Overview
The editors seek to publish leading-edge papers on any aspect of data quality in survey research in the wake of technology, artificial intelligence (i.e., bots) and other novel developments.
The use of survey methodology is ubiquitous in social science research. Information (data) from surveys may have profound effects on decision-making in business, politics/government, and healthcare, to name a few broad areas. Threats to the quality of survey data are many and varied given the advances in electronic technology for data collection. For example, respondents may be humans acting unethically with the goal of capturing an incentive rather than offering true/meaningful information. Respondents may be non-human (i.e., bots) when humans are expected. Both examples demonstrate fraud; a serious threat to data quality.
Other threats to data quality include problems in research design. Survey data can be compromised without proper respondent screening. Surveys in which the topic is considered sensitive has been found to affect data quality. Measurement issues (such as exclusion of a “prefer not to answer” option in a forced-choice scale) may decrease data quality. The Insights Association, a leading non-profit organization representing the marketing research industry, recently highlighted the importance of data quality. In the Data Integrity Initiative Report of 2022 the following quote is pertinent to our topic:
"In fact, insights are increasingly performed by a brand’s internal non-research resources, who opt for user-driven tools and “roughly right” speed to insights over expert researchers and traditional, slower and more accurate research approaches. As a result, ensuring both providers and users of sample and data services are well versed on data integrity is of the utmost importance to preserving sound decision-making and the reputation and value of the marketing research industry." (https://www.insightsassociation.org/Resources/Data-Integrity-Toolkit)
Possible Topics
- Survey fraud - how to recognize and/or combat fraud
- Data collection strategies -- via social media, email, panels, etc.
- Survey design (including but not limited to)
- evaluation and use of online panels (i.e., sample sourcing)
- metrics to evaluate data quality
- Reporting data quality to clients and/or other stakeholders
