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Rebase data by adjusting the sample (base) used in your calculation
Rebase data by adjusting the sample (base) used in your calculation
Updated over a week ago

Rebasing is a filter that can be applied to your data, helping to modify your results by filtering out irrelevant or unrelated responses and adjusting to your screening criteria. This is done by altering the base (sample) used in the calculation.

Info! Rebasing of questions with missing values can instead be automatically handled using sysmis.

Here is an example of rebasing case

Consider a survey with 10,000 respondents that has a question about gaming, e.g ‘Have you played any video games in the last 12 months?’.

If half of the respondents (5,000) answered 'No' to this question, the following frequency of gaming question - ‘How often do you do each of the following?’ - will only be served to those who answered 'Yes', as the question is irrelevant to the respondents who do not play video games.

When served this question, any further calculations should be based on only those who answered 'Yes' to the original question.

Here is example calculations:

We have a total sample of 10,000 in a survey. 5,000 respondents answered 'Yes' to whether or not they had played video games in the past 12 months, and 500 of these said they play video games on social networking sites (at least once a week).

After the rebasing the percentage calculation formula will be 500/5000 x 100 = 10%. Here we get % of people who play games on social network sites is calculated from the people who actually played video games.

Without rebasing the formula is 500/10000 x 100 = 5%, and we would conclude that only 5% of respondents play games on social network sites, that is incorrect.

Therefore, rebasing is important as it gives you accurate results. It excludes the irrelevant data (for example, people who aren't asked a question, or blank data), and only uses those who have answered a question to calculate percentages from.

Note! Rebasing does not delete your data or information, it simply ignores it and treats the rest of the data as a subgroup when calculating percentages.

DataTile provides two key types of rebase feature:

  • Manual - a filter defined for each characteristic or for a group of characteristics individually.

  • Automatic - data is automatically rebased, based on selected categories that match a chosen feature name.

Manual rebasing

You can manually rebase your data through two different methods, the logical expression builder, or coding in the grid.

To rebase using the logical expression:

  1. Hover the expression builder icon which will appear within your columns or rows.

  2. On the right side of the expression builder window there is a base tab that can be enlarged by clicking on the arrow.

  3. Drag&drop the variables you want to rebase into the base tab.

  4. Click SAVE.

To rebase several features at once:

  1. Select the options within the rows/columns that you want to rebase.

  2. Drag the variable to rebase your selected rows/columns with into your table, and drop into the 'all items' field marked with *.

  3. In the pop up box select rebase.

  4. The attributes that are now rebased within your table will be underlined.

Automatic rebasing

You can also automatically rebase your data using DataTile’s name matching algorithm in following steps:

  1. Select the variable you want to rebase with and drag it across to your table, dropping it into the 'all items' field marked with *.

  2. In the pop up select ‘Rebase: match categories with labels’.

  3. DataTile automatically compares options' names in the crosstab, and attach the rebase to matched rows/columns.

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