Sentence examples for data quality and trustworthiness from inspiring English sources

With an increase in the diversity of prospective disease markers and a relative ease in collecting such information electronically through blogs, mailing lists, feeds, and queries, a diligent control for data quality and trustworthiness has to be established.

Theoretical saturation, constant comparative analysis, trustworthiness and validity checks provided assurance of data quality and rigor [ 38, 41].

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 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.

30 Of the 33 interviews conducted, data saturation was reached at 27 interviews, but to increase study quality and trustworthiness the extra six interviews (20%) were conducted.

This DISCERN questionnaire is used to evaluate reliability, quality, and trustworthiness of general and treatment-specific healthcare information.

In the present study, several measures were taken to ensure quality and trustworthiness.

Throughout the process, measures were taken to address these limitations and to ensure quality and trustworthiness [ 42].

To ensure the overall quality and trustworthiness of the process evaluation findings, a range of strategies will be applied across all phases of the study to achieve overall data credibility (internal validity), dependability (reliability or the consistency of the findings) and confirmability (neutrality) [ 24].

For example, Alabri and Hunter ([2010]) describe a framework combining data quality control and trust metrics to enhance the reliability of citizen science data, and Kuan et al. ([2010]) as well as Yang et al. ([2011]) propose reputation management systems to evaluate the trustworthiness of gathered data by co-observers and data end-users.

That will cause more challenges like data uncertainty and trustworthiness.