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Qualitative Methods Refresher: Creating Codes and Building a Robust Codebook 

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Eli Lieber, Ph.D. and Sara E. Grummert, Ph.D.  

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Coding data is one of the most creative, fun, and important aspects of research. 

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There are hundreds of resources available regarding qualitative coding strategies, as well as how to build a strong codebook (a.k.a. codetree) for your data analysis.  

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In this blog, we make a return to the foundations of creating a robust coding system—from creating thoughtful codes to systematically developing a comprehensive codebook optimized for your research questions!  

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First Of All, What Is a Code?  

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A code is a word or phrase researchers apply to an excerpt of their data (whether text, image, video, audio, etc.) that captures a specific meaning, pattern, relationship, or theme (Salmona et al., 2019).  

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Qualitative and mixed methods researchers develop dozens of codes over the course of a project. Together, these form a codebook or Code Tree, which makes the definition and dynamic management of codes essential for quality research.  

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Which Strategies or Methods Do We Use to Create Codes? 

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Codes must be developed systematically into a label we can use to identify and organize content meaningful to our research questions.  

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This way, codes can be used consistently by different members of our research teams. This also helps to categorize data across our data—that is, this helps to tell a broader story. 

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So, briefly, here are two separate ways we develop code systems: 

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1. Codes are identified in one of two ways: 
   

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   1. ‘A Priori’ or ‘Etic’—codes are predetermined based on theory, an interview protocol, or some other manner not directly connected to the data. This is also referred to as deductive coding. 
      

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   2. ‘in Vivo’ or’Emic’–codes are more emergent in nature as they are inspired by what is found directly in the data and help us see and understand things we did not anticipate. This is also referred to as inductive coding.
      

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2. Code viability is then developed through an iterative process of hypothesis generation, hypothesis testing, modification, reconsidering, re-testing ….
   

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   …until we have something that everyone on our team can consistently apply to the data in our project and capture something meaningful across our research data that speaks to our research questions!

    

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   For example, say we come up with a code called ‘religiosity’ and define its use to ‘tag the stuff people tell us about religious rituals they engage in at home each day.’ But what if some people say they only do these activities 5 days a week?

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   What if others say they sometimes do these activities at home and sometimes they do them in a temple? What if we see this in the reports of the first three of our participants, but none of the other 50 people talk at all about religious rituals engaged in at home?

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   This is a simple example of the kinds of questions we should be asking as we develop guidelines for how our codes are defined and the criteria we will use to decide if they are to be applied or not.

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   A rule book, or code book, should evolve as themes develop and we then keep testing and modifying the rules as we explore if the codes will be useful for all the kinds of data we collect from our participants.

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   Remember, it is easy to come up with nice dictionary definitions for the codes we think we will be using.  However, people in natural ways use  their own rich and varied language in how they communicate with us during our research.  This is the messy and wonderful world of qualitative research methods as it can be tricky to map the ‘academic’ concepts we have in our heads to the very real-world data we collect.

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3. Do this thoughtfully and thoroughly enough, however, and you may be rewarded with a code system that stabilize as you develop enough codes to capture all the important content you find in your qualitative data to address your questions.

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Quick Qual Coding Tips:  

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When you begin coding, code a portion of your data, such as an interview, and pause the process to assess the codes you have developed and/or applied. Doing so will help you decide which preliminary codes deserve to be in your codebook, which preliminary codes can collapse into one overarching code, and so on.  

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 This iterative process is especially important when coding inductively and/or in teams as it will build efficiency by minimizing the maintenance of codes that are unnecessary, redundant, and overly specific/idiosyncratic and help inform the building of a manageable and effective code system.  

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Within Dedoose, there are several analysis charts and features that can aid your codebook-building process including the Code Application Chart to view code to media application, the Code Co-Occurrence Chart to view how codes are applied together on common content, and the Code Presence chart to easily view and track how codes are applied across your media.  

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Our Publication “Qualitative and Mixed Methods Data Analysis Using Dedoose” Can Help You Master Coding (And Our App!) Faster. 

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New and experienced researchers alike can benefit from the expert tips, tricks and how-to’s contained in the Dedoose methods textbook as presented by the Dedoose creators.  And check out the outstanding case studies from a range of experienced researchers as real-world examples of our cloud-based app is being put into action across the social sciences.  

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We encourage Instructors to request a Review Copy or select the electronic or physical version (Both come equipped with student-friendly pricing).  

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If you are interested in trying out Dedoose for yourself first, enjoy a free 30-Day Trial on us. Remember—there are never any old-school ‘licenses’ with Dedoose, you only pay your subscription fees when you actually use the app! Pretty fair, huh? 

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Thank you for Using Dedoose! Happy Data Wrangling.  

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