Introduction and how the sampling industry works
The landscape of market research sampling has had to evolve significantly in recent years. AI-driven bots are trying to infiltrate primary market research, and high-quality respondents have so many competing priorities, that engaging them in market research has become increasingly difficult. Researchers are also under pressure to complete projects quicker while maintaining data quality. This is particularly critical in healthcare and pharmaceutical market research, where the accuracy and reliability of data can directly impact a company’s bottom-line and even patient outcomes.
When acquiring samples from providers you might encounter three common scenarios:
Proprietary sample: A provider has its own database or panel, which it builds, recruits, manages, and owns. In healthcare and pharmaceutical research, these panels often include patients with specific conditions, caregivers, or healthcare professionals.
Partnered sample: A provider collaborates with other companies to meet project quotas by combining multiple sources when its proprietary sample is insufficient. This approach is useful for reaching challenging populations, such as patients with rare diseases or specialists in a particular medical field.
Aggregated sample: A provider without its own database, resells or aggregates samples from other providers, leveraging their knowledge of various sources and unique technologies. This method can be effective for large-scale studies requiring diverse participant profiles.
Transparency about sample characteristics and partnerships is crucial for sample providers, especially in healthcare research, where participant demographics are key to data integrity.
Sample biases and sample blending
Samples are recruited through various methods, such as online, offline, in-person, telephone, and mail, and each introduces its own biases. For instance, panels built from online game rewards might skew younger, while those from travel reward programs might have higher-income respondents. In healthcare research, recruitment methods can introduce biases related to aspects like health literacy, access to healthcare, and socioeconomic status. Understanding sample biases is essential for interpreting results accurately.
When facing difficult sampling quotas and/or to help balance sampling biases, providers often blend samples from multiple sources. This approach has both advantages and disadvantages:
Communication and transparency
Effective communication among companies offering market research, client-side researchers, and sample providers is essential. Early dialogue during project specification and bidding helps ensure clarity about sample origins and the feasibility of using proprietary or partnered samples. Researchers must be informed buyers, asking critical questions about sample sources and their impact on project fulfillment and data quality. In healthcare and pharmaceutical research, this also includes understanding regulatory requirements and ensuring compliance with privacy laws such as HIPAA.
Market research data cleaning checklist
To ensure sample quality, data cleaning is a crucial process. Reviewing datasets for outliers, bots, fraudulent respondents, speeders, and cheaters before completing data collection is essential. This process includes:
Thorough data cleaning ensures high-quality data for analysis and reporting, which is critical for making informed decisions
Conclusion
Understanding the sampling industry is key to conducting effective market research. Being aware of the different types of sample providers, potential biases introduced by various recruitment methods, and pros and cons of sample blending can help researchers make informed decisions to ensure the quality and reliability of their data. Clear communication and transparency between researchers and sample providers is essential, and a thorough data cleaning process is required to maintain the integrity of the research findings. By staying vigilant, market researchers can navigate the complexities of sampling and deliver valuable insights that drive informed decision-making.
