Data-AnalysisThe procedure that is adopted to interpret relationships, trends, patterns, etc. within any collected information is called Data Analysis. For the information to be analyzed well in terms of establishing the relativity, it may need to be subjected to statistical reasoning to understand the relationships that exist or seem to exist between the different variables, and to what extent these relationships hold true. 

Frequent comparisons of information are done with data from other groups, which can include a control group, to derive conclusions from the accumulated data. This evaluation is mostly done to make better sense of the overall concept, through a detailed appraisal, so that the work done can be understood with much more clarity.

Types of Data

All data that is collected for analysis can be segregated into two clear categories—qualitative and quantitative, although both kinds of data may not be required for every evaluation.

The data that can be converted to numbers or statistics, is called quantitative data. On the other hand, data that is collected as stories, life experiences, personal anecdotes, quotes, opinions, or something that cannot sensibly be reduced to statistics or numbers is called qualitative data. Both data sets, qualitative and quantitative, have to be understood and analyzed differently.

Quantitative Data includes “hard” data that is expressed in numbers, can be quantified and statistically manipulated. This includes data like, average number of times an event occurs in a specified time period (month, year, and decade), etc.

Qualitative data on the other hand, is “soft” data and is mostly relative and non reducible to definite numbers. This turns out to be both a strength and a weakness. It is more concerned with the qualities of the subject. The joy of a student on seeing her test paper grade, the softness of a cloth, the smell of a perfume, the level of poverty in a community–these are all examples of qualitative data, that is, they are observable, but not quantifiable.

Qualitative data through data collection and analysis can help you understand the data better, whether the applied methods are working, and figure out emerging patterns in behavioural, physical and social scenarios.  Events, circumstances, environmental factors, etc. all refer to insights into how participants feel about the issue being addressed, the difficulties and advantages they go through, and whether anything needs to be done to change or improve what is being done.

Impetus Research—a one of a kind market research company—helps you to understand and evaluate actions during collection and analysis of data, and thus continuously improve your data analysis techniques. Read more about data collection and analysis techniques online at Impetus Research.