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Weighting your data can adjust for a fluctuation in respondents, such as a significant increase in responses from a certain age group. For example, the data might have shown that the overall awareness of your brand has increased. However, this increase may be due to a recent sample composition change, such as a higher percent of respondents in the 18-24 age group, not an actual increase in awareness. You can use weighting to correct fluctuation in your study participants so any metric changes are due to meaningful market trends.
To start creating weights for your data:
Enter a name for your weight. Use a descriptive name that explains what variables are included in the weight (for example, “US Sample weights by age and gender”). This helps you understand what data the weight will affect when you apply it later.
Choose any combination of target audiences to apply the weights to. For example, if you run your study in multiple countries, you can choose to create weights for each country separately or create weights for all countries combined.
TIP! It’s best to create separate weights for distinct target audiences. If you run your study in multiple countries, each country has a distinct population. Creating weights for all countries combined can make your results less accurate.
Choose the variables in your study, such as age and gender, to weigh your study by. Once you choose variables, you’ll enter the percentages to weight the variables by.
The variables you choose depend on the goal of your study and the industry it’s based in. If you’re targeting consumers, you might want to focus on demographic data like gender. If you’re targeting businesses, you might want to include industry, job level, or department.
Enter the percentages to weight each variable by. The percentages across all variables must add up to 100%.
There are a few tips you can use to choose the target percentages.
When you’re done, select Next.
Weight trimming looks at the difference between your largest and smallest weights to make sure they don’t vary too much. If the variation in your weights is too big, we’ll trim your weights to make the data more accurate. Trimming will apply to future waves of data.
When you’re ready, select Generate Weight to create your weight.
Once you create your weights, you can apply them to your data. Your data will update after applying the weights.
You can apply multiple weights to your data if the weights are created for different target audiences, such as a US sample and UK sample. Applying multiple weights allows you to view and compare weighted data across multiple distinct target audiences. However, if the target audiences for multiple weights overlap, they can't be applied together. For example, if a target audience has a weight based on age and gender and another based on age and industry, you can’t apply both weights.
To apply weights to your data:
We’ll attempt to use the same percentages you set up previously when new waves of data come in. As long as you consistently collect data for the variables you chose to weight by, we’ll create weights for future waves.