1. Promoting Integrity in the Next Generation of Researchers A Curriculum for Responsible Conduct of Research In Occupational Therapy (2005) Funded by the Office of Research Integrity through the American Association of Medical Colleges

2. Data Management

3. Objectives  Discuss why data management is an issue of both scientific rigor and ethics.  Describe what constitutes data.  Discuss who is responsible for the accuracy of data collection, recording, and storage.  Discuss the policies that determine how long data must be kept, who may access data, and in what contexts data may or must be withheld.

4. Research Relies on Trust  Principal Investigators are rarely supervised  Even data collectors are rarely line-of-sight supervised  Research relies on a researchers to: Develop and employ unbiased research methods Honestly and accurately report a study’s methods, data handling, and analyses

5. Data Management Includes:  Data Collection  Recordkeeping  Data Analysis  Data Ownership  Data Storage/Retention  Sharing Data (Magnus & Kalichman, 2002)

6. Examples of Data  Recorded by an observer Physical characteristics or behaviors  Live or recorded  Self-report by participant Questionnaires, Checklists, Surveys  Records of research decisions  Records of research protocols  Interaction between participants & researcher Tapes/notes: Focus Groups, Interviews  Recordings by computer Digitized images Instrument measures  Institutional files Medical or School Records

7. Researchers Share Responsibility for Data  Principal Investigator Creates system that collects, records, and stores data Trains data collectors Supervises data collection, recording, and storage  Data Collector Collects data Records data Stores data while study is in process

8. Responsible Data Collection  It is unethical to: Knowingly collect data in a manner that is biased Falsify or fabricate data Tailor or change a protocol to alter findings Change or remove non-conforming data to bend findings

9. Your Policy on Data Collection  Insert your University /Department data collection policy here  Or ask students to develop same

10. Responsible Record Keeping  Records must be thorough, complete, and organized.  Keep Outcome data Meeting notes and other records that show history of work, “what was done, by whom, and when” (Magnus & Kalichman, 2002)

11. Responsible Record Keeping (continued)  In quantitative studies, deviations from protocol should be avoided Record all actions, incidents, and variations from protocol in a lab notebook (University of Minnesota, n.d.)

12. Responsible Record Keeping (continued)  In qualitative studies, protocols may be intentionally altered during a study Confirm changes with advisor & collaborators Record changes, actions, incidents, and variations in a lab notebook Record reasoning for above as well

13. Responsible Record Keeping (continued)  In both qualitative and quantitative studies, a research notebook should Be used only for the research study Have continuous pages Be written in ink

14. Responsible Record Keeping (continued)  When correcting errors on data forms or lab notebook Strike through, date, and initial all errors or corrections. Note reasons for changes if they are not obvious. Never erase. (University of Minnesota, n.d.)

15. Your University’s or Department’s Policy on Record Keeping  Insert your University /Department record keeping policy here

16. Responsible Data Analysis  It is unethical to change, add, or exclude data to bias findings or produce a desired result.  In quantitative research: Decisions to exclude data from analysis must be based on objective rules, preferably established before the ‘cleaning’ Excluded data should be described in any report made of the study Reduce likelihood of biased interpretation by using blind analysis

17. Responsible Data Analysis (continued)  In qualitative research: Divergent, conflicting, or disconfirming subject themes should be reflected in the findings Ensure that findings accurately reflect data through triangulation, peer review, and member checking

18. Responsible Data Analysis (continued)  If any qualitative or quantitative data are excluded or changed, researchers must inform audience or reader How data were reviewed How data were selected for exclusion or change Which data were excluded from analysis How exclusion or alteration changed findings

19. Responsible Data Ownership  Regardless of funding source, study data belong to the institution (University or College), not the individual researcher or collaborative group  Institutions may choose not to exert their ownership  Individual researchers (students and faculty) are generally permitted to have a copy of the study data after subject identifiers are removed

20. Your University’s Data Ownership Policy  Insert your University /Department ownership policy here

21. Responsible Data Storage  Data should be stored in a manner that protects them from loss, theft, or damage  Store historical records of decision making, draft work, and other documents detailing processes with the same care as outcome data

22. Responsible Data Storage (continued)  Store signed consent forms separate from data forms  Store master-code connecting names to participant numbers, separate from data forms  Destroy master-code as soon as possible.  Keep a ‘back-up copy’ of database stripped of identifiers

23. Responsible Data Retention  Ensure that data are retrievable regardless of technological changes in Devices used to collect data Software or hardware used to store data

24. Responsible Data Retention (continued)  Data should be kept after study ends Federally funded data must be kept for at least 3 years after a final report (Columbia University, 2003–2004) American Psychological Association recommends that data be kept for 5 years post-publication (American Psychological Association, 2001)

25. Responsible Data Sharing  Data sharing refers to one researcher allowing another to use another’s raw data or database  Federal guidelines encourage data sharing of NIH-supported studies (National Institutes of Health, 2003)  Unless proprietary agreements prohibit sharing, all interested parties may access data gathered using public funding

26. Responsible Data Sharing (continued)  Before sharing: Protect rights and privacy of participants (e.g., IRB, HIPAA) by stripping all identifiers or variables that could identify individual subjects

27. Responsible Data Sharing (continued)  Data cannot be shared if: It is impossible to strip identifiers or otherwise protect confidentiality and anonymity of subjects. Sharing compromises proprietary information and there are temporary restrictions specified by contractual agreement with sponsors

28. Resources  American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.  Columbia University. (2003–2004). Responsible conduct of research: Courses portal. Course 6: Data acquisition and management. Retrieved August 20, 2005, from http://www.ccnmtl.columbia.edu/projects/rcr/rcr_da ta/foundation/index.html. http://www.ccnmtl.columbia.edu/projects/rcr/rcr_da ta/foundation/index.html

29. Resources (continued)  Magnus, P. D., & Kalichman, M. (2002, September). Data management. Retrieved August 20, 2005, from RCR Education Resources, Online Resource for RCR Instructors: http://rcrec.org/r/index.php?module=ContentExpress &func=display&meid=29&ceid=2. http://rcrec.org/r/index.php?module=ContentExpress &func=display&meid=29&ceid=2  Martinson, B. C., Anderson, M. S., & deVries, R. (2005). Scientists behaving badly. Nature, 435, 737– 738.

30. Resources (continued)  National Institutes of Health. (2003, February 26). Final NIH statement on sharing research data. Retrieved August 20, 2005, from http://grants.nih.gov/grants/guide/notice-files/NOT- OD-03-032.html. http://grants.nih.gov/grants/guide/notice-files/NOT- OD-03-032.html  University of Minnesota. (n.d.). Guidelines for maintaining laboratory notebooks. Retrieved August 20, 2005, from http://www.ptm.umn.edu/v3/documents/labnotes.pdf.

31. This completes the presentation on Data Management THANK YOU!