Scholarlink

<p>Partial credit trees (PCtree) from the model-based recursive partitioning framework combine the partial credit measurement model for polytomous items with decision trees from machine learning. This method allows researchers to investigate measurement invariance by detecting differential item and differential step functioning (DIF/DSF) in a data driven way. In this manuscript, we extend PCtrees by an effect size measure for DIF/DSF in polytomous items, the partial gamma coefficient from psychometrics. We evaluate this extension of PCtrees in a series of simulation studies. Our results show that the partial gamma coefficient supports researchers in evaluating whether splits in the tree are meaningful, identifying DIF and DSF items, and can stop the tree from growing in case of negligible effect sizes. Furthermore, we assess and implement a correction for item-wise testing that is particularly crucial in longer tests. Finally, we illustrate the extension of PCtrees using data from the LISS panel to showcase its enhanced interpretability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Partial credit trees meet the partial gamma coefficient for quantifying DIF and DSF in polytomous items

  • 期刊论文

  • 发表日期：

    2025年1月4日

查看全文
  • Mirka Henninger,
  • Jan Radek,
  • Marie-Ann Sengewald,
  • Carolin Strobl

摘要

Partial credit trees (PCtree) from the model-based recursive partitioning framework combine the partial credit measurement model for polytomous items with decision trees from machine learning. This method allows researchers to investigate measurement invariance by detecting differential item and differential step functioning (DIF/DSF) in a data driven way. In this manuscript, we extend PCtrees by an effect size measure for DIF/DSF in polytomous items, the partial gamma coefficient from psychometrics. We evaluate this extension of PCtrees in a series of simulation studies. Our results show that the partial gamma coefficient supports researchers in evaluating whether splits in the tree are meaningful, identifying DIF and DSF items, and can stop the tree from growing in case of negligible effect sizes. Furthermore, we assess and implement a correction for item-wise testing that is particularly crucial in longer tests. Finally, we illustrate the extension of PCtrees using data from the LISS panel to showcase its enhanced interpretability.