Sampling Bias Mitigation

Sampling Bias Mitigation is a methodological strategy in data collection that systematically adjusts for distortions arising from non-representative sampling to ensure that the resulting data accurately reflect the target population.

220

Publications

19.4K

Citations

915

Authors

354

Institutions

Publications per year

2017–2026

87

Authors

915

  1. DD
    Douglas D. Heckathorn

    Cornell University, United States

    4 publications · 2.4K citations

    4

    h-index

  2. MS
    Mark S. Handcock

    University of Washington, United States

    2 publications · 660 citations

    2

    h-index

  3. AK
    Akira Kunimatsu

    The University of Tokyo, Japan

    2 publications · 225 citations

    2

    h-index

  4. WU
    Wolf U. Blanckenhorn

    University of Zurich, Switzerland

    2 publications · 59 citations

    2

    h-index

  5. KK
    Kiyoto Kasai

    The University of Tokyo, Japan

    2 publications · 225 citations

    2

    h-index

1–5 of 915

Venues

  1. 9

    h-index

  2. PLoS ONE

    8 publications · 2.1K citations

    8

    h-index

  3. PubMed

    Repository · 6 publications · 164 citations

    6

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 221 citations

    5

    h-index

  5. Expert Systems with Applications

    4 publications · 924 citations

    4

    h-index

1–5