Why do probability panels need fewer panel members compared to access panels for research projects?

At Lifepanel we have discussions with different parties that have an interest in our probability panels. One of the questions we keep getting is related to the size of our panel. 

How come your panel size is only 3500-4000 panel members per country whereas other access panel panel providers boast numbers of 100.000s and beyond?

When discussing probability panels and access panels in research, it’s important to understand the distinction between the two. A probability panel is a type of survey panel that is created by randomly selecting participants from a larger population, ensuring that every individual in the population has a known and non-zero chance of being included. This method aims to create a sample that is representative of the overall population and not targeting any specific subpopulations. On the other hand, an access panel is a database of individuals who have agreed to be contacted for research purposes. These panels can be large and diverse, with members typically pre-qualified or self-selected based on specific criteria. They can be used for a variety of research activities, including studies, surveys, product testing, and more. One of the great advantages of access panels is the ability to sample niche audiences sufficiently as well due to the large panel size. 

Here are some reasons why probability panels need fewer panel members compared to access panels:

  1. Statistical Representation: Probability panels are designed to be statistically representative of the target population. Because each member is randomly selected, the characteristics of the population are more accurately reflected within a smaller sample size. In contrast, access panels may suffer from self-selection bias, as individuals who choose to join such panels might not represent the broader population adequately. There is no need for having to oversample specific quotas since no quotas exist in probability panels. Any demographic skews will be accounted for using weighting. 
  2. Reduced Bias: With random selection, probability panels minimise various forms of sampling bias. This means that the findings from a smaller, more accurately constructed sample can be generalised to the entire population with greater confidence than findings from a similarly sized non-random sample from an access panel.
  3. Higher Data Quality: Researchers using probability panels aim for high-quality data that can be extrapolated to the general population. Because each participant is chosen based on probability, less weighting and fewer adjustments are required to correct for over- or under-representation of certain groups within the sample.
  4. Lower Margin of Error: In probability sampling, as long as the sample is randomly drawn and adequately sized, the results will have a predictable margin of error. This allows for confident assertions about the population with fewer participants compared to non-probability samples (like those from access panels), which may require larger sample sizes to achieve similar levels of precision due to potential biases.
  5. Cost Efficiency: Conducting research with a probability panel can be more cost-effective because it requires fewer participants to achieve reliable results. This can be particularly beneficial for studies with limited budgets where researchers still need to make strong inferences about the broader population.
  6. Regulatory and Academic Acceptance: For certain types of academic or regulatory research, probability samples may be required because they provide a level of rigour that non-probability samples cannot match. In these cases, researchers must use probability sampling even if it means working with fewer participants than they would with an access panel.
  7. NatRep studies versus Sup-Populations: Access Panels are often used to sample specific parts of the populations such as specific niches. On the other hand, NatRep studies only require a factor 3-4 versus the total desired targeted size of required interviews.
  8. Sampling frequency: Most probability panels limit the number of surveys for their panel members to only 1-2 per month while having only 1-2 projects at max per month.
  9. Selection of projects: In order to provide a high level of data accuracy, probability panel providers like Lifepanel do not accept any kind of research for their panel members and often reject lengthy or irrelevant surveys. Generally speaking, only NatRep or close to NatRep surveys are used.

It’s worth noting that while probability panels have these advantages in terms of representativeness and reduced bias, they also come with challenges such as potentially higher initial setup costs and difficulties in reaching certain populations (e.g., those without stable addresses or internet access). Access panels can offer greater convenience and faster turnaround times but require larger sample sizes to mitigate potential biases and ensure that the data collected can be generalised to the broader population.

To conclude, most European probability panels currently do not focus on any sub-population, do not allow sample-only to ensure a high engagement rate for their panellists, nor do they accept low incidence rate projects that can lead to high panel attrition.

At Lifepanel we apply the same quality standard to our surveys with a limit to length of the survey, ensuring mobile-first principles and ideally self-hosted surveys rather than sample-only. Our Panel sizes allow in most markets to realise N=1000 with up to N=2000 in some of the core markets.

Carsten Broich
Carsten Broich
Carsten is the founder of Sample Solutions and Lifepanel with over a decade of sampling and social research experience. A trained aerospace engineer who discovered his love for random phone numbers.