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Key research themes
1. How can online survey and sampling methodologies optimize data quality and recruitment efficiency in diverse populations?
This research theme focuses on methodological innovations and practical implementations in online data collection, particularly exploring survey modes, respondent-driven sampling (RDS), and crowdsourcing platforms. As online surveys and web-based respondent-driven sampling gain prominence, understanding how to design, implement, and monitor these techniques is crucial for reaching diverse and hard-to-reach populations efficiently while ensuring data quality, representativeness, and participant engagement. This area addresses challenges such as self-selection bias, digital exclusion, data validity, anonymity, and sampling frame absence.
Key finding: Developed a bespoke webRDS platform integrating an existing survey tool (ODK) with a custom RDS management system, enabling anonymous recruitment through multiple channels (e.g. WhatsApp) and supporting large,... Read more
Key finding: Provided a clear framework for implementing WebRDS based on formative research, survey design, coupon system development using free software, and monitoring strategies. The study underscores the necessity of understanding... Read more
Key finding: Demonstrated that web-based survey administration offers distinct advantages in speed, convenience, and respondent accessibility compared to traditional survey modes, while also allowing dynamic instrument features such as... Read more
Key finding: Analyzed the strengths and limitations of crowdsourcing platforms like MTurk for survey data collection, highlighting best practices to increase data validity and reliability. Findings address common issues such as... Read more
Key finding: Systematically compared different survey modes for discrete choice experiments, emphasizing the implications of sampling strategies (e.g., random vs. opt-in panels) on data quality and generalizability. The work clarifies... Read more
2. What innovations in online aggregation, automation, and process integration improve efficiency and transparency in web-based data collection systems?
This theme investigates technical and process-oriented advancements in online data collection platforms, including real-time query processing (online aggregation), automated generation of data collection systems, and integration of geoprocessing services. These innovations aim to enhance user interactivity, reduce development overhead, ensure dynamic adaptability, and facilitate sharing and dissemination of geo-spatial and survey data. Addressing system architecture, code generation, and dynamic rendering enables more robust, scalable, and accessible web data platforms that respond interactively to users' needs, making data collection more efficient and transparent.
Key finding: Introduced an online aggregation framework in relational DBMS allowing users to observe and interact with running aggregation queries, including adaptive control over query execution and provision of running confidence... Read more
Key finding: Proposed a novel code generation and dynamic form rendering approach that abstracts data schema definitions from implementation, dramatically shortening development time, standardizing architecture, and reducing human errors.... Read more
Key finding: Presented a theoretical approach for sharing and combining geo-processing services over the web, using the OGC Open Web Services specification to enable interoperability and chaining of spatial data processing (e.g., PS-InSAR... Read more
3. What are the key considerations and best practices for producing and sharing high-quality, ethically compliant open data derived from online research platforms?
This research theme addresses the challenges and strategies involved in managing the entire lifecycle of online data collection for scientific research, including ethical participant consent, data accuracy, participant burden, and the production of open, reusable datasets. It highlights methodologies for designing accessible web platforms, maintaining participant motivation and data validity, and adhering to disciplinary and legal frameworks for data sharing, thereby enhancing both societal and scientific benefits through transparency and interoperability.
Key finding: Developed and evaluated a bespoke online recruitment and data collection platform for breast reconstruction patients, demonstrating feasibility, participant acceptability, and high accuracy of self-reported clinical data. The... Read more
Key finding: Provided a best-practice stepwise guide for producing and sharing open research data involving personal data, focusing on participant consent, data preparation and metadata standards, repository selection, and ethical/legal... Read more
Key finding: Reviewed contemporary challenges in business data collection such as respondent burden, technological impacts, imperfect response processes, and emerging alternative data sources, advocating for innovative qualitative and... Read more
Key finding: Established WEBDATANET, a multidisciplinary European network addressing methodological challenges in web-based data collection, promoting knowledge exchange, theoretical and empirical research, and setting best practices. It... Read more
Key finding: Examined the practical realities contrasting the promises of Internet data collection, emphasizing that while online methods offer increased sample size, diversity, and cost savings, challenges remain around ethics, technical... Read more