The Role of Weighting in Creating a Better Representation of Opinion

Population Weighting: Part 1

Have you ever wondered how we get a clear picture of national opinions, or what a nationally representative survey sample is? With so many voices and perspectives, it can be challenging to accurately represent what the majority of people really think. But where you aware that statistical weighting plays a crucial role in this process?

In this blog post, we'll explore the importance of weighting in data sets of surveys and how it helps us move from bias towards balance when creating a better picture of national opinions. This is an overview of weighting, and why it is necessary, rather than a technical lesson on statistics so please read on!

Introduction to Weighting nationally representative samples for surveys

When we talk about understanding public opinion, we are usually referring to opinion surveys. Surveys are a common way to collect data on what people think about issues, trends, politics, and more. However, surveys can be biased if they do not properly account for the population they are trying to represent. This is where weighting comes in. Weighting is a process of adjusting data to account for bias and make the results more representative of the population as a whole.

There are many ways to weight data, but the most common method is to use demographic information such as age, gender, region, and education level. This information is used to create weighting factors that are applied to the survey data. The weighted data is then used to produce better estimates of what the general population thinks.

Weighting is an important tool for correcting bias in surveys, but it is not perfect. Some forms of bias, such as self-selection bias, cannot be corrected with weighting. Additionally, weighting factors are often based on assumptions about the population that may not be accurate. Despite these limitations, weighting is still a valuable tool for improving the accuracy of survey data.

The Benefits of Weighting National Samples

There are many benefits to weighting national samples when conducting research. Weighting ensures that the sample is representative of the population, which is important for validity and generalizability. Additionally, weighting can help to account for any non-response bias that may be present in the data. Finally, weighting can also help to reduce variability in the data, which makes it easier to draw reliable conclusions from the research findings.

Common Biases When Collecting Data and How Weighting Can Help

There are many common biases that can occur when collecting data, which can lead to an inaccurate picture of public opinion. Weighting is a statistical technique that can be used to adjust for these biases and create a more accurate representation of national opinion.

Common biases include self-selection bias, response bias, and selection bias. Self-selection bias occurs when people who are more likely to respond to a survey are also more likely to have certain characteristics (such as being older, wealthier, or more educated). Response bias occurs when people who do respond to a survey tend to give answers that are not representative of the population as a whole (for example, if they have strong opinions on the issue being surveyed). Selection bias occurs when the people who are selected to participate in a survey are not representative of the population as a whole (for example, if they are selected based on their age, race, or gender).

Weighting can help correct for these biases by adjusting the data to make it more representative of the population as a whole. For example, if older people are underrepresented in a survey, the data can be weighted so that they make up a larger proportion of the total. This will create a more accurate picture of national opinion.

The Impact of Weighting on Representativeness

Weighting is a statistical technique that can be used to adjust for known differences between the characteristics of a sample and the population from which it was drawn. For example, if a survey is conducted among registered voters, but the target population is all adults, the data can be weighted to account for the over-representation of registered voters in the sample.

Weighting can also be used to adjust for unequal probabilities of selection into a sample. For example, if a telephone survey only includes landline telephone numbers, households without landlines would have zero probability of being selected into the sample. To correct for this, data from cellphone-only households can be weighted up so that their contribution to the final results is proportional to their share of the population.

Properly done, weighting can improve the representativeness of a survey by making it more closely reflect the composition of the target population. However, weighting can also introduce new sources of error if it is not done correctly. For example, if data are incorrectly weighted, or if weighting is used to try to compensate for a non-random pattern of responses (such as voluntary response bias), results can be distorted.

Overview of Different Types of Weighting

Different types of weighting are used to adjust for different sources of bias in opinion polls. The most common type of weighting is post-stratification weighting, which adjusts for differences in the composition of the population being surveyed and the general population. Other types of weighting include raking, which adjusts for differences in the responses of different subgroups within the population; calibration, which adjusts for systematic errors in the poll results; and regression, which adjusts for relationships between different variables in the data.

Common Methods for Calculating Weights

There are a number of different ways to calculate weights, but some methods are more common than others. The most common method is to weight by population size, which gives each person in the population an equal weight. Other common methods include weighting by income, age, education, and gender.

Use of RIM Weighting in Polls and Market Research

One of the most common weighting techniques is RIM Weighting (Random Iterative Method). It allows the researcher to look at individual demographics, such as gender, and ensure the sample is balanced to reflect the incidence of that demographic in the sample to match the population.

Potential Challenges in Applying Weighting Techniques

There are several potential challenges that can arise when weighting poll results. First, it can be difficult to identify all of the relevant factors that need to be considered in the weighting process. Second, even if all relevant factors are considered, there is always the potential for error in the weighting calculations. Third, weighting techniques may not be effective in correcting for all sources of bias. Finally, weighted results may be more difficult for laypeople to interpret and understand than unweighted results.

Weighting Efficiency
A key issue in the use of weighting, whether it is RIM weighting, or other techniques is weighting efficiency. When the weight is calculated, a weighting efficiency measure is given expressed as a %. Ideally the weighting efficiency should be 80% or more. If it drops significantly below 60-70% there is a much higher mismatch between sample and population, and it may be necessary to reconsider the analysis design and weights used.


Weighting has the potential to revolutionize how we use survey data to paint a picture of national opinions. The importance of weighting cannot be overstated, as it ensures that our surveys accurately reflect the population by accounting for any biases or disparities in sample selection. As more organizations and researchers become aware of this practice and its implications, weighting can be used increasingly effectively to create a better overall representation of public opinion.

 Neil Cary

Lead contact for survey design, project management and data reporting.