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In Section 3 we will describe in detail the process of data sampling and collection.
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Other uses of data sampling are uncontroversial.
Results are derived for two contexts: a fixed taxon coverage pattern, such as that observed from an already assembled data set, and a randomly generated pattern derived from a process of sampling new data, such as might be observed in an ongoing comparative genomics sequencing project.
In the following we provide some information about the process of data collection, briefly discussing the sampling methodology and the techniques adopted to collect data.
Therefore, some of the inconsistency among analyses may be due simply to the process of randomly sampling the complete data set 100 times for each 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.
This is called theoretical sampling - "the process of data collection for generating theory whereby the analyst jointly collects, codes, and analyses his data and decides what data to collect next and where to find them, in order to develop his theory as it emerges" [ 16, 17].
We used theoretical sampling during the process of data analysis, so that each new case was selected because he/she was particularly appropriate for clarifying relationships and logic among constructs.
In Subsection 4.1, the sample and the process of data collection are described.
The process of data analysis was ongoing during data collection for the purpose of theoretical sampling and saturation [ 14].
The processes of sampling, data collection and data analysis were continuous and iterative.
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