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The data extraction, data cleaning, data integration, data transformation, and data reduction operators can be regarded as the preprocessing processes of data analysis [20] which attempts to extract useful data from the raw data (also called the primary data) and refine them so that they can be used by the following data analyses.
This approach is characterised by the use of flexible analytical guidelines aimed at building inductive middle-range theory though successive processes of data analysis and theory development.
This grounded theory research developed a theory inductively from data gathered using theoretical sampling, processes of data analysis and data collection, which informed each other, and constant comparative analysis.
Furthermore, many processes of data analysis are repeatedly performed in different laboratories, individual concepts are developed and data become widely distributed but with little option to combine or share this individually generated information.
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Content analysis was used during the process of data analysis.
Open image in new window Fig. 1 Process of data analysis and themes development.
The data is too large and diverse, which makes the process of data analysis difficult and time consuming.
Information about the process of data analysis, which is gathered under the titles of "pre-assessment" and "post-assessment" are shared below.
Through coding memos, regular discussions, and joint coding sessions, the authors sought to maintain a high level of inter-coder reliability throughout the process of data analysis.
This means that traditional reduction solutions can also be used in the big data age because the complexity and memory space needed for the process of data analysis will be decreased by using sampling and dimension reduction methods.
They are well suited for the development of interactive data analysis tools, which enable the extraction of rule-based knowledge from data and the introduction of a priori knowledge in the process of data analysis and system identification.
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