Sentence examples for expert sampling from inspiring English sources

In another study [11] streaming API is compared with expert sampling.

Although, many empirical studies evaluated the effectiveness of expert sampling in many dimensions such as trustworthy, diversity of discussion topics, statistic representative of samples, or sentiment.

However, the major problem with the mentioned techniques is that, these techniques are biased toward high degree nodes similar to expert sampling.

Therefore, we can conclude from previous studies and the recent ones [23] that expert sampling is rich in content and is more valuable for content-based models such as topical models.

In fact, Twitter streaming preserves the statistics of the sample size as the whole representative sample, but for content-based models which can benefit from the context, expert sampling is more superior.

Additionally, our expert sample was relatively small (n = 10), racially homogeneous, and dominated by men.

After discussing the traditional Delphi approach and its dissensus based derivatives, the author opens the case for a dissensus Delphi based explorative research tool with special consideration of the Delphi aim, the expert sample and the Delphi design.

This research is trying to measure people's acceptance, to live in or deal with these buildings, whom had no previous experience, using the photo-questionnaire survey and interviews with a purposive expert sample (n = 164) at Egypt and Japan.

The expert sample consists of four vocational educational training (VET) school teachers and five trainers from small (<50 employees: n = 1), medium (51 250 employees: n = 1), large (251 1000 employees: n = 1), and very large (>1000 employees: n = 2) enterprises.

In the expert sample, six participants were of the rank of course coordinator, lecturer, or assistant professor; two were of the rank of associate professor; and two were of the rank of full professor.

This study expands our understanding of design practitioners' cognitive processes by exploring the development of innovative solutions for transactional problems using a DbA approach, via a semantic-word-based ideation method, on a relatively large expert sample size (n = 73) of transactional domain experts.