Data Quality Interpretation

Data Quality Interpretation is a methodological framework within the interpretation of results that systematically evaluates the accuracy, completeness, consistency, and reliability of empirical data to assess its suitability for valid inference and meaningful conclusion drawing in scholarly research.

70

Publications

4.8K

Citations

311

Authors

178

Institutions

Publications per year

2017–2026

24

Authors

311

  1. CZ
    Chunhui Zhao

    State Key Laboratory of Industrial Control Technology

    3 publications · 94 citations

    3

    h-index

  2. RJ
    Richard J. Howes

    Research Manitoba, Canada

    2 publications · 138 citations

    2

    h-index

  3. FG
    Furong Gao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 44 citations

    2

    h-index

  4. PM
    Philip M. Farrell

    University of Wisconsin–Madison, United States

    1 publications · 15 citations

    1

    h-index

  5. AB
    Asunción Borrell

    Universitat de Barcelona, Spain

    1 publications · 16 citations

    1

    h-index

1–5 of 311

Venues

  1. PubMed

    Repository · 3 publications · 141 citations

    3

    h-index

  2. Analytical Chemistry

    2 publications · 60 citations

    2

    h-index

  3. Sexual Abuse

    2 publications · 138 citations

    2

    h-index

  4. Journal of Clinical Epidemiology

    2 publications · 335 citations

    2

    h-index

  5. BioMed Research International

    1 publications · 78 citations

    1

    h-index

1–5