Sentence examples for and trustworthiness of data from inspiring English sources

Qualitative methods informed by sociocultural and expansive learning theories countered ethnocentric tendencies, ensured equivalency, and trustworthiness of data.

If data was gathered during an experiment it can be considered reliable andtrustworthy 6.1 9.1 11.1 29.3 44.4 The reliability and trustworthiness of data should always be questioned 18.

Related to reliability and validity of scientific knowledge, less than half of the participants thought that the reliability and trustworthiness of data should always be questioned (44.4%) and that scientists know that atoms exist because they have made observations that can only be explained by the existence of such particles (35.4%).

Additional steps taken to ensure quality and trustworthiness of data analysis included the following: all transcribed interviews were checked by the interviewer for consistency and all audio-taped interviews were checked line by line for accuracy.

To ensure qualitative rigour and trustworthiness of the data, two researchers analyzed the data separately.

The veracity property of big data relates to reliability and trustworthiness of big data systems.

To improve quality and trustworthiness of the data, a range of participants were interviewed and regular meetings were held between data collectors to discuss the data collection process and identify inconsistencies between findings of various participant groups.

As without assurances of security and trustworthiness of retrieved data, the utility and effectiveness is always doubtful.

Collecting data from an adequate number and from different types of respondents allowed the use of triangulation to assess the consistency and trustworthiness of the data.

The use of multiple methods, interrogated under KMR, not only added to the robustness and trustworthiness of the data but more importantly these methods were found to be culturally acceptable by the Māori participants.

The complexity in the smart city data analytics manifests due to a variety of issues: i) Requirements of cross-thematic applications e.g. energy, transport, water, urban etc, and ii) multiple sources of data providing unstructured, semi-structured or structured data, and iii) trustworthiness of data [6,7].