【作者简介】马文，南京大学新闻传播学院；陈云松，南京大学社会学院

【文章来源】《社会学研究》2026年第2期

【内容提要】在定量研究中，描述因缺乏解释能力被逐渐边缘化，而数智技术的发展为其重返研究核心提供了新的可能。本文提出不同于回归模型假设检验的“数据深描”方法，旨在运用多模态数据和算法技术，对社会现象及过程的时空结构和潜在关系进行清晰呈现。数据深描通过可视化结构展示、数智化指标测量、局部描述关联累积与算法模型因果预示，分别形成关于结构感知的“景深”、概念指标的“进深”、关联呈现的“层深”与潜在因果的“纵深”，从而为定量研究从描述性理解向解释性理解的转变搭建桥梁，也体现了构建中国自主知识体系的方法自觉。

【关键词】数据深描；因果分析；定量研究方法；自主知识体系

【项目基金】本文为国家社会科学基金重大项目“国家治理视角下基于数智方法的社会风险评估与应对”（24&ZD168）的阶段性成果。

【全文链接】https://shxyj.ajcass.com/Magazine/show/?id=122489

Revisiting Deseription: Data Deep Description in Quantitative Research

Abstract: In the realm of quantitative social science research, descriptive approaches have gradually been marginalized due to their limited capacity for explanation. The advancement of digital and intelligent technologies offers new possibilities for their return to the research core. This paper proposes the “data deep description” method,which differs from regression model hypothesis testing by employing multimodal data and algorithmic techniques to clearly present the spatio-temporal structures and latent relationships of social phenomena and their processes. Through visualized structural displays, digital-intelligent indicator measurement, localized description accumulation of associations, and algorithmic model anticipation of causality, data deep description achieves structural pereeption’s “depth of field”, conceptual indicators' “depth of elaboration”, association presentation's “depth of layering”, and latent causality's ‘depth of extension” separately. This approach builds a bridge for the transition from descriptive understanding to interpretive understanding in quantitative research. embodying a methodological consciousness in constructing China's autonomous knowledge system.