Research Article Open Access

Data Quality and Indicators

Jorge Matute1 and A.P. Gupta2
  • 1 Private Consultant, Guatemala, c/o Section 411, PO Box 2-5289 Miami, Florida 33102-5289, United States
  • 2 Department of Soil Science, CCS Haryana Agricultural University, Hisar, India

Abstract

This study highlights the importance of collecting good quality data from multidisciplinary studies. Bias in data may be the result of instrument inaccuracies, imprecise data recording techniques, inaccurate data entry to computers or inappropriate statistical analysis and presentation. Recommendations for good data quality control are given. Different types of data are discussed: raw data, simple indicators and complex indicators. It is shown how measurements from the components of multidisciplinary systems can be combined to form complex indicators and a specific example is given using Z-scores and dot charts. Finally the accumulated effect of bias in the individual component measurements upon the combined indicator is shown.

American Journal of Agricultural and Biological Sciences
Volume 2 No. 1, 2007, 23-30

DOI: https://doi.org/10.3844/ajabssp.2007.23.30

Submitted On: 6 January 2007 Published On: 31 March 2007

How to Cite: Matute, J. & Gupta, A. (2007). Data Quality and Indicators. American Journal of Agricultural and Biological Sciences, 2(1), 23-30. https://doi.org/10.3844/ajabssp.2007.23.30

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Keywords

  • Data quality
  • indicators
  • bias