Work Description

Title: Qualitative Data Reuse: Records of Practice in Educational Research and Teacher Development: Interview Protocol, Qualitative Data Analysis Codeset, and Attributes for the Interviews Open Access Deposited
Attribute Value
  • The semi-structured interview protocol is based on a thorough review of the literature and our previous research in this area. The interview protocol was piloted with individuals who represented our target interview populations which helped the research team to refine questions as well as add and delete questions of higher relevance to the participants. Our participant sample consisted of data reusers in the field of education, with an emphasis on mathematics education research and teacher education. We defined data reusers as individuals who used digital records of practice that they themselves did not produce for new research and/or teaching purposes. We consulted the research literature and attended conferences to recruit participants using convenience sampling to identify researchers, pre-service teacher-educators, and in-service teachers engaged in professional development. We also asked interviewees to nominate additional interviewees using a snowball sampling technique. Of those 44 interviewees, the majority identified either research or pre-service teacher education as their primary areas of reuse, and described themselves as university faculty members. The majority of our interviewees were at institutions in the United States.
  • These data collection and analysis protocols and the attribute list are part of a larger research project, the Institute of Museum and Library Services # LG-06-14-0122-14. funded "Qualitative Data Reuse: Records of Practice in Educational Research and Teacher Development." As such, our research questions concern data reuse and data curation: 1. Data Reuse: What are the dynamics of the data reuse lifecycle (from selection of data through the reuse of data) in a qualitative digital educational archive? 2. Data curation: What special issues are involved in curating digital qualitative data for reuse? • How can qualitative data archives best support data reusers throughout the data reuse lifecycle? • What aspects of this experience are informative for other types of qualitative data archives? The overall project employed mixed methods and collected interview, observational, and trace data from data reusers of video records of practice in education and repositories holding video records of practice. The interview protocol and interview codeset relate to the 44 interviews conducted with researchers and teacher-educators who have reused digital video records of practice as qualitative data for research and/or teaching.
Contact information
Funding agency
  • Other Funding Agency
Other Funding agency
  • Other Funding Agency
ORSP grant number
  • 14-PAF04585
Date coverage
  • 2014-12-01 to 2017-09-01
Citations to related material
  • Frank, Rebecca D, Tyler, Allison R. B., Gault, Anna, Suzuka, Kara, and Yakel, Elizabeth, (forthcoming), "Issues of Privacy in Qualitative Video Data Reuse," International Journal of Digital Curation
  • Suzuka, K., Frank, R., Crawford, E. & Yakel, E. (2018). Video re-use in mathematics teacher education. In E. Langran & J. Borup (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 329-336). Washington, D.C., United States: Association for the Advancement of Computing in Education (AACE).
  • Frank, R. D., Chen, Z., Crawford, E., Suzuka, K. and Yakel, E. (2017), Trust in qualitative data repositories. Proceedings of the Association for Information Science and Technology, 54: 102–111.
  • Frank, R. D., Suzuka, K., & Yakel, E. (2016). Examining the Reuse of Qualitative Research Data: Digital Video in Education. In Archiving Conference (Vol. 2016, pp. 146–151). Washington, DC: Society for Imaging Science and Technology.
Resource type
Last modified
  • 07/18/2018
  • doi:10.7302/Z28C9TGP
CC License


Files (Count: 2; Size: 1020 KB)


Download will include text file of metadata.

Globus is for large data sets.   What is Globus?