Wiki Pages > Producing Information > Supporting Qualitative Data by Using Content Analysis
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.

References

The 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:
  1. What is being analyzed?
  2. How is that data being defined?
  3. What is the population being drawn?
  4. What is the relative context of the data being analyzed?
  5. Are there any boundaries of the data being analyzed?
  6. What is the target of inferences?
Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Sage Journals, 97-113. Retrieved from http://qrj.sagepub.com/content/6/1/97.full.pdf html In this article, Bryman focuses on combining both qualitative and quantitative data. This is done by incorporating content analysis and using it to highlight the important points of both. The way to integrate qualitative and quantitative data (according to Bryman) is to create two sections of a report to be equally dominant throughout the text. Both sections use content analysis to both specify and clarify the data being discussed in a report. Hsieh, H. F., & Shannon, S. E. (2005).Three approaches to qualitative content analysis. Sage Journals, 1277-1288. In this article, Hsieh and Shannon discuss multiple ways to approach qualitative content analysis. They explain how to use content analysis to highlight the subject matter's key issues. Content analysis uses qualitative data to focus on language rather than data. The goal is “to provide knowledge and understanding of the phenomenon under study.” (p. 1278). Neuendorf, K. A. (2002).The Content Analysis Guidebook. Thousand Oaks, CA: Sage Publications Nuendorf highlights the importance of making clear and concise translations between qualitative data and concept analysis. Providing a clear representation of information is a crucial element in technical communication. Misunderstanding or misrepresenting data is a common mistake committed by writers who cannot translate a message properly. It is always important to make direct correlations between pieces of data. Failing this causes reader confusion. Sandelowskie, M. (2000). Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies. Research in Nursing & Health, 23, 246-255. In this article, Sandelowskie explains how it is possible to combine the two forms of data. Qualitative and quantitative data can complement one another if used properly. Sandelowskie explains how mixed-method studies are not two opposite subject matters. They allow for a better reflection of represented data. In these cases, quantitative analysis shows the significance of the study, while qualitative data extracts more information from the quantitative data and confirms the data. Using content analysis for both will further expose the important pieces of content.