Validity Concerns in Research Using Organic Data

Topic: Validity Concerns in Research Using Organic Data

Speaker: Nan Zhang

Time: June 6，2019 13:30

Location: No. 25 teaching building, 3th floor, A classroom

Speaker:

Dr. Nan Zhang is a Professor of IT and Analytics at the American University’s Kogod School of Business. Dr. Zhang is a world-renowned expert on database anddata analytics, having published over 100 research papers and served as a program director at the U.S. National Science Foundation (NSF) for both fields. According to csrankings.org, He is ranked among top 15 computer scientists in premier Database publicationsfrom 2009 to 2019 in the U.S. Before joining Kogod, Dr. Zhang was a professor of Information/Computer Science at Penn State, George Washington, and UT Arlington. His work has received several awards, including the NSF CAREER award in 2008, Best Paper Awardsfrom IEEE ICC 2013 and NAS 2010, and Best Paper Nominations from IEEE ISI 2015 and HICSS 2018/2019.

Abstract:

The field of meta-science -- the use of scientific methodology to study science itself -- has examined various aspects of this robustness requirement for researchthat uses conventional designed studies (e.g., surveys, laboratory experiments) to collect and analyze data. Largely missing, however, are efforts to examine the robustness of empirical research using "organic data", namely data that are generated withoutany explicit research design elements and are continuously documented by digital devices (e.g., content and social interactions extracted from social networking sites, Twitter feeds, and click streams). Given the growing popularity of using organic data inbusiness research, it is essential to understand issues concerning the usage and processing of organic data that may affect the robustness of research findings. This paper first provides an overview of commonly present issues that threaten the validity ofinferences drawn from empirical studies using organic data. This is followed by a discussion on some key considerations and suggestions for making organic data a robust and integral part of future research endeavors in management research.