scale. It derives measurement precision metrics by applying information and SEM formulas to the calibrated IRT parameters.

Can I use this Skill for test equating across different test forms?▼

Yes, you can use this Skill for test equating across different test forms. It supports the Stocking-Lord concepts needed to convert scores between different test forms after you calibrate the 2PL or 3PL item parameters from your response data.

What data format do I need for IRT model calibration and DIF screening?▼

For IRT model calibration and DIF screening, you need a binary response matrix of test scores alongside demographic group arrays. The Skill processes these response and group arrays to estimate parameters and perform Mantel-Haenszel DIF classification.

When should I use 3PL over 2PL models for educational measurement?▼

You should use 3PL over 2PL models for educational measurement when guessing behavior is expected, as the 3PL model estimates an additional guessing parameter. This Skill implements both logistic IRT probability functions to fit your item characteristic curves accordingly.