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.
Because of the unpredictable situation regarding Covid, we have decided to offer the course online in February 2022. This is because there is a lot of interest from foreign students and it is not yet known whether students will be able to travel freely.
To make it possible for students from all over the world to follow the course, we offer the tutorials and practicals at two different times. You can choose which dates / times suits you best and register 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, but you will be placed on a waiting list. We will contact you as soon as a place becomes available. At that time you can still decide whether you want to participate in the course.
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 behavioral research and who deal with the development, evaluation, and interpretation of health measurements using IRT.
Attendants are expected to have at least basic knowledge of epidemiological and statistical methods. We will be using R during practicals; hence familiarity with the basic R operations is required. We will provide example R files to illustrate the level of familiarity we expect, as well as self-study resources for you to develop this familiarity.
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.
To be able to do the computer practicals of this course you will need R and R studio on your computer or laptop. R and R studio can be downloaded for free from the internet: https://cran.r-project.org/
Students participating in the course as part of the Master’s programme Epidemiology need to pass the exam in order to complete the course.
Students not participating in Master’s programme Epidemiology who sign up for this course as a separate / single course can optionally register for the exam. The examination fee is € 150 per registration.
You can register for the exam via the website: Exams. 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. Only contact hours are stated on this certificate.
Only for Dutch medical specialists!
If you wish to be considered for accreditation points by the KNMG , you must sign the attendance list on the last day of the course.
To qualify for the accreditation points, you must have been present the whole course.
Epidemiology and Data Science, Amsterdam UMC