Weighting Your Survey Data: What You Need to Know and How to Do It Right

Weighting Your Survey Data: What You Need to Know and How to Do It Right


This blog article will go into the topic of data weighting and explain why it is so important in the research process. To help you better grasp survey data weighting, we’ll start by defining it and outlining how Survalyzer uses it. You can find out more about the distinctions between weighted and unweighted data in this article. We’ll also go through the advantages and disadvantages of data weighting and explain how it might provide you more control over the survey data you collect.


It is a fundamental objective of almost all research studies to understand how a population thinks, behaves, or responds. There are instances when the population of interest is limited to a specific group of respondents, such as your current customers or members of a panel. Other times, the sample should represent a larger population. And if your sample isn’t representative of your desired population, weighting data may be an appropriate way to measure it.

What is survey data weighting?

Survey data weighting is a statistical technique used in market research to adjust survey results to accurately represent the target population. It involves assigning different weights to different responses based on certain characteristics like age, gender, ethnicity, etc. To ensure that the sample accurately reflects the larger population. This is done to minimize bias and increase survey results reliability and validity of survey results.

When is data weighting useful?

Weighting compensates for differences in survey respondents’ selection probability and non-response rates. These differences may be influenced by demographic factors such as age, gender, education, income, and location. In situations of non-response bias, over- or under-sampling of particular groups, or both, it is recommended to weight the data.

The impact of non-weighted data on analysis and results

Non-weighted data can lead to inaccurate analyses. Biases can occur due to unequal demographic representation, which affects results accuracy. The impact of non-weighted data can be seen in the difference between sample data and population data. If the sample data does not accurately represent the population, the results and analysis will be biased, leading to incorrect conclusions and decision-making.

How does survey data weighting work in detail

The first step in conducting a successful research study is to start with a specific use case. In this article, we will focus on a use case for data collection where the objective is to understand the perception of the general population towards a specific topic, such as climate change perception.

1. Data collection

To collect data for this study, the first thing we need to do is to formulate a question. In this case, the question is: “To what extent do you consider climate change a threat? Please indicate.” Once we have the question, we can proceed to collect demographic data such as gender, age, education, and region.

For gender, we can collect data on:

  • women,
  • men,
  • and diverse.

The term “divers” is often used as a catch-all category for individuals who identify as non-binary or gender non-conforming. This category can be complex and may require careful consideration when it comes to weighting data. It is important to ensure that all respondents are properly represented in the final sample and that their responses are not being inadvertently skewed by the weighting process

For age, we should check the age categories in the relevant case, such as:

  • 18-24 years old
  • 25-34 years old
  • 35-44 years old
  • 45-54 years old
  • 55-64 years old

Education is also an important demographic factor to consider. Some common categories include:

  • No formal education
  • Primary education
  • Secondary education
  • Bachelor’s degree
  • Master’s degree
  • Doctorate degree

These categories can provide a clear understanding of the education levels of the respondents and how that may affect their responses to certain questions. It is crucial to consider the survey context and purpose when selecting the appropriate categories.

Region is another critical demographic factor that should be included in the study. In this example, we can take the Dutch provinces as the region of interest.

All these factors can influence respondent selection and non-response rates. The characteristics of the respondent, such as their attitudes and behaviours, can also be used for weighting.

2. Craft Actual Distribution Table (collected data)

After collecting the data, we create the actual distribution table, which shows the distribution of responses by each demographic group.

Demographic groupPercentage
Unmarried male18.5%
Unmarried female18.7%
Married male21.2%
Married female20.9%
Widowed man2.3%
Widowed woman3.1%
Divorced man4.7%
Divorced woman6.6%

3. Look At Expected Distribution Table (real population data)

An expected distribution table shows how the distribution of survey responses would appear if the sample were completely representative of the population. This kind of table is compared to the actual distribution table as a benchmark to see if there are any notable changes that would suggest bias in the survey sample.

Demographic groupPercentage
Unmarried male20%
Unmarried female16.9%
Married male23.8%
Married female23.7%
Widowed man1.4%
Widowed woman4.7%
Divorced man4.1%
Divorced woman5.4%

If you would like to look at expected distribution tables, what are the sources you can use? 

Destatis, BFS, and CBS are the authorized statistical institutions of Germany, Switzerland, and the Netherlands correspondingly, which can be immensely beneficial when weighting data in market research, owing to their reliable and comprehensive demographic data on the population of these nations.

These sites provide detailed information on the characteristics of distinct demographic groups like age, gender, education, and income, which researchers can use to guarantee that the sample in a survey represents the target population and rectify any biases that may arise due to oversampling or undersampling.

4. Compare and calculate weighting factors

In this example, we noticed an oversampling of females and respondents from one region, which can lead to biased results. To correct this we can then use weighting to adjust the data so that it reflects the actual population distribution, ensuring that the results are more representative of the entire population.

Using the data from the previous table, let’s say you want to weight the data for gender and marital status. First, you would calculate the weights for each demographic group by dividing the expected percentage by the actual percentage:


Demographic GroupExpected DistributionActual DistributionWeighting Factor
Unmarried Male20%18.5%1.08
Unmarried Female16.9%18.7%0.90
Married Male23.8%21.2%1.12
Married Female23.7%20.9%1.14
Widowed Man1.4%2.3%0.61
Widowed Woman4.7%3.1%1.52
Divorced Man4.1%4.7%0.87
Divorced Woman5.4%6.6% 0.82


Next, you would apply these weights to the survey data. For example, if there were 100 unmarried males in the survey, you would multiply that number by 1.08 to get the weighted count for that group. You would repeat this process for each demographic group.

Pros and Cons of Data Weighting

Survey data using as any statistical method has both advantages and disadvantages which need to be considered before using it. This chapter provides you with an overview of the pros and cons of data weighting, so you could decide when is the right time to implement it.


Helps to correct for non-response bias, resulting in accuracy
Ensures greater representation of underrepresented groups
Enables reliable and robust statistical analysis and modeling
Aligns sample data with known population data
Improves the overall quality and validity of research findings


Time-consuming and expensive to implement, especially when done manually
Can introduce additional complexity and error into the data analysis process
May require assumptions to be made about the underlying population, which could result in incorrect weightings
May not be appropriate or effective in all research contexts, particularly in cases where a simple random sample is appropriate
Results may be more difficult to interpret or communicate to stakeholders, particularly if the weighting factors are complex or unfamiliar to them

How to use weighting in Survalyzer

When using weighting in Survalyzer, the first step is to create a survey and collect data. Once the data has been collected, the next step is to create a weighting definition or set weights for the survey. This is done by defining weighting variables, such as age, gender, income, and education, and assigning weights to each variable. We’ve explained everything in our help center article about weighting solution.

After the weighting definition is created, it needs to be uploaded to Survalyzer. After uploading, the next step is to close the survey. The calculation process is triggered when the survey is closed, and weighted data is generated. It is important to note that the weighting process in Survalyzer is flexible, allowing users to adjust the weights as needed and recalculate the data at any time. By following these steps, researchers can ensure that their survey data is properly weighted, providing accurate and representative insights.

In Survalyzer, data weighting is available exclusively in the Professional Analytics product, which offers advanced features for survey data analysis.

Uploading a weighting definition to Survalyzer


Overall, data weighting is an effective technique in market research to make sure results are accurate. It’s a complicated process, but when done right, it ensures the survey results reflect the target audience. Survey data should be weighted based on factors like demographics, respondent characteristics, and sampling methods. Remember that by using reliable sources of actual population data in the survey, you can ensure the sample represents the target population as well as you can correct any bias caused by over- or undersampling.

Christian Hyka

Managing Partner
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