用于恢复个体与群体层面效应的AI情境测量：与调查测量及职业应用的验证

中文摘要 AI 辅助

研究人员越来越多地使用人工智能来构建传统调查中缺失的社会、组织和职业特征的测量指标。我们提出AICOME（AI COntextual MEasurement，人工智能情境测量）框架，用于评估AI生成的受访者层面测量指标是否能在情境模型中恢复个体和群体层面的效应。核心思路是，在受访者层面构建的AI测量指标可用于推导其群体层面的聚合值和个体偏差，使研究人员能够同时估计群体间和群体内的关联，而非仅将AI测量视为响应预测。我们使用2022年中国家庭追踪调查（CFPS）验证该框架，其中职业提供实证分组结构，若干与工作相关的调查变量提供验证基准。针对计算机使用、外语使用、每周工作时长和管理职责，我们在受访者层面、模型层面、情境及边界条件验证中，将调查测量与AI生成的测量进行比较。结果显示，当拥有丰富的受访者和工作特征时，AI情境测量可恢复观测调查变量中包含的大部分情境模型信息。每周工作时长提供了最有力的验证案例，AI生成的测量再现了CFPS中观察到的与满意度相关的强烈负向职业间和职业内关联。该框架还确定了明确的边界条件：当信息仅限于职业和基本人口统计数据时，性能会下降；当多个相关概念被同时视为未观测时，恢复效果会更弱。研究结果表明，AICOME最适合从丰富的现有数据集中恢复少量理论上重要的构念。

英文摘要

Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent from conventional surveys. We propose AICOME, AI COntextual MEasurement, a framework for evaluating whether AI-derived respondent-level measures can recover individual and group-level effects in contextual models. The key idea is that an AI measure constructed at the respondent level can be used to derive its group-level aggregate and its individual deviation, allowing researchers to estimate both between-group and within-group associations rather than treating AI measurement as response prediction alone. We validate the framework using the 2022 China Family Panel Studies (CFPS), where occupations provide the empirical grouping structure and several job-related survey variables provide validation benchmarks. For computer use, foreign-language use, weekly hours, and management responsibilities, we compare survey measures with AI-derived measures in response-level, model-level, contextual, and boundary-condition validations. The results show that AI contextual measurement can recover much of the contextual-model information contained in observed survey variables when rich respondent and job characteristics are available. Weekly hours provides the strongest validation case, with AI-derived measures reproducing the large negative between- and within-occupation associations with satisfaction observed in CFPS. The framework also identifies clear boundary conditions: performance deteriorates when information is restricted to occupation and basic demographics, and recovery is weaker when several related concepts are treated as simultaneously unobserved. The findings suggest that AICOME is most useful for recovering a limited number of theoretically important constructs from rich existing datasets.

发表机构

• Northwestern University（西北大学）
• Nanjing University（南京大学）

机构由 AI 辅助整理，请以论文原文为准。