Differential item functioning (DIF) refers to a situation where an item on a test or survey behaves differently for different groups of people, such as by gender, race, or socioeconomic status. There are several methods for detecting DIF, each with its own strengths and weaknesses. Some of the most commonly used methods include:
It's important to note that no single method is considered to be the best for detecting DIF, and the choice of method depends on the specific characteristics of the study and the population. It is recommended to consult with a statistician or psychometric expert when working with DIF analysis.
Albert Isa The Mantel-Haenszel (MH) procedure is the most often used approach in psychometrics for discovering Differential Item Functioning (DIF). The MH technique is a chi-square test that compares the performance of multiple groups (for example, males against females) on a single item. The test examines the proportion of right answers for each group and looks for statistically significant differences. If a substantial difference is discovered, the item is marked as DIF.
Item Response Theory (IRT)-based approaches such as the likelihood ratio test (LRT), the Wald test, and the t-test are very popular. Individuals' latent trait scores are used in these ways to examine the DIF. These approaches outperform the Mantel-Haenszel process in terms of robustness and accuracy.
The optimum DIF detection method is determined on the study issue and the data set. However, in the identification of DIF, IRT approaches are thought to be more powerful and efficient than traditional test theory (CTT) methods. IRT approaches give more precise information regarding the nature of DIF and are therefore more suited for large-scale assessment initiatives. Among the IRT approaches, the LRT method is thought to be the best for identifying DIF.
The LRT approach, for example, may detect DIF in terms of item difficulty and item discrimination, which can aid in determining the precise cause of DIF. This data can be utilized to improve the assessment's measurement quality.
It's vital to remember that DIF detection is only one stage in the DIF analysis process, and it's not enough to make choices regarding the item or group in issue. Further research must be undertaken to determine the effect size or magnitude of the DIF, the direction of the DIF, and the influence on the measurement quality of the assessment.
@Prof Qamar I'm very grateful sir, the contribution really added an insight to me. I appreciate your effort.
Hi Albert
Follow this link for great paper that compares different methods for identifying DIF. I use the the MIMIC as it is based on a factor analytic model with all the advantages that this brings.
chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://journals.sagepub.com/doi/pdf/10.1177/0146621605275728?casa_token=FtpI-vMdoYYAAAAA:rjOO3oTAeqUngZ6_B5iErTFAdCnzHKkI71gFq2O5cfQEmvglsUnj_OC3BAqhrDqqaogjSR7ZtE95
Mark
Although some methods are better than others, it is not possible to talk about 'the best' method/technique. It depends on your research question and your data.
> Some methods require large samples (e.g. IRT-based methods).
> Some methods may show the effect of 'item impact' as DIF (e.g. Angoff's TID).
> If you have polytomous data or more than 2 groups to compare, you should use generalized methods (e.g. poly-SIBTEST or Generalized Mantel-Haenszel)
> If you suspect non-uniform DIF in your test, you should reanalyze the data with methods likely to detect non-uniform DIF (e.g. Logistic Regression or Breslow-Day).
please i needed a guide on step by step procedure of analyzing DIF, I have never done it before and I have a large sample size of about 17000.
secondly I need a free software im using IRT based method
Albert Isa You can analyze your data with 'difR' package in R.
Article A general framework and an R package for the detection of di...
That package is a collection of methods to detect dichotomous differential item functioning.
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