Does survey mode matter? An experimental evaluation of data quality in China

IF 5.2 1区经济学 Q1 ECONOMICS 中国经济评论 Pub Date : 2024-09-05 DOI:10.1016/j.chieco.2024.102271

Shihan Feng, Feng Huang

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Abstract

This study employs experimental data from the Labor Force Survey (LFS) to compare the data quality of computer-assisted web interviewing (CAWI) and computer-assisted personal interviewing (CAPI). The experiment was conducted in 2023 as part of the LFS, an ongoing longitudinal face-to-face survey of Chinese adults aged 16 and above in a mega city. Using the identical questionnaire, respondents were randomly assigned to either the control group (CAPI mode only) or the treatment group (optional CAPI and CAWI modes). The characteristics of households and individuals obtained using mixed-mode design do not significantly differ from those obtained from single-mode design (CAPI), indicating no mode effect on data quality for mixed-mode surveys. Additionally, there are no significant differences in data quality between CAWI and CAPI. CAWI respondents tend to take more time to answer the questions compared to CAPI respondents. Our findings offer valuable insights into enhancing mode-specific targeting and improving the quality of survey data collection by leveraging both existing survey data and paradata.

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调查模式重要吗？中国数据质量的实验评估

本研究利用劳动力调查（LFS）的实验数据，比较计算机辅助网络访谈（CAWI）和计算机辅助个人访谈（CAPI）的数据质量。该实验于 2023 年进行，是劳动力调查的一部分。劳动力调查是一项对特大城市中 16 岁及以上中国成年人进行的纵向面对面调查。使用相同的问卷，受访者被随机分配到对照组（仅 CAPI 模式）或治疗组（可选 CAPI 和 CAWI 模式）。采用混合模式设计获得的家庭和个人特征与采用单一模式设计（CAPI）获得的家庭和个人特征没有显著差异，表明混合模式调查对数据质量没有模式影响。此外，CAWI 和 CAPI 在数据质量方面也没有显著差异。与 CAPI 受访者相比，CAWI 受访者往往需要更多时间回答问题。我们的研究结果为利用现有调查数据和范式加强特定调查模式的针对性和提高调查数据收集质量提供了宝贵的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

中国经济评论 ECONOMICS-

CiteScore

10.60

自引率

4.40%

发文量

380

期刊介绍： The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.