There is a great need in health outcomes research to develop instruments that accurately measure a person's health status with minimal response burden. This need for psychometrically sound and clinically meaningful measures calls for better analytical tools beyond the methods available from traditional measurement theory. Applications of item response theory (IRT) modeling have increased considerably because of its utility for instrument development and evaluation, assessment of measurement equivalence, instrument linking, and computerized adaptive testing. IRT models the relationship, in probabilistic terms, between a person's response to a survey question and their standing on a health construct such as fatigue or depression. This information allows instrument developers to develop reliable and efficient quality of life measures tailored for an individual or group. This introductory course will discuss the basics of IRT models and applications of these models to improve health outcomes measurement. Illustrations that focus on measuring key health-related quality of life domains in different disease populations will be used throughout the presentation. The NIH Patient-Reported Outcomes Measurement Information System (PROMIS) project will also be discussed for its relevance for assessing patient-reported outcomes using modern psychometric methods. This course is designed for those with little to no experience with IRT.