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Achieving Transparency, Traceability, and Readability with Human-Coded Data

2024/12/16 by Amanda B. Edgell, Jean Lachapelle, Seraphine F. Maerz · 1 voice
Computer Science · Medicine · Social Sciences · #Computational and Text Analysis Methods #Data Analysis with R #Data-Driven Disease Surveillance

paper · pdf · doi:10.1017/s1049096524000714

openalex publication_date 2024/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

ABSTRACT Many important questions in political science require the use of human-coded data or information that has been systematically ordered and quantified by a human being from qualitative sources. This article discusses challenges and recent innovations in collecting and documenting human-coded data. We review five datasets produced within the past 10 years and also reflect on our experiences in collecting a quarterly dataset that tracked state responses to the COVID-19 pandemic. We argue that scholars can deliberately produce and publish theoretically grounded human-coded data in an accessible format that promotes transparency, traceability, and readability. We highlight several ways that scholars are already doing this, including narratives, source lists, and coding justifications that enhance the quality of their human-coded datasets. We also discuss common issues during coding and how technological innovation through interactive web-based platforms can improve the documentation of coding decisions.

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