Best Practices for Online and Virtual Data Collection Methods to Ensure Data Integrity

Although online recruitment and data collection methods have the potential to reach a wider audience and decrease study costs, such methods have also introduced unique threats to data quality and reproducibility that are not commonly observed when conducting research in-person. Thus, it is important for researchers to understand how to best utilize online methods to avoid threats to data integrity (e.g., bots, misrepresentation, and duplicate participation) at each stage of the research process (i.e., recruitment, consent, and data collection). In this article, the authors discuss threats to data integrity and provide three case examples to illustrate such threats when conducting online research. The authors will also provide recommendations for practices to minimize the threats to data integrity in online research. What is the significance of this article for the general public?The public significance of establishing methods to ensure data integrity cannot be overstated. Safeguarding data from manipulation, inaccuracy, and falsification are paramount to developing effective public health recommendations and interventions. Additionally, demonstrating that effective methods to ensure data integrity exist can promote public confidence in science.