Creating Inheritable Digital Codebooks for Qualitative Research Data Analysis

  • Shalin Hai-JewEmail author
Part of the Multimedia Systems and Applications book series (MMSA)


In qualitative, mixed methods, and multi-methodology research, a codebook captures how research data may be analyzed for insight. Codebooks serve multiple purposes: they enable researchers to explore data; identify patterns; advance their research, and develop insights. Codebooks, also referred to as code lists, may be created using emergent methods (based on researcher interaction with the target data), a priori methods (based on theories, frameworks, models, and other extant sources), and combined approaches (informed by a priori sources and insights from the coding). With the affordances of Computer Assisted Qualitative Data AnalysiS (CAQDAS) tools, contemporary codebooks may originate in more ways than manual coding. NVivo 11 Plus enables the development of semi- and fully autocoded codebooks based on three main methods: sentiment autocoding (unsupervised), automated theme extraction (unsupervised), and coding by existing pattern (supervised). The use of technologies fundamentally influences the evolution of codebooks and the applied coding to the research data. This work introduces some of these interaction effects between a researcher and a CAQDAS technology in the development of a codebook from initial conceptualization through the evolution and finalization of a digital codebook for qualitative research. Further, this work suggests that contemporary digital codebooks may be designed to be inheritable or transferable, which is an assumption more common in fields using mostly quantitative research approaches. If there are more common distribution channels for qualitative, mixed methods, and multi-methodology research codebooks, research may be advanced in many areas.


Qualitative Research Mixed Method Research Context Code List Technological Affordances 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I am deeply grateful to the anonymous reviewers who provided feedback on this work. It is hard to gain perspective when one is so deeply focused on a work. Thanks!


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kansas State UniversityManhattanUSA

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