skills.rest
xjtulyc
xjtulycCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

psychometrics

Run end-to-end psychometric validation on questionnaire item response datasets.

33|6|Updated Mar 17, 2026
Search Tags:#python#reliability#psychometrics#factor analysis#DIF#measurement invariance#item response theory
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill psychometrics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: psychometrics
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/11-psychology/psychometrics
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill psychometrics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas>=2.0.0, numpy>=1.24.0, scipy>=1.10.0, factor_analyzer>=0.5.0, semopy>=2.3.0, matplotlib>=3.7.0, scikit-learn>=1.3.0.

What problem does it solve?

This Skill helps you validate and refine a psychological questionnaire by quantifying item quality, reliability, factor structure, and latent trait behavior so your scale measures what it claims to measure.

Core Features & Use Cases

  • Classical Test Theory diagnostics: computes item difficulty proxies, skewness/kurtosis, corrected item-total correlations, and alpha-if-deleted to identify weak or misfitting items.
  • Reliability estimation: calculates Cronbach’s alpha with a bootstrapped confidence interval and common interpretive guidance.
  • Factor analysis workflow: runs EFA with KMO/Bartlett tests and parallel-analysis factor retention, then performs CFA with semopy to check model fit.
  • Latent trait modeling (IRT): estimates a 2PL item response model for discrimination and difficulty parameters and supports DIF-oriented interpretation.
  • Differential Item Functioning (DIF): flags potential DIF using Mantel–Haenszel testing with Bonferroni control.
  • Measurement invariance testing: tests configural/metric/scalar invariance across groups using CFA comparisons.

Use Case: You have a draft 20-item scale administered to 500 respondents, and you need to (1) drop problematic items, (2) confirm the intended factor structure, (3) check whether reliability is acceptable, (4) model item difficulty/discrimination, and (5) test whether the scale behaves similarly across demographic groups.

Quick Start

Ask the AI to run a complete psychometric validation pipeline on your item responses by computing CTT item statistics, Cronbach’s alpha, EFA factor retention, CFA fit indices, 2PL IRT parameters, and Mantel–Haenszel DIF flags from a pandas DataFrame.

Frequently Asked Questions about psychometrics

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

FAQPage Schema
How do I validate a questionnaire scale and check item quality using Python?▼

Questionnaire scale validation in Python computes Classical Test Theory item diagnostics, reliability, factor structure, and latent trait behavior to ensure your scale measures what it claims to measure.

How do I perform factor analysis with EFA and CFA on Likert scale data?▼

Factor analysis on Likert scale data runs exploratory factor analysis with KMO/Bartlett tests and parallel-analysis factor retention, then performs confirmatory factor analysis using semopy to check model fit.

Can I estimate item response theory 2PL parameters to model item difficulty and discrimination?▼

Item response theory 2PL modeling estimates discrimination and difficulty parameters for questionnaire items and supports DIF-oriented interpretation for latent trait analysis.

How do I test measurement invariance and detect differential item functioning across demographic groups?▼

Measurement invariance testing checks configural, metric, and scalar invariance across groups using CFA comparisons, while DIF detection flags items using Mantel-Haenszel testing with Bonferroni control.

Does this psychometric validation workflow require specific Python dependencies like pandas and semopy?▼

Psychometric validation requires pandas, numpy, scipy, factor_analyzer, semopy, matplotlib, and scikit-learn to compute fit and diagnostic statistics from YAML-defined parameters and Python data structures.

Why does Cronbach's alpha drop when I remove specific items from my questionnaire?▼

Cronbach's alpha calculations include alpha-if-deleted statistics to identify weak or misfitting items, meaning dropping problematic items with low corrected item-total correlations can improve overall scale reliability.

Related Skills

Explore More Education & Research Skills→
stahurastahura
19

sql-query

Execute raw SQL queries against dataset aliases and parse columnar responses into row objects.

Community
Intermediate
xjtulycxjtulyc
33

irt-psychometrics-edu

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

Community
Advanced
rilicalrilical

publication-plots

Convert saved run artifacts into publication-ready plots and metric summaries.

Community
Intermediate
shaitamam-80shaitamam-80

meta-analysis

Plan meta-analyses with effect-size selection, heterogeneity assessment, and R code templates.

Community
Intermediate