How to ensure sample quality when conducting a survey has always been a key topic for researchers and marketers alike. Doing so ensures that the results yielded from the survey are accurate and high-quality.
Zinklar’s Sampling and Panel Online Access Advisor Efrain Ribeiro has recently conducted an investigation with the Coalition for the Advancement of Sampling Excellence, or CASE4Quality – of which he sits as a founding member. CASE’s study concluded that with the proliferation of sample providers, so has the rise of undesired responses. This means that more vigilance is needed overall.
There are real steps that can be taken to ensure the maximum quality of the sample. While speaking at a recent webinar, Ribeiro presented CASE’s findings as well as some recommendations that work well to help all teams who are looking to ensure the quality of their data. Ribeiro says: “Today, there are multiple detection services that can aid in the identification and prevention of undesired respondents at a study level.”
A Solution to Ensuring Sample Quality
Zinklar has recognized this need and continues to take steps to ensure that for every study launched, there is a layer of protection incorporated into each step of the study. The platform makes use of cutting-edge AI technology that continually monitors sample quality, as well as partnering with top-quality panel providers to build in new layers of security at every level to minimize any possible deviation from quality standards and, thus, avoid survey fraud.
This is done through four key areas:
1. Selection of providers
Partnering with sample providers that can assure a quality sample set is key. To do this, Zinklar makes sure that our providers are ESOMAR-compliant during the vetting process. We only work with suppliers that have a demonstrated record of quality over time. Further, Zinklar always requires that they provide a transparent look into their quality process, and ensure that there is visibility into how they recruit clean respondents. At any failure during the quality check process, those panelists are removed. This is something that is taken very seriously, and as evidenced in the CASE study, is one of the most important starting points.
2. Controls within the survey
Throughout the survey, more layers of protection are incorporated. The surveys on the Zinklar platform make use of mobile-exclusive technology. Mobile surveys provide multiple benefits: from a much more representative sample to lower dropout rates. The controls are much more strict, and the length of the surveys is much shorter, which decreases the incidence of undesired respondents. Finally, with shorter surveys, the incentives are much smaller, which ultimately proves less attractive to attempts at cutting corners to get the reward.
3. Onboarding the users
The Zinklar onboarding process provides support in integrating users onto the platform. This includes getting users of the platform fully incorporated into not just how the platform works, but also empowering users to be more aware of possible quality checks they can incorporate to help them be an additional line of defense. This means providing clear guidelines and support about how to implement questions that can validate the quality of the responses, including clarity and attention checks.
4. Monitoring the results
Once the survey is complete, the results themselves provide clues to the next steps to take in the analysis of the veracity of the responses. Zinklar’s panel experts consistently work to monitor the results of survey responses, as well as utilize the platform’s algorithm to detect nonsense responses. This is a multi-point process that detects and removes any activity that acts against high-accuracy results.
With Ribeiro and an expert team on sample quality, Zinklar is continuing to equip its platform with the latest technologies that ensure the data accuracy that brands can rely on.
Learn more about Zinklar’s data quality protection, and see it in action for yourself by getting in contact with a member of our team today.
