Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring. It starts with a question, and data is nothing but an answer to that question.

Researchers rely heavily on data as they have a story to tell or problems to solve. We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.” Why analyze data in research? On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion. It helps find patterns and themes in the data for easy identification and linking.

Summarization and categorization together contribute to becoming the second known method used for data reduction. Three essential things occur during the data analysis process - the first is data organization. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. Considerations in research data analysisĭefinition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights.Methods used for data analysis in quantitative research.Methods used for data analysis in qualitative research.

Finding patterns in the qualitative data.

Data analysis tools for research