Introduction There are a number of different qualitative research techniques that can be used to analyze data. These include crisp and fuzzy set qualitative comparative analysis, content analysis, exploratory design and explanatory design. This paper will examine each of these individually, and seek to contrast them. Then, the best types of research for each design will be examined, and the paper will conclude with an explanation of how technology has changed qualitative research.

Qualitative Comparative Analysis

Qualitative comparative analysis is sometimes used in business settings. This is when contextual information and cognitive knowledge are blended. The data sets can either be crisp or fuzzy. The difference is that crisp data sets would be, for example, survey responses where there are a finite number of responses, and they are set out in the questions. A fuzzy set would be the output of a survey with open-ended questions. For the purposes of analysis, the big difference is that the fuzzy set you might have different responses that basically say the same thing. To properly analyze that data, the fuzzy set would have to be codified. An example would be a simple geographic response. The question “what country are you from?” with a dropdown menu would yield only United States. But if the question is open-ended, you’d get “US”, “USA”, “United States”, “United States of America” and maybe some others. So different responses that say the exact same thing.

This is a simple example. For more complex examples, it will be more difficult to code, but the principle is the same. The researcher would look for answers that are similar or the same, group them together in order to make comparisons about the data.

There are several advantages to this qualitative technique. First, it can handle both crisp and fuzzy data sets, which is the required degree of flexibility in qualitative analysis. Furthermore, This technique provides the opportunity to compare...

Comparative analysis can help researchers draw conclusions. This technique probably works best with smaller data sets, because the comparison is often manual. However, for larger responses, the inherent flexibility of this methodology allows for comparison to still take place, but limits the utility of trying to do this over larger data sets.
Content Analysis

Content analysis is a means by which qualitative data is distilled into key pieces of information that can then be analyzed. An example might be Harrison (2013), who examined over 2000 articles to determine different types of mixed methods research. By analyzing the content of the different articles, searching for specific themes or topics, the research has the ability to determine how the information is coded, and that provides the researcher with significant flexibility. While there is a manual element to this, it is clear that the researcher also can benefit from this amount of freedom in shaping his or her own research.

The content analysis is best used for things like research where there are key content themes. It works best when there is a common thread – in the Harrison case these are all research papers. Where there is no common thread, a content analysis will simply yield that there’s nothing in common between the content in the different pieces.

The content analysis can be done over large data sets, but because it requires manual codification this process will be time-consuming. For business, it is perhaps not the best system, in the sense that content analysis would need to be automated in order to be efficient, and It often is not that.

Exploratory Design

An exploratory design is one that starts with qualitative data, leading to a quantitative follow-up. The exploratory nature uses the open-ended, fuzzy nature of qualitative data to explore a concept with the objective of being able to formulate a testable hypothesis. This type of design seeks to take a fairly broad subject, learn more…