Clinical measurement of latent constructs (e.g. quality of life, pain, depression) often requires use of questionnaires. Measurement of such constructs using questionnaires relies on statistical measurement models. The Item Response Theory (IRT) provides statistical models which link the latent construct score of a patient to the questionnaire responses of the patient. IRT models are the current golden standard in this context, compared to the Classical Test Theory (CTT) model where the latent scores are linked to responses by sum-scores.
This is an online course of 5 half days!
We offer the tutorials and practicals at two different times (morning and afternoon), enabling students from all over the world to attend the course. You can choose your preferred time after registering for this course.
The course consists of alternating series of lectures, computer practicals, and working groups. Lectures will introduce the theoretical issues in IRT, whereas practicals and working groups will focus on answering questions and applying the theoretical understanding on example datasets and interpretation of published papers.
If a course is [Full], you can still register, and we will place you on a waiting list. We will contact you as soon as a place becomes available. You can then decide whether you still want to participate.
In medicine and health sciences we often measure constructs that are not directly observable, such as quality of life, pain, or depression. Measurement of such constructs requires use of questionnaires, which validity and reliability is assessed using statistical measurement models. The Item Response Theory (IRT) provides statistical models which model the relationship between the construct and the questionnaire responses. IRT models are increasingly being used in addition to the Classical Test Theory (CTT) model. Both models have different assumptions and the analyses can complement each other. CTT, for example, focuses more on the validity and reliability of sum scores, while IRT focuses more on the validity and reliability of individual items in a questionnaire. In addition, IRT has several advantages over CTT.
An important practical advantage of the IRT based measurement instruments is the flexibility for use in research and clinical practice. For instance, IRT models can be used to create short form questionnaires tailored for specific target groups. Furthermore, a computerized adaptive test (CAT) can be developed which selects the most informative questions for each individual during the administration, based on the previous responses of the individual.
This course is an introductory course on IRT with a focus on its use in the development and validation of questionnaires used in medical and health sciences. We focus on the conceptual understanding of IRT and less on the statistical details. We will cover the underlying principles of IRT and the conceptual differences between IRT and CTT. Then, we will study the theoretical pillars of IRT (e.g. the concept of latent trait, statistical modeling of items and responses, assumptions of IRT). Once the ground is set, we will move on to the practical issues in applications of IRT, such as checking the model assumptions, evaluating the model fit and estimating reliability of measurements. Finally, we will focus on more advanced issues such as differential item functioning, the principles behind item banking and computerized adaptive testing. As an example we will be using the Patient-Reported Outcomes Measurement Information System (PROMIS), as the items included in these questionnaires were selected under the IRT model and are widely used in clinical settings.
After attending the course, students will have an understanding of the item response theory (IRT) in the context of medical measurement. Practically, the participant will be able to evaluate a scientific article which uses IRT and perform IRT analyses.
These main goals are considered to be the sum of the following sub goals:
The course is designed for healthcare practitioners and researchers who are active in medical, allied health, psychological, or behavioural research and who deal with the development, evaluation, and interpretation of health measurements using IRT.
Participants are expected:
If you participate in the course, an instruction video will be available a few weeks before the starting date.
The course materials (lectures, assignments, feedback of the assignments etc) are available on Canvas, our digital learning environment. The documents will remain available on Canvas for at least one year.
In order to do the computer practicals, you will need R and R studio, which you can download for free at https://cran.r-project.org/.
Epidemiology Master’s students are required to do the exam in order to complete this course. For other students (not enrolled in Epidemiology Master), the exam is optional and costs €160,- per registration.
Students who complete the exam with a pass grade will receive an exam statement mentioning the academic credits (ECs).
You can register for the exam on the Exams page. Registration will close 3 weeks prior to the exam.
Please note that you need to pass the exam in order to receive credits (EC).
A certificate of participation will be granted to all students who have attended at least 80% of the classes. The number of contact hours will be stated on the certificate.
Only for Dutch medical specialists!
On the final day of the course, you need to sign the attendance list if you wish to obtain KNMG accreditation credits (PE-points).
To qualify for these credits, there is an attendance requirement of 100%.
Professor of Outcome Measurement in Healthcare, Amsterdam UMC
Epidemiology and Data Science, Amsterdam UMC
