The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result. In this post, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.
The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.
Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions.
While there are several types of processes that are implemented based on individual data nature, the two broadest and most common categories are “quantitative and qualitative analysis”.
Qualitative data analysis can be summed up in one word – categorical.
With this type of analysis, data is not described through numerical values or patterns, but through the use of descriptive context. Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:
Related Posts
Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly-focused data segmentation.
A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes.
After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.
