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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Validation Relaxation: A Quality Assurance Strategy for Electronic Data Collection.

Avi Kenny1, Nicholas Gordon1, Thomas Griffiths1

  • 1Last Mile Health, Boston, MA, United States.

Journal of Medical Internet Research
|August 20, 2017
PubMed
Summary

Mobile data collection enhances efficiency but risks errors. A "validation relaxation" strategy successfully identified and monitored data errors, improving enumerator performance over time.

Keywords:
data accuracydata collectioneHealthmHealthquestionnaire designresearch methodologysurvey methodologysurveys

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Area of Science:

  • Public Health
  • Health Informatics
  • Data Science

Background:

  • Mobile devices are increasingly used for data collection in developing countries, offering potential quality and efficiency gains over paper-based methods.
  • However, mobile data collection systems lack hardcopy backups, hindering standard quality assurance and potentially masking database flaws, enumerator misunderstandings, and recording errors.

Purpose of the Study:

  • To design and evaluate a strategy called "validation relaxation" for estimating data error rates and assessing enumerator performance during electronic data collection.
  • This method intentionally omits validation features for specific questions to allow, detect, and monitor data recording errors.

Main Methods:

  • A cluster sample population survey in Liberia utilized an electronic data collection system (Open Data Kit).
  • A classification scheme for detectable errors was developed, and validation relaxation techniques (intentional redundancy, removal of "required" constraints, illogical response combinations) were implemented.
  • Error rates were calculated, and logistic regression analyzed changes in error rates over time for individuals and the program.

Main Results:

  • The aggregate error rate was 1.60%.
  • Error rates did not significantly differ between enumerators but decreased significantly over time with increased application use (from 2.3% to 0.6%).
  • The highest error rate (13.6%) occurred with an intentional redundancy question for a birthdate field; other errors were low (0.0%–3.1%).

Conclusions:

  • Removing validation rules on electronic data capture platforms enables the detection and monitoring of data errors.
  • "Validation relaxation" can assess enumerator error rates, identify trends, and pinpoint areas needing further training or quality control.
  • This strategy is valuable for identifying errors responsive to training and should be part of a comprehensive data quality assurance approach.