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graded response model

Member Training: Item Response Theory and Rasch Models

by David Lillis  Leave a Comment

Item Response Theory (IRT) refers to a family of statistical models for evaluating the design and scoring of psychometric tests, assessments and surveys. It is used on assessments in psychology, psychometrics, education, health studies, marketing, economics and social sciences — assessments that involve categorical items (e.g., Likert items).

In this webinar, you will learn about:

  1. The key ideas and techniques of IRT, with examples from educational assessment
  2. The Rasch Model and the Graded Response Model — two of the most commonly-used IRT models
  3. A range of analytic techniques that should be used in conjunction with IRT

This webinar will introduce you to the basic ideas and applications of IRT, and show how you can acquire the skills necessary to conduct IRT analysis at the professional level.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
Not a Member? Join!

About the Instructor

David LillisDavid Lillis is an applied statistician in Wellington, New Zealand.

His company, Sigma Statistics and Research Limited, provides online instruction, face-to-face workshops on R, and coding services in R.

David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: graded response model, item response theory, rasch model

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