Methods

Research data privacy

Privacy planning asks what information a study needs, who can access it, and what could happen if it is disclosed or linked with other information. Removing names alone does not settle those questions.

What could reveal a participant’s identity or sensitive circumstances, and which protections match the study’s actual risks?

How it works

  1. Map the information collected and the people or systems that handle it. For a classroom planning exercise, separate contact details used for scheduling from the responses used for analysis, and identify why each field is needed.
  2. Assess the proposed sharing conditions before release. NIH guidance recommends considering privacy even for legally de-identified data and considering controlled access where appropriate. Consent can establish sharing conditions, but consent itself is not a technical privacy safeguard.
  3. Document a practical access and retention plan with institutional specialists. Explain what can be public, what requires permission, and what should remain restricted. Reassess linkage risk when a new data source or proposed reuse changes the context.

Evidence: (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

A supposedly anonymous diary

Teaching scenario

Synthetic teaching scenario: A student removes names from diaries about stress but retains exact dates, a rare job title, and descriptions of a small club. A classmate recognizes the combination without needing a direct identifier. The exercise asks students to create two packages: a fictional demonstration file for the class and a restricted research file for authorized analysis. They also draft a plain-language description of who would see each package. The scenario does not establish that any particular redaction method is sufficient for a real dataset.

This example is constructed for learning. It does not report an observed research result.

What can you conclude?

It supports deliberate decisions about collection and access while preserving useful analysis wherever ethically and legally possible.

Evidence: (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

Limits and common mistakes

  • De-identification can reduce risk but cannot guarantee that no one will identify a person through combinations or outside information.
  • NIH guidance is scoped to its policy context and does not replace applicable privacy law, consent conditions, or institutional review.

A mistake to catch

Calling a file anonymous simply because the name column is gone, then uploading detailed free text without further assessment.

Evidence: (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

Sources and inspection notes

Links checked October 21, 2026. Inspection scope is stated for each source; an access date is not a publication date.

  1. Protecting privacy when sharing human research participant data. (n.d.). https://www.grants.nih.gov/grants/guide/notice-files/NOT-OD-22-213.html

    US NIH supplemental privacy guidance, NOT-OD-22-213 Nonbinding principles and best practices associated with the NIH data-sharing policy; not a global compliance manual. Checked 21 October 2026.

Keep reading

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020). Psychology 2e. OpenStax. https://openstax.org/books/psychology-2e/pages/2-4-ethics

Nearest related OpenStax Psychology 2e reading. This is a course connection, not evidence for every specialist detail above.

Specialist reading: Protecting privacy when sharing human research participant data. (n.d.). https://www.grants.nih.gov/grants/guide/notice-files/NOT-OD-22-213.html

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