At Decision Analyst, panel and sample quality assurance takes place at a number of key points during the research process, starting with panel recruitment and continuing with sample selection, data collection, and data cleaning.

The term "data weighting" in most survey-related instances refers to respondent weighting (which in turn weights the data or weights the answers). Here are some best practices to keep in mind when you are thinking about weighting survey data.

Non-response bias occurs when people who participate in a market research study are inherently different from people who do not participate. This bias can negatively impact the representativeness of the research sample and lead to skewed outcomes.

As market researchers, we are obligated to dig deeper to better understand the Hispanic market, ensure our sample is representative of the market, and deliver actionable and accurate research.

Stereotypes and cookie cutters and cohorts do not lead to brilliant, original marketing and advertising campaigns.

Think carefully about your sampling plans, find sampling partners who can serve as trusted consultants, and be ever vigilant lest unwanted sampling biases creep into your surveys.

Decision Analyst started its first online panels in late 1995 and progressively added new panels thereafter, including international and B2B online panels. Here are the systems and practices that Decision Analyst employs in its pursuit of “clean and pure” online samples:

Social, economic, political, and technological advancements have made their mark on Gen Z. Continuing to monitor and learn how this generation is impacted, and more importantly, how your company should communicate with and cater to Gen Z, will only serve to benefit your brand.

Tradespeople, heavy industry, and construction professionals, what can we do to best reach these hard-to-find but vital segments? Here are some best practices and techniques

It's generally a good idea to keep your target universe as broad and inclusive as possible. Here is some advice on dealing with Universe Error, Standard Error, and Sample Screening Error in marketing research surveys.

Online data collection relies heavily on the honesty of respondents. Key areas of mitigating front-end data vulnerability include correct identification of respondents, constructing surveys that are both cognitively and affectively close to the actual consumer decision-making process.