Display options
Cite
Bauermeister J, Pingel E, Zimmerman M, et al. Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data. Field methods. 2012;24(3):272-291doi: 10.1177/1525822X12443097.
Bauermeister, J., Pingel, E., Zimmerman, M., Couper, M., Carballo-Diéguez, A., & Strecher, V. J. (2012). Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data. Field methods, 24(3), 272-291. https://doi.org/10.1177/1525822X12443097
Bauermeister, Jose, et al. "Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data." Field methods vol. 24,3 (2012): 272-291. doi: https://doi.org/10.1177/1525822X12443097
Bauermeister J, Pingel E, Zimmerman M, Couper M, Carballo-Diéguez A, Strecher VJ. Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data. Field methods. 2012 Aug 01;24(3):272-291. doi: 10.1177/1525822X12443097. Epub 2012 Apr 26. PMID: 23180978; PMCID: PMC3505140.
Copy Download .nbib Format: NLM
Share it on

Field methods. 2012 Aug 01;24(3):272-291. doi: 10.1177/1525822X12443097. Epub 2012 Apr 26.

Data Quality in web-based HIV/AIDS research: Handling Invalid and Suspicious Data.

Field methods

Jose Bauermeister, Emily Pingel, Marc Zimmerman, Mick Couper, Alex Carballo-Diéguez, Victor J Strecher

Affiliations

  1. Department of Health Behavior & Health Education, School of Public Health, University of Michigan - Ann Arbor, MI.

PMID: 23180978 PMCID: PMC3505140 DOI: 10.1177/1525822X12443097

Abstract

Invalid data may compromise data quality. We examined how decisions taken to handle these data may affect the relationship between Internet use and HIV risk behaviors in a sample of young men who have sex with men (YMSM). We recorded 548 entries during the three-month period, and created 6 analytic groups (i.e., full sample, entries initially tagged as valid, suspicious entries, valid cases mislabeled as suspicious, fraudulent data, and total valid cases) using data quality decisions. We compared these groups on the sample's composition and their bivariate relationships. Forty-one cases were marked as invalid, affecting the statistical precision of our estimates but not the relationships between variables. Sixty-two additional cases were flagged as suspicious entries and found to contribute to the sample's diversity and observed relationships. Using our final analytic sample (N = 447; M = 21.48 years old, SD = 1.98), we found that very conservative criteria regarding data exclusion may prevent researchers from observing true associations. We discuss the implications of data quality decisions and its implications for the design of future HIV/AIDS web-surveys.

References

  1. Curr HIV/AIDS Rep. 2007 Dec;4(4):201-7 - PubMed
  2. J Sex Res. 2005 Nov;42(4):342-52 - PubMed
  3. AIDS Care. 2004 Nov;16(8):1002-11 - PubMed
  4. Am J Public Health. 2007 Jun;97(6):1113-7 - PubMed
  5. AIDS Behav. 2008 Nov;12(6):964-73 - PubMed
  6. AIDS Behav. 2011 Feb;15(2):261-72 - PubMed
  7. Am Psychol. 2004 Feb-Mar;59(2):105-17 - PubMed
  8. Sex Transm Dis. 2006 Sep;33(9):576-84 - PubMed
  9. AIDS Behav. 2007 Jul;11(4):505-21 - PubMed

Publication Types

Grant support