Introduction

In the rapidly evolving field of educational measurement, health assessment, and psychological evaluation, Item Response Theory (IRT) has emerged as a powerful statistical framework. Item Response Theory (IRT) for Psychometrics Training Course is designed to provide professionals and researchers with advanced analytical skills and a deep understanding of IRT models, application strategies, and software integration to drive evidence-based decisions in test development and psychometric analysis. Through data-driven case studies, participants will gain hands-on experience in interpreting item-level responses, estimating latent traits, and implementing IRT in both classical and contemporary testing environments.

This course focuses on latent trait modeling, test reliability, differential item functioning (DIF), and test equating using IRT. It integrates cutting-edge tools such as R, Python, and commercial software like IRTPRO and Mplus to prepare learners for real-world psychometric challenges. Whether you're developing psychological scales, educational assessments, or health outcome measures, this course will strengthen your capacity to apply IRT principles for measurement accuracy, fairness, and predictive validity.

Programme Curriculum

Item Response Theory (IRT) for Psychometrics Training Course

Introduction

Course Objectives

Understand the fundamental concepts of Item Response Theory and its evolution.
Differentiate between Classical Test Theory (CTT) and IRT in psychometric analysis.
Apply the 1PL, 2PL, and 3PL IRT models for test item calibration.
Evaluate model fit using statistical indices and visual diagnostics.
Analyze item characteristic curves (ICCs) and information functions.
Conduct Differential Item Functioning (DIF) analysis using IRT methods.
Perform computerized adaptive testing (CAT) using IRT algorithms.
Utilize IRT in scale development for educational and psychological testing.
Implement IRT models using R packages (e.g., ltm, mirt, TAM) and Python libraries.
Design test equating and linking strategies using IRT.
Conduct person fit analysis and interpret reliability estimates.
Use IRT in validation and refinement of patient-reported outcome measures.
Integrate machine learning with IRT models for predictive psychometrics.

Target Audience

Psychometricians
Educational Measurement Experts
Clinical Researchers
Assessment Developers
Data Scientists in Education/Health
Graduate Students in Psychology/Education
Test Developers and Analysts
Government and NGO Monitoring & Evaluation Specialists

Course Duration: 5 Days

Course Modules

Module 1: Foundations of IRT and Psychometrics

History and principles of Item Response Theory
Key differences between IRT and Classical Test Theory
Assumptions and prerequisites of IRT
Overview of latent traits and item characteristics
Key terminology: discrimination, difficulty, guessing
Case Study: Comparing CTT vs IRT in a school-based assessment

Module 2: 1PL, 2PL, and 3PL IRT Models

Mathematical formulations of IRT models
Estimating item parameters using MLE and Bayesian methods
Model comparison and fit statistics
Interpretation of item characteristic curves
Choosing the right model for your data
Case Study: Fitting 1PL and 2PL models in psychological tests

Module 3: Software Tools for IRT Analysis

Introduction to R packages (mirt, ltm, TAM)
Implementing IRT in Python with pyirt
Using commercial tools: IRTPRO, Mplus, BILOG-MG
Importing and cleaning data for IRT analysis
Visualizing ICCs and test information functions
Case Study: IRT modeling of a national examination using R

Module 4: Differential Item Functioning (DIF)

Understanding measurement bias and fairness
Methods for detecting DIF: Mantel-Haenszel, logistic regression
Impact of DIF on test validity
Interpreting output from DIF analyses
Reporting DIF findings for stakeholders
Case Study: Gender-based DIF analysis in standardized testing

Module 5: Computerized Adaptive Testing (CAT)

Concepts of adaptive testing and item banks
IRT-based algorithms for item selection
CAT implementation challenges and considerations
Developing item exposure controls
Evaluating CAT performance and test length
Case Study: Simulation of a CAT for employee assessment

Module 6: Test Equating and Linking Using IRT

Need for test equating in longitudinal assessments
Anchor item strategies and linking coefficients
IRT-based equating vs traditional methods
Establishing score comparability across forms
Interpreting and reporting equated scores
Case Study: Equating multiple test forms for a national curriculum exam

Module 7: Person Fit and Test Reliability in IRT

Person fit indices: outfit and infit statistics
Estimating test reliability: marginal reliability, conditional SE
Detecting aberrant response patterns
Using IRT information for score precision
Visualizing person-item maps
Case Study: Analyzing test taker response patterns in health surveys

Module 8: Advanced Applications and Predictive IRT

Integrating IRT with machine learning models
Multidimensional IRT (MIRT) applications
Longitudinal modeling using IRT
IRT in patient-reported outcomes (PROMIS)
Real-world big data applications in psychometrics
Case Study: Predicting learner outcomes using IRT + AI analytics

Training Methodology

Instructor-led sessions with interactive lectures
Hands-on exercises using real-world datasets
Live software demonstrations (R, Python, IRTPRO)
Group discussions and peer collaboration
Case-based problem-solving sessions
Ongoing feedback and assessments throughout the course

Bottom of Form

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

IRT psychometric modeling test analysis

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000