Surveys have long been the primary source of data collection about peoples’ attitudes, beliefs, and opinions and are useful for measuring specific information about individuals, as well as understanding public opinions and creating accurate and precise official statistics. Recently, artifacts of our increasingly digital lives have offered additional, broader information about our behaviors (eg purchase histories, personal interests captured through internet browsing) in the form of 'big data'.
These two sources of information have great potential to complement one another to allow scientists to better understand people and the world in which we live, for instance by combining the low cost per data point of big data (offsetting the rising costs of survey-based data collection) with the ability to collect very specific information addressing research questions using survey data.
This themed issue of the Journal of the Royal Statistical Society, Series A is dedicated to solutions to these challenges, through innovative methodological developments and applications, bringing together computer science and social science. As usual with Series A, the focus is on the development and/or evaluation of innovative methodology that is directly motivated by, and substantially increases our understanding of, real world data problems in social and public data collection settings.
In this themed issue, we are looking for the state-of-the-art in research at the intersection of these two approaches to data collection and analyses, exploring important issues pertaining to their combination and use in the social and computer sciences.
Topics include, but are not limited to:
- Combining multiple sources of data (such as registers, survey and qualitative data, media data, and sensor data)
- Ethics of data collection, processing, sharing, and use in the era of big data
- Measurement, feature construction
- Innovations in data collection: methods and new data sources
- Advances in questionnaire and survey user interface/experience design and evaluation
- Artificial Intelligence (AI) or machine learning (ML) in data cleaning and processing
- Estimation issues using big data and/or survey data
- Advances in modeling: machine learning, deep learning, AI; computationally intensive methods
- Quality assessments of various sources of data (such as registers, survey and qualitative data, media data, and sensor data)
- Communication and data visualization.
We particularly encourage interdisciplinary submissions that involve collaboration between social scientists and computer scientists. This themed issue is closely connected with the BigSurv20 conference, a major conference in the intersection of big data and survey science. Select participants have also been invited to submit papers to this issue, but interested contributors can submit to issue without attending conference.
The deadline for manuscript submissions is midnight on 15th January 2021.
Submissions— which should clearly indicate 'JRSS‐A Big Data Meets Survey Science themed issue' in the cover letter—should be made in the usual way online at https://mc.manuscriptcentral.com/jrss, where further guidance about the structure, length and format of manuscripts may be found.
All manuscripts will be peer reviewed in line with the journal’s standard policy. However, in order to produce the themed issue in a timely manner, authors will be asked to complete revisions within eight weeks of receiving referee reports.
Guest editors: Dr Don Jang (firstname.lastname@example.org), Dr Antje Kirchner (email@example.com), Professor Ana Lucía Cordova Cazar (firstname.lastname@example.org), and Professor Daniel Oberski (email@example.com).