Data weighting

Note: Data weighting is only available for Professional Analtyics users

When to apply data weighting?

Data weighting is particularly useful in situations where certain groups are overrepresented or underrepresented in your initial sample composition. By applying weights post-data collection, you can correct for any biases that may have occurred during the data collection process. This allows you to work with representative data.

Data weighting is usually applied to demographic characteristics such as age and gender. These fields will be used in the example below to match the sample data to the Dutch population.

Representation demographic characteristics

Survey SampleDutch Population
Representation of Gender
Survey SampleDutch Population
< 20 years old10%21%
20 – 39 years old20%26%
40 – 64 years old50%33%
65 – 79 years old18%15%
> 80 years old2%5%
Representation of Age

In this example there is an overrepresentation of male and divers respondents and an underrepresentation of young (<39 years old) and elderly (>65 years old) respondents.

Download weighting data definition template in order to follow with the next steps on this manual

How to apply data weighting?

To make sure your collected data is a good representation of the Dutch population the desired weight is uploaded using an Excel file (example file is attached below). The file always contains three columns.

A. Preparing the Excel File

This column contains the variable names that you defined in your survey:

Note: In order to work with custom variables, “custom” prefix must be added in weighting template, for example, custom.age.

This column contains the values of the variables in your survey:

This column contains the values from the age distribution column of the Dutch population.

Note: Only use the integer number excluding the percentage symbol (%):

In this example where two demographic characteristics (age & gender) are used this results in the following Excel format:

B. Uploading the Excel file

Navigate to the ‘Analyze’ tab of your survey and check the ‘Interviews weighting’ box to upload your Excel file:

Your Excel file will be validated based on the following:

  • Weight can be specified with integer numbers or as fractions;
  • The variable name must exist;
  • If the variable is based on a question with a defined range of values (e.g. single response question) the defined value must be in the range;
  • The weights for one variable must sum up to 100.

If the Excel file is validated the interviews will be weighted when the survey is closed:

Note: The closing of the survey triggers the weighting of the interviews.

C.Analyzing the results

When the survey is closed you can analyze the results. The analysis of weighted interviews is only possible in Pro-reports.

For instance in this example it is shown how many males and females actually rated the service with 5 stars (Count) and what result would be a representative display of the Dutch Population:

To get the Weighted Count, the basis variable of the column needs to be the custom variable InterviewWeight, the aggregator needs to be sum:

To receive the correct values, a filter on the question needs to be set:

The basis variable of the row is set to be take from column and the filter needs to be set in a way, that only the requested answer possibilities are selected (this is the same procedure for not weighted results).

Within Professional Analytics the options to display and compare the data are practically limitless.

What happens if I upload an incorrect file?
In this example the sum of the weight for the gender variable is exceeded (103%) and a non-existent variable value for the age variable was added (6). This will result in an error message and allow you to download the error report:
In the error report the rows that are causing an issue are highlighted and the reason for the error is shown:
Updated on January 31, 2024

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