![]() ![]() To take this further you could open the codebook in Word and add an 'Example from text' column to demonstrate how each code is used: You can export your nodes and their descriptions to create a codebook from your NVivo data. So my regular first response in consulting and training now related to qualitative data analysis is – where is your codebook? Strategy 1. I’ve seen experienced teams convinced that they are all on the same page about their codes, but when given the task of developing a codebook in a systematic way, they find they often have different understandings of what they mean by common terms. It helps to clarify codes and what you mean when you apply them to your data not only to yourself, but also to your team members and supervisory staff. your examiners!) and create a process that is repeatable? How do you decide what is included in a node and what is not? How would you describe your process to someone else (i.e. I find that most of us are not very articulate about what we mean by each of the codes we are using to investigate the data. It just seems too hard doesn’t it?īelieve me, the tedious work is well worth it! But often we don’t document these in detail. We often feel we have clear conceptualizations of what we mean by different codes related to our data. thematic analysis) we believe we need to do an adequate, and hopefully even good analysis of the data we often struggled many months or years to collect. We all approach our data with the best of intentions, equipping ourselves with the tools (e.g. I have seen how it helps to fix some of the challenges that researches face when coding qualitative data. This article has become my research bible. The topic of a codebook came to my immediate attention when I read the article “ Developing and Using a Codebook for the Analysis of Interview Data: An Example from a Professional Development Research Project” by DeCuir-Gunby, Marshall and McCulloch, which was published in the Field Methods journal in 2010. (Thanks QSR for inviting me to write this post!) Every single researcher I have discussed this with (and they are now in the hundreds) has found some benefit in this, so I had to share it with you. Abbreviations commonly used in the code definitions are "DK" ("Don't Know"), "NA" ("Not Ascertained"), and "INAP" ("Inapplicable").I originally began writing this blog post about teamwork and my recent experiences in seeing how important it is to clarify the definitions of codes when working in teams.īut I now realize that such advice applies to all researchers, in all disciplines, studying all manner of topics. Value Label: Indicates the textual definitions of the codes.Code Value: Indicates the code values occurring in the data for a variable.Users can use these "Missing Data" codes as needed. If "9" is a missing value, then the codebook could note "9 = Missing Data." Other examples of missing data labels include "Refused," "Don't Know," "Blank (No Answer)," and "Legitimate Skip." Some analysis software requires that certain types of data be excluded from analysis and designated as "Missing Data," (i.e., inappropriate, not ascertained, not ascertainable, or ambiguous data categories). Missing Data Code: Indicates the values and labels of missing data.In some cases, an expanded version of the Variable Name can be found in a Variable Description List. Variable Label: Indicates an abbreviated variable description (maximum of 40 characters) that can be used to identify the variable.If the variable is a multiple-response type, then the width referenced is that of a single response. Variable Column Location: Indicates the starting location and width of a variable.Variable Name: Indicates the variable number or name assigned to each variable in the data collection.The following elements are generally included for each variable in the data file: The body of a codebook describes the content of the data file. Information on data collection, data processing, and data quality.A description of the survey design and methodology.Other indications of the content and characteristics of each variableĪdditionally, codebooks may also contain:.Exact questions and skip patterns used in a survey.Codes used to indicate nonresponse and missing data.Column locations and widths for each variable.Users are strongly encouraged to review the codebook of a study before downloading the data file(s).Īlthough codebooks vary widely in quality and amount of information given, a typical codebook includes: ![]() A codebook provides information on the structure, contents, and layout of a data file.
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