Qualitative Data Interpretation
Qualitative data interpretation is the process of understanding non-numeric data like concepts, opinions, or experiences. It is very useful for getting the insights from the interviews, focus groups, and open-ended survey responses and thus it makes a contribution to the theory development and the making of the informed decision.
The most widely used means of collecting qualitative data are interviewing, focus groups, and observational studies. Such as, the in-depth interviews make it possible for researchers to go deeper into the participants' thoughts and feelings, and focus groups make it easier for discussions amongst the participants to get out the common problems or attitudes.
Having the ability to use triangulation, which is a combination of different data sources and methods to verify the findings, means that researchers are able to increase the trustworthiness of their qualitative data interpretation. Also, the researchers can team up with peer debriefing and member checking where the participants go through the findings for errors and by this, they make sure that the interpretation is exactly as they think.
Coding is a essential step in qualitative data analysis where researchers sort data into themes or patterns. For instance, when a researcher is analyzing interview transcripts, he may code answers that are related to 'customer satisfaction' and 'product quality' to find individual themes that he may then use to inform business strategies.
Definitely, qualitative data interpretation makes it a tool for the understanding of quantitative data by explaining the conditions of numerical findings. That is, for instance, when a questionnaire tells about how a customer felt the satisfaction was, qualitative interviews can tell the reasons for the said score of satisfaction, which is ultimately more comprehensive.