A frequently employed concept in educational measurement is the idea of differential item functioning (DIF), which is used in tests like the Programme for International Student Assessment (PISA). When test items respond differently for several groups of test takers, even though those test takers have similar levels of the underlying construct being measured, this phenomenon is known as DIF. In this article, we’ll discuss the DIF idea in relation to PISA and how it affects assessment fairness and equity.
Differential item functioning analysis is essential in PISA for spotting potential biases in test items and guaranteeing the fairness of the assessment across various student groups. Researchers and policymakers can examine if certain groups such as students from diverse cultural backgrounds or men and women have an advantage or disadvantage in reacting to particular items by looking for objects that demonstrate DIF. In order to guarantee that the assessment appropriately depicts students’ abilities and that the findings are valid and reliable, this information is crucial.
The selection of the reference group and the focal group is the first step in the PISA DIF analysis. The target group consists of students from a separate subgroup who will be contrasted to the reference group, while the reference group is made up of children who serve as the baseline for comparison. Gender, socioeconomic status and cultural or language origins are typical PISA subgroups for comparison.
The Mantel-Haenszel (MH) method and logistic regression are the two most popular statistical techniques for identifying differential item functioning. The MH technique analyses the likelihood of passing each item between the reference and focal groups while accounting for the students’ overall aptitude. On the other hand, based on the examinee’s aptitude and group membership, logistic regression makes an estimate of the likelihood of success on a given item.
Once DIF has been identified, it is crucial to properly evaluate the findings. DIF shows potential disparities in item functioning between groups rather than necessarily indicating bias or unfairness in the assessment. The root causes of DIF must be identified through additional research. Different educational experiences for each group, cultural or linguistic variances and item content that is more recognisable to one group than the other are also potential answers.
Tackling DIF entails various processes. The DIF items must first be selected, and their influence on test results must then be assessed. If the effect is significant, changes can be made to the scoring or equating procedures to take into account the different ways the items work. Also, DIF items should be revised or replaced to guarantee that they are impartial and fair to all student groups.
Policymakers and educators can learn more about the fairness and equity of the PISA evaluation by undertaking DIF analysis. DIF analysis assists to the improvement of item quality and fairness by assisting in the identification of potential biases in test items. In order to ensure that the evaluation appropriately reflects students’ abilities, regardless of their background factors, it allows for alterations in the scoring procedures. Additionally, by taking into account the potential impact of item functioning on group comparisons, DIF analysis enhances the interpretation and presentation of PISA data.
It’s crucial to remember that DIF analysis has its limits. The accuracy and representativeness of the data are crucial for DIF detection. Limited sample sizes within particular groups may reduce the ability to precisely detect DIF. Also, as statistical significance may not necessarily translate into practical importance, it is important to exercise caution when interpreting DIF results. While analysing and responding to DIF findings, contextual elements and substantive considerations must be taken into account.
In order to ensure fair and equal assessment processes, DIF analysis in PISA is essential. DIF analysis aids in the improvement of assessment tools and scoring processes to improve fairness and validity by identifying potential biases in test questions. DIF analysis helps to improve the PISA evaluation over time by fostering a more inclusive and precise knowledge of student performance across various subgroups through careful interpretation and the right actions.
