Statistical Inference for Covariate-Adjusted and Interpretable Generalized Factor Model with Application to Testing Fairness
协变量调整的可解释广义因子模型推断及测验公平性应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究教育测量中的广义潜因子模型，未提供宏观或金融因子、离散选择等所列方向的充分依据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
协变量调整的可解释广义因子模型推断及测验公平性应用
- 研究问题
- 如何在控制潜在能力等因子后识别和推断协变量对离散测验响应的影响，从而考察题目是否存在群体偏差？
- 方法与思路
- 作者研究协变量调整的广义因子模型，提出可解释的可识别性条件，采用联合最大似然估计并分析协变量效应与潜在因子。
- 材料陈述的贡献
- 摘要称，作者建立协变量效应估计的一致性和渐近正态性，以及因子和载荷的估计推断结果，并用数值研究及 PISA 数据展示。
- 阅读关注点
- 核对协变量效应与因子载荷的可识别性条件、样本量和测验长度的渐近关系，以及公平性检验与多题推断的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未给出完整条件、检验构造或应用数值，不能仅据模型效应判定公平性。
原文摘要
Latent variable models are popularly used to measure latent factors (e.g., abilities and personalities) from large-scale assessment data. Beyond understanding these latent factors, the covariate effect on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational testing, where the covariate effect reflects whether a test question is biased toward certain individual characteristics (e.g., gender and race), taking into account their latent abilities. However, the large sample sizes and test lengths pose challenges to developing efficient methods and drawing valid inferences. Moreover, to accommodate the commonly encountered discrete responses, nonlinear latent factor models are often assumed, adding further complexity. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects. Furthermore, we derive estimation and inference results for latent factors and the factor loadings. We illustrate the finite sample performance of the proposed method through extensive numerical studies and an educational assessment dataset from the Programme for International Student Assessment (PISA).