Sampling Data Quality

Sampling Data Quality is a methodological construct within resource reporting standards that evaluates the representativeness, accuracy, and reliability of data collected through sampling techniques to ensure valid inference and transparency in scientific reporting.

62

Publications

7.2K

Citations

239

Authors

126

Institutions

Publications per year

2017–2026

26

Authors

239

  1. LS
    Laura Schechter

    University of Wisconsin–Madison, United States

    3 publications · 258 citations

    3

    h-index

  2. AG
    Alan Grossfield

    University of Rochester Medical Center, United States

    3 publications · 501 citations

    3

    h-index

  3. DM
    Daniel M. Zuckerman

    University of Pittsburgh, United States

    3 publications · 501 citations

    3

    h-index

  4. GG
    George G. Judge

    University of California, Berkeley, United States

    3 publications · 258 citations

    3

    h-index

  5. DR
    Daniel R. Roe

    University of Utah, United States

    2 publications · 192 citations

    2

    h-index

1–5 of 239

Venues

  1. Information & Management

    2 publications · 23 citations

    2

    h-index

  2. Living Journal of Computational Molecular Science

    LiveCoMS · 2 publications · 192 citations

    2

    h-index

  3. The Journal of Human Resources

    JHR · 2 publications · 244 citations

    2

    h-index

  4. Analytica Chimica Acta

    2 publications · 101 citations

    2

    h-index

  5. Methods in Psychology

    1 publications · 168 citations

    1

    h-index

1–5