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Triangulating quantitative data with qualitative data

We know there's a vast amount of data out there and that there's an even larger stream of data coming in the future.  With all this data around, organizations are turning to various methodologies in making sense of their data.  Organizations are also turning to traditionally academic approaches such as the qualitative research methodology.  Thanks to modern technology, employee exit interviews, customers' comments, observations, videos, audios, tweets, and blogs are all now almost effortlessly captured and collected.  There are now tons of data waiting to be analyzed and utilized and it doesn't always have to be quantitative data.  It doesn't always have to be a survey where organizations put in a lot of effort in survey development and data collection. It doesn't always have to be a turnover analysis. Blogs in the internet, exit interviews of past employees, or other unstructured data can be 'structured' and analyzed as a way to make stronger assessments in organizations. Analysts should be encouraged to pursue qualitative data and make sense of them in order to add perspective, support quantitative findings, triangulate data, and enrich overall information.  Analysts can further explore the 'narratives' behind the numbers. For example, in an employee satisfaction survey score of 8 out of 10, there might be merit in exploring further what the employees are saying about their job satisfaction which can be found in blogs, wikis, personal observations, comments sections, audio/video recordings, etc. 

So here's a SWOT analysis of the Qualitative Research Aproach:

And I should also add a preface to this SWOT arrangement because the arrangement depends on the research project and the priorities. For instance, in projects that are expected to be lengthy and/or of very high priority, researchers might want to consider the lengthy, detailed, and iterative approach as an opportunity or maybe even a strength. This SWOT exercise if I remember right was done for a dissertation project and as far as I know, most people want to get their dissertation done as quickly as possible (but as thoroughly as possible of course). So as a heads up, readers take into account the nature of the project and see if some of these variables need re-arranging for your specific purpose.

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Tags: data, qualitative


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