A chart/table could previously use one weight variable in the calculations only but now you can apply different weight variables when applying filter compares, each compare series can will then be calculated with a unique weight variable. 

Here we see an example, the chart displays the awareness result for Dapresy Telecom for four different segments. The Male and Female result is based on a “Gender weight” while the Young and Old result is based on an “Age group weight”.

To apply a unique weight variable to each compare series the setup is done in two steps;

  1. Connect the weight variables to the different Filter options in the administration screen Filter Vs Weights (see image 1 below)
  2. In the chart and table setup, select to apply the weight of the compare filters, this option appears only for the Filters connected to weight variables in step 1 above if these are applied as Filter Compares in the chart/table (see image 2 below)

Here we see the screen for connecting Weight variables to the filter options

Here we see the new Weight option in charts and tables, it appears for filters connected to weight variables when they are applied as compare filters. As the Weight option is ticked, in this example, the weight variables used in each compare series will be the variables connected to each Segment option. 

Note: as each respondent can have 1 weight per compare series you cannot apply the weight from two or more compare filters simultaneously as that would result in more than one weight per respondent and series. In these scenarios the weight option becomes disabled for the additional compare filters as soon as it has been ticked in one of these, see example of this logic in the image below.

Here we see an example of multiple filters connected to weight variables, as only one of these can be applied the Weight option in the Prepaid or Postpaid filter becomes disabled as it has been ticked in the Segment filter.  



Note: if the filter compares are not nested you are able to apply the weights from multiple compare Filters as that is not resulting in more than one weight per respondent and series.

Here we see an example of non-nested compare filters, in these scenarios you can apply weights from more than one Filter variable.