Investigating the Distractors to Explain DIF Effects Across Gender in Large-Scale Tests With Non-Linear Logistic Regression Models

Abstract

The purpose of this study is to examine the distractors of items that exhibit differential item functioning (DIF) across gender to explain the possible sources of DIF in the context of large-scale tests. To this end, two non-linear logistic regression (NLR) models-based DIF methods (three parameters, 3PL-NLR and four-parameter, 4PL-NLR) were first used to detect DIF items, and the Mantel-Haenszel Delta (MH-Delta) DIF method was used to calculate the DIF effect size for each DIF item. Then, the multinomial log-linear regression (MLR) model and 2-PL nested logit model (2PL-NLM) were applied to items exhibiting DIF with moderate and large DIF effect sizes. The ultimate goals are (a) to examine behaviors of distractors across gender and (b) to investigate if distractors have any impact on DIF effects. DIF results of the Art Section of the General Aptitude Test (GAT-ART) based on both 3PL-NLR and 4PL-NLR methods indicate that only 10 DIF items had moderate to large DIF effects sizes. According to MLR differential distractor functioning (DDF) results, all items exhibited DDF across gender except for one item. An interesting finding of this study is that DIF items related to the verbal analogy and context analysis were in favor of female students, while all DIF items related to the reading comprehension subdomain were in favor of male students, which may signal the existence of content specific DIF or true ability difference across gender. DDF results show that distractors have a significant effect on DIF results. Therefore, DDF analysis is suggested along with DIF analysis since it signals the possible causes of DIF.

Policy-Based Spam Detection of Tweets Dataset

“…It is essential to develop automated methods that can detect and indicate incidents of harassment in real-time, allowing for prompt interventions; • Machine learning and natural language processing: To identify and classify instances of online harassment and cyberbullying on Twitter, it is necessary to construct sophisticated machine learning models and natural language processing techniques. These models should be consistent with Twitter's guidelines regarding abusive behavior and harassment, allowing for the proper identification and classification of harmful content [43,44];…”

“…Machine learning and natural language processing: To identify and classify instances of online harassment and cyberbullying on Twitter, it is necessary to construct sophisticated machine learning models and natural language processing techniques. These models should be consistent with Twitter's guidelines regarding abusive behavior and harassment, allowing for the proper identification and classification of harmful content [43,44]; Efforts should be made consistently to optimize the performance of spam identification classifiers. This involves fine-tuning the models, refining the feature selection procedure, and researching creative techniques to improve spam detection's accuracy, precision, and recall.…”