从行政混乱到分析群体：一个针对纵向大学行政记录的三阶段标准化流程
From Administrative Chaos to Analytical Cohorts: A Three-Stage Normalisation Pipeline for Longitudinal University Administrative Records
AI总结：
本文提出了一种三阶段标准化流程，用于处理纵向大学行政记录中的数据缺失问题，确保分析群体的结构一致性。
摘要：
随着纵向大学行政记录在数据驱动决策中的日益广泛应用，往往忽视了一个关键层面：如何在建模前对原始不一致数据进行标准化。本文提出了一种针对拉丁美洲一所公立大学24,133名工程学生（1980-2019年）的数据集的三阶段标准化流程。该流程包括：（i）N1 CENSAL，将人口统计数据统一为单一的个人层面；（ii）N1b IDENTITY RESOLUTION，将重复标识符整合为一个规范ID，同时保留审计跟踪；（iii）N1c GEO和SECONDARY-SCHOOL NORMALISATION，构建参考表，分类学校类型（州立、国家、州省、私立世俗、私立宗教），并标记不可恢复的情况为DATA_MISSING。该流程保留了100%的学生，实现了完整的地理编码，并为56.6%的人口提供了有效的学校类型。剩余的43.4%被识别为由于遗留招生实践而非随机非响应而结构缺失。通过犯罪学分析（卡方检验、逻辑回归）发现，缺失性高度可从入学年代和地理预测，确认了结构性和历史性的机制。本文贡献包括：（a）一种透明且可重复的高等教育标准化流程；（b）一种处理结构性缺失信息而不进行推测插补的框架；（c）指导如何定义分析上一致的群体（全人口与基于中学信息的子群体）以用于下游学习分析和政策评估。
英文摘要：
The growing use of longitudinal university administrative records in data-driven decision-making often overlooks a critical layer: how raw, inconsistent data are normalised before modelling. This article presents a three-stage normalisation pipeline for a dataset of 24,133 engineering students at a Latin American public university, spanning four decades (1980-2019). The pipeline comprises: (i) N1 CENSAL, harmonising demographics into a single person-level layer; (ii) N1b IDENTITY RESOLUTION, consolidating duplicate identifiers into a canonical ID while preserving an audit trail; and (iii) N1c GEO and SECONDARY-SCHOOL NORMALISATION, which builds reference tables, classifies school types (state national, state provincial, private secular, private religious), and flags irrecoverable cases as DATA_MISSING. The pipeline preserves 100% of students, achieves full geocoding, and yields valid school types for 56.6% of the population. The remaining 43.4% are identified as structurally missing due to legacy enrolment practices rather than stochastic non-response. Forensic analysis (chi-square, logistic regression) shows missingness is highly predictable from entry decade and geography, confirming a structural, historically induced mechanism. The article contributes: (a) a transparent, reproducible normalisation pipeline tailored to higher education; (b) a framework for treating structurally missing information without speculative imputation; and (c) guidance on defining analytically coherent cohorts (full population vs. secondary-school-informed subcohorts) for downstream learning analytics and policy evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
