Taking Sample Blending to the Next Level
Want to know a dirty secret in the online sample world? Sample panel bias exists, and it impacts the results of studies on a daily basis.
This is because online sample panels are different from each other, and they change over time. This can mean major challenges for researchers, especially if they are using a single panel on their study.
Normally, combining multiple sources is the best way to overcome this, but not all techniques for combining multiple panel providers are created equal.
Blending shouldn’t be done just for blending’s sake. It should be done in a strategic manner. Customizing a blend based on a client’s needs will ensure the best results possible.
That’s why you need Strategic Sample Blending
Sample Bias Problems
Single Source Bias
Running Out of Sample
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Different Ways to Combine Sample
What is Strategic Sample Blending?
Strategic sample blending takes sample blending to the next level and is the best sample design to ensure confident business decisions. It is blending three or more sample providers, but the selection and blending of the selected providers is done in an intentional and controlled manner. Providers are selected to complement one another, while reducing the overall sample bias and any potential behavior or attitudinal impacts a panel can have. This method ensures that sample blending isn’t done simply for blending’s sake. Utilizing our strategic methodology, we build customized blends that best meet clients’ needs while ensuring the best results possible.
Additionally, by strategically selecting providers and managing their allocation, you increase overall feasibility while avoiding “top-up” situations, and panel bias, both of which can skew your data.
Benefits of Strategic Sample Blending
When you use a single source for all your sample, your feasibility is limited to that of your selected source. This can be very problematic, especially if your study needs a large number of completes or is a tracking or wave study and needs completes on a frequent basis.
Some think that by aggregating or stacking panels that they get around the feasibility problem of a single source – and you do – to an extent. Feasibility is still a problem as the entire reason you are stacking panels is because the main sample source you engaged didn’t have enough overall feasibility. Because of that, you are trying to make up the shortfall by throwing all panels on the study to get the completes you need. While this does solve the feasibility problem when talking about completes, it causes a different problem – one that sample blending solves from the start.
When you throw a bunch of different sample providers on at the end of a study to get the remaining completes your original partner was unable to get due to insufficient feasibility, it can cause your data to change.
This is especially evident if you see a sudden spike or a sudden drop in your results. If you stacked panels to get the needed completes, how can you be sure that your results are because of the efforts your organization took or the true feelings of your target audience, and hasn’t been biased by one of the panels you added to get more completes? That makes a market researcher’s job a lot harder when trying to defend the data to their client.
Strategically blending sample can ensure that you maintain the consistency of your data. This is because, with strategic blending, you build out a plan that utilizes 3 or more different panels and distributes the number of completes across the panels, so that you are only using a fraction of their overall feasibility. You also build in back-ups at the start so that if any of the panels were to fall short, you have already strategically selected a partner to fill in the gaps.
Another benefit of strategic sample blending that goes hand-in-hand with maintaining data consistency is reducing risk. By using a single source of sample, you are putting all your eggs in a single basket, hoping that it will all work out. The risk you incur with this strategy is two-fold – first, you risk your data consistency if you end up having to bring on additional panels if you fall short.
Second, and probably more importantly, you risk incorporating sample bias into your results. Sample bias is the potential data bias you can introduce by using a specific sample panel for all or a vast majority of the completes.
Sample blending reduces your risk by ensuring that no single panel will account for 50% or more of your study’s completes, which not only reduces the risk of inconsistent data, but also the risk of sample bias.
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Faster Fielding Times
Finally, strategic sample blending improves your field timing. By engaging multiple sample panels at the same time for only a fraction of their overall feasibility, fielding takes less time. With using a single source or stacking, you are waiting to get your completes from a single source, and then spending additional time essentially re-fielding when you have to bring on additional panels to get the remaining completes. This can extend your field time by several days – days that are invaluable to your clients.