论文标题
用于检测差异项目功能的非参数贝叶斯模型：美国政治代表的申请
A Non-parametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US
论文作者
论文摘要
研究代表质量时的一种共同方法是比较选民和立法者的潜在偏好，通常通过将项目响应理论（IRT）模型与共同的刺激集拟合。尽管受到相同的刺激，选民和立法者可能不会对这些刺激如何映射到其潜在偏好方面有共同的理解，从而导致差异性项目功能（DIF）和估计的无与伦比。我们通过重新分析了有影响力的调查数据集，探索通过IRT模型获得的潜在偏好的无与伦比的存在，调查受访者表示他们对美国立法者先前投票的票数呼叫投票的偏好。为此，我们建议在标准IRT模型中的项目响应函数之前定义Dirichlet过程。与典型的多步检测DIF的方法相反，我们的策略使研究人员能够拟合单个模型，自动识别来自潜在性状的不同映射的无与伦比的亚组，从潜在性状上识别到观察到的响应上。我们发现，尽管有一群选民可以将其估计的立场与立法者进行比较，但大量的受调查选民以根本不同的方式了解刺激。忽略这些问题可能会导致关于代表质量的错误结论。
A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item-response theory (IRT) model to a common set of stimuli. Despite being exposed to the same stimuli, voters and legislators may not share a common understanding of how these stimuli map onto their latent preferences, leading to differential item-functioning (DIF) and incomparability of estimates. We explore the presence of DIF and incomparability of latent preferences obtained through IRT models by re-analyzing an influential survey data set, where survey respondents expressed their preferences on roll call votes that U.S. legislators had previously voted on. To do so, we propose defining a Dirichlet Process prior over item-response functions in standard IRT models. In contrast to typical multi-step approaches to detecting DIF, our strategy allows researchers to fit a single model, automatically identifying incomparable sub-groups with different mappings from latent traits onto observed responses. We find that although there is a group of voters whose estimated positions can be safely compared to those of legislators, a sizeable share of surveyed voters understand stimuli in fundamentally different ways. Ignoring these issues can lead to incorrect conclusions about the quality of representation.
