While creating a test system, it is essential to maintain reliability and validity throughout the process. You can use different theories or models to achieve the purpose. Item Response Theory (IRT) is one such tool based on mathematical models to understand and analyze test results.
It is a psychometric model that can be used to analyze how test takers respond to items on a test. It can estimate the difficulty of items, the test takers’ ability, and the testing process’s fairness. IRT has several advantages over other psychometric models, but it also has some disadvantages.
What is Item Response Theory?
Also known as Latent Response Theory, Item Response Theory (IRT) is the combination of different mathematical models which focus on the relationship between attributes and their performance.
The manifestation of this theory requires the existence of an instrument, audience responses, and the presence of an underlying trait (latent trait) to be measured. The latent quality is not something you can measure quantitatively by observing. It is abstract and requires the collection of relevant information through different tools.
So, measuring such a performance requires the audience to answer some questions, usually in the form of a test or questionnaire. After the application of the mathematical models, the output is the explanation of the link existing between the attributes and their performance.
What is Item Response Theory Used For?
IRT can be applied in multiple domains for ability testing purposes. Initially, it was used in psychometrics but later extended to education and medical fields. In education, this theory is used to calibrate tests and assign scores based on abilities.
Currently, popular tests like Scholastic Aptitude Test (SAT) and Graduate Record Examination (GRE) are developed using Item Response Theory.
In the medical field, health outcomes and quality of life are the major domains in which IRT is used.
Advantages of Item Response Theory
Below are the main advantages of Item Response Theory:
The Principle of Variation is Applicable
When using the Item Response Theory, one of the significant advantages is its objectivity. The attributes and the performance are not connected. Simply put, IRT ensures that the tests’ results wouldn’t depend on the particular group of people attending the test at a specific time.
In the SAT results, the measure of ability is not impacted by the individual group. The ability to measure remains constant.
The Focus is on Item Analysis
Unlike some other models where the focus is on the entire test, IRT focuses on the individual item. Each item has its own item characteristic curve (ICC curve). This curve demonstrates the probability of the correctness of the answer based on the latent trait of the tester being measured.
Reasonable Data Set Required
You don’t need a vast audience to prove the test results through this theory. A reasonable-sized audience is enough to calibrate and evaluate the theory precisely. So, even if you don’t have a large data set, you can efficiently process using IRT.
Explicit Latent Trait is Defined
Under the IRT model, an explicit latent trait is identified before the data collection process initiates. It makes the entire process straightforward for all the stakeholders involved. You won’t have to spend implicitly highlighting or figuring out the trait being measured, as might be the case in a few situations.
Ensures Valid and Reliable Data
Belonging to the mathematical family, Item Response Theory ensures the delivery of valid and reliable results. The results are valid if the process or instrument measures what exactly it is created for. Similarly, data reliability is tested when the theory measures the constructs across time, individuals, and situations.
Disadvantages of Item Response Theory
Below are the main disadvantages of Item Response Theory:
Strict Assumptions Involved
Unidimensionality and Monotonicity are the two assumptions that make it difficult for the users of Item Response Theory to implement in every situation. Monotonicity is the assumption in which it is expected that an increase in the trait level will increase the probability of the right response. In Unidimensionality, the tester believes the number of latent attributes being measured is not more than 1.
Comparatively Difficult as Compared to Other Options
The IRT technique is challenging to execute. It is not only because of the strict assumptions but also because the entire dataset is based on accurate estimates of the item parameters and model fit. It is possible but highly costly and requires substantial expertise.
Larger Sample Required as Compared to CTT
IRT requires a larger sample size than the Classical Test Theory (CTT), which makes the data collection complex. Also, it limits the tester’s ability to identify the relationship between the trait and attribute.
Why Should you Consider Item Response Theory Over Others?
One of IRT’s most significant advantages over the other theories is that it focuses on the test taker’s ability irrespective of the test items, thus helping you measure the real value. Besides, they allow local precision of the estimated scores.
In contrast, other theories like CTT rely on the population precision Standard Error of Measurement SEM, which only works with mediocre population size. A population size too big will underestimate the precision, while a small population size will result in overestimation.
Moreover, in IRT, the measurement item’s features do not depend on the representative sample. This non-attribution to the population allows testers to implement it in various scenarios.
Summing Up
Item Response Theory was developed to overcome the drawbacks of the Classical Test Theory (CTT). IRT comes with multiple positives, like measurement of the linkage between an underlying trait of the test taker and the performance and the higher reliability & validity with a reasonable population required.
Based on these advantages, testers utilize Item Response Theory in multiple fields. However, no theory is perfect. There are certain limitations of IRT, too, i.e., following strict assumptions and using a considerably reasonable population size. These limitations must be kept in consideration before applying the theory.
