Data Ethics

​Ethics are principles of right and wrong above and beyond legal parameters. These are especially important because technology today allows for easy access to information, as well as readily available tools that enable misleading manipulation of data. This publication is segmented into canons, each providing a real-life example along with recommended behavior presented in a comprehensible format that encourages supplemental discussion and promotes use as a template for the adoption of new policies; it does not undermine the need for formal and systematic training in this area. The nine canons are divided into three sections: Integrity, Quality and Security.
 
A person with integrity demonstrates honesty and professionalism. They will not cheat, steal or lie, and they will comply with the standards of behavior that apply to their role. Among these are an ability to perceive data as providing only partial information about the whole person. Especially important is current knowledge of “responsibilities regarding the protection of student data under the Family Education Rights and Privacy Act (FERPA), the Individuals with Disabilities Education Act (IDEA), and the Health Insurance Portability and Accountability Act (HIPAA)” (Forum Guide to Data Ethics, 2010, p. 13). Following unbiased standardized data reporting protocols and governing operations minimizes the possibility that others may misinterpret information. Finally, be willing to hold others accountable for ethical use of data, understanding that they should be doing the same with you.
 
Quality data has practical utility, accuracy, validity, and reliability. It is also timely and cost-effective. Take care to include relevant data, definitions, and documentation that promotes understanding and analysis because value judgements depend greatly on information that provides context.
 
Good security is valuable as it preserves resources, protects private information, and promotes functional operation of the academic system. Establishing policies and enforcing procedures to protect sensitive data helps guarantee privacy and confidentiality.

REFERENCE

National Forum on Education Statistics. (2010). Forum guide to data ethics (N FES 2010–801). U.S. Department of Education. Washington, DC: National Center for Education Statistics. 

INSIGHT

Because non-cognitive data describes personal traits of an individual, it should be treated as private information and treated with the utmost integrity and security for the protection of both the individual and the quality of the data.