While Big Data offers a potentially less expensive, less burdensome, and more timely alternative to survey data for producing a variety of statistics, it is not without error. But, the construction of, access to, and overall data structure of Big Data make it difficult to know where to start looking for errors and even more difficult to account or correct for them.
In this course, we will walk through the Total Error Framework, an extension of the Total Survey Error framework, which can be applied to all types of Big Data and can serve as a template for researchers to investigate error in Big Data. We will walk through several examples of error and map it onto the framework and provide exercises for participants to come up with their own examples.
Finally, we will walk through some best practices in determining whether the use of Big Data is a ‘good’ choice for various research objectives, how to correct or avoid errors in Big Data, and documenting the strengths and weaknesses of your Big Data source.
Instructor: Ashley Amaya
Dr. Ashley Amaya is a Senior Survey Methodologist at the Pew Research Center. She has a PhD in survey methodology from the Joint Program in Survey Methodology at the University of Maryland. Dr. Amaya has published several articles and book chapters on the Total Survey Error Framework, it’s adaptation to Big Data, and the synthesis of survey and Big Data.
Registration Fees:
This is part of the RTI/Odum series of short courses. There are no registration fees associated with this course, but registration is required to secure your spot.
Additional course information: