Using Multiple-Variable Matching to Identify EFL Ecological Sources of Differential Item Functioning

Context is a vague notion with numerous building blocks making language test scores inferences quite convoluted. This study has made use of a model of item responding that has striven to theorize the contextual infrastructure of differential item functioning (DIF) research and help specify the sources of DIF. Two steps were taken in this research: first, to identify DIF by gender grouping via logistic regression modeling, an inventory of mostly cited DIF sources was prepared, based on which a list of demographic items was appended to the TOEFL reading paper only to be administered to the intermediate Iranian undergraduates; second, using multiple-variable matching regression (Wu & Ercikan, 2006), a built-in sequence was followed to let every potential DIF source be considered as a covariate, over and above the conditioning variable, and specify whether a particular ecological variable could reduce DIF value/status. Then, all significant variables were analyzed together to show the final DIF predictors. The same procedures, i.e., individual/collective analyses, were employed after the purification of the test. The results indicated three ecological predictors affecting DIF before and after purification: income, administration convenience, and SES. The ultimate predictors helped create an EFL configuration of the ecological model of item responding. The implications for validity arguments are also discussed.