Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Matthew Diemer, Ph.D.

Item Response Theory (IRT) methods are best known for their use in large-scale achievement testing, such as (in the U.S.) the SAT and GRE. However, IRT methods have unique—and overlooked—advantages for improving measurement in the social sciences, psychology, education, medicine, business, marketing, and other fields. For example, IRT illuminates how precise a measure is across people with low, medium, or high levels of the underlying construct of interest. (In contrast, factor analytic approaches assume an item, subscale, or measure is equally precise for everyone.) Further, IRT is unique in allowing researchers to identify redundant and/or low-information items, making it possible to streamline longer scales to yield more efficient short-form measures.

This workshop provides a hands-on introduction to IRT. It minimizes notation and jargon and instead emphasizes conceptual understanding and application, with a particular emphasis on figures—the “visual language” of IRT. The Graded Response Model, which is designed for analyses of Likert-type items (e.g., strongly disagree to strongly agree), will be the primary focus.

Each day will be roughly divided between lecture and guided analytic practice aimed at learning to apply course concepts. Participants are encouraged to bring their own data to use during the ‘hands on’ portion of each session. Alternatively, participants will be provided with a dataset and analytic problems to work on.