Qualitative data and the collection of that data are needed in technical communication. However, data collection alone is only a small part of what is used to create a strong technical document. Being able to translate that data is as important as the data itself. Since readers may not be familiar with particular points of qualitative analysis, it is necessary to translate data in ways that readers will understand. In a nutshell, content analysis is based on taking a large amount of content and reducing it into fewer categories by exposing the more important points. Content analysis allows researchers to sift through large amounts of data more effectively and define data they may be unfamiliar with.
ReferencesThe following sources all describe how to complete content analysis. They all base their studies off of being able to simplify information to make it clearer to the reader. Barringer, B. R. (2004). A quantitative content analysis of the characteristics of rapid-growth firms and their founders. Journal of Business Venturing, 684-687. Retrieved from Characteristics of Rapid Growth. Even though Barringer refers to quantitative content analysis in this article, the discussion can also apply to qualitative analysis. With conceptual analyses, it is vital to make a direct connection between narrative data and a summary. Readers must be able to obtain a clear understanding of the data through summaries to ensure statements are valid. Specifying different relationships and how conclusions were made will enhance how the data is interpreted by the reader. Krippendorf, K. A. (2004). Content Analysis: An Introduction to Its Methodology.(2nd ed., pp. 11-15). Thousand Oaks, CA: Sage Publications. In this book, Krippendorf refers to content analysis as a way to simplify all kinds of data. When analyzing qualitative data, put information into a conceptual context and reduce it: All information and data need to be broken down and explained so the audience or researcher can understand the information at hand without reading materials individually. Readers may not want to look too far into how information is related to the research. Information should be a summary of the information that can be translated easily and understood by the reader. Krippendorff breaks down how the information needs to be addressed by asking six questions:
- What is being analyzed?
- How is that data being defined?
- What is the population being drawn?
- What is the relative context of the data being analyzed?
- Are there any boundaries of the data being analyzed?
- What is the target of inferences?