计算机科学
工作流程
代码本
数据收集
数据质量
数据挖掘
数据库
质量(理念)
情报检索
人工智能
运营管理
公制(单位)
哲学
统计
数学
认识论
经济
作者
Kristoffer Bjärkefur,Luíza Cardoso de Andrade,Benjamin Daniels
标识
DOI:10.1177/1536867x231196496
摘要
Data collection and cleaning workflows implement highly repetitive but extremely important processes. In this article, we describe an update to iefieldkit, a package developed to standardize and simplify best practices for high-quality primary data collection across the World Bank’s Development Impact Evaluation department. The first release of iefieldkit provided workflows to automate error checking for Open Data Kit-based survey modules, duplicate management, data cleaning, and codebook creation. This update to the package includes improved commands to document and implement data point corrections, verify the structure or contents of data using codebooks, and create replicationready data through automated variable subsetting.
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