Data Preparation- Explorations in Analytical Frameworks
When conducting qualitative analysis, the researcher should be keen to ensure that the data that they collect is free from biases. To be unbiased, the data should preserve the words as best as possible and also have the data support what the research stands for. Therefore, when undertaking data preparation, the researcher filters the data to ensure that what is not needed is left out and what is essential remains so that the remaining data is precise and legitimate (Lukenchuk, 2017). Therefore, data preparation helps the researcher go through the collected information to narrow it down so that what is left is not misleading and also so that what is left does not have very little that cannot help in the study (Chandra & Shang, 2019). From some scholars’ perspectives, data preparation is about cleaning the data.
Furthermore, when undertaking qualitative data collection, one must be careful to identify data saturation. Lukenchuk (2017) has defined data saturation as a point during data collection when one realizes that there is no new information collected or discovered during the data analysis. Therefore, a researcher will know that saturation is reached when nothing new is uncovered during an investigation (Chandra & Shang, 2019). In the case of qualitative research, the researcher will detect saturation when participants are repeating themselves and when what is shared by one is similar to what is shared by another participant.
Analytical frameworks are created to shape analysts’ thinking and further encourage logical thought. So, these frameworks facilitate sense-making. They include grounded theory, ethnography, phenomenological research, case studies, and narratives. Grounded theory (GT) is central to this week’s discussion, and it means collecting data and analyzing it simultaneously (Lukenchuk, 2017). Theoretical coding happens when substantive categories are related to the central category. From examining the two, preferences would lean towards the grounded theory because it is inherently flexible.
References
Chandra, Y., & Shang, L. (2019). Qualitative research using R: A systematic approach. Springer, Singapore
Lukenchuk, A. (2017). Chapter Seven: Working with the Data and Presenting the Findings. Counterpoints, 428, 101–117. http://www.jstor.org/stable/45177755
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Question 
Data preparation is a step in the research process most of us who do not often do research forget must be done. Outliers, incomplete interviews, and other considerations apply even in qualitative research. What are the pragmatic aspects of data preparation; in other words, why must it be done? When collecting data at the qualitative level, how do we know when saturation has been reached?
Data Preparation- Explorations in Analytical Frameworks
Finally, this week, identify the analytical frameworks addressed in your reading and this week’s lesson. Does one hold more appeal to you than another; in what way?