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Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data.
Jose Bauermeister1, Emily Pingel, Marc Zimmerman
1Department of Health Behavior & Health Education, School of Public Health, University of Michigan - Ann Arbor, MI.
Field Methods
|November 28, 2012
Summary
Data quality decisions impact HIV risk research among young men who have sex with men (YMSM). Overly strict data exclusion may obscure true associations, affecting HIV/AIDS web-survey designs.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Data quality is crucial in research, especially for sensitive topics like HIV risk behaviors.
- Decisions on handling invalid or suspicious data can influence study outcomes.
- Young men who have sex with men (YMSM) are a key population for HIV/AIDS prevention research.
Purpose of the Study:
- To investigate how data quality decisions affect the association between internet use and HIV risk behaviors in YMSM.
- To evaluate the impact of excluding invalid and suspicious data on statistical findings.
- To provide insights for designing future HIV/AIDS web-surveys.
Main Methods:
- Collected 548 entries over three months for analysis.
- Created 6 analytic groups based on data quality decisions (full sample, valid, suspicious, mislabeled, fraudulent, total valid).
- Compared groups on sample composition and bivariate relationships.
Main Results:
- Excluding 41 invalid cases affected statistical precision but not variable relationships.
- Flagging 62 suspicious entries revealed their contribution to sample diversity and observed relationships.
- Conservative data exclusion criteria may lead to overlooking genuine associations.
Conclusions:
- Data quality decisions significantly influence findings in HIV/AIDS web-surveys.
- Researchers must carefully consider data handling to avoid obscuring true associations.
- Methodological transparency in data quality is essential for reliable HIV/AIDS research.
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