Summary |
This article describes the weighting algorithm used in askiaanalyse. |

Applies to |
askiaanalyse |

Written for |
Data processor |

Keywords |
weighting; weight; algorithm; analyse; askiaanalyse |

*Documentation Note : merge with http://analysishelp.askia.com/weighting_analyse*

**Definition**

The process of weighting involves changing the weight of the interviews to correct sampling errors.

Here is an example: we interviewed 60 men and 40 women. The population we want to describe is equally composed of men and women. To correct this, each time we run counts on a question, instead of adding 1 for each man, we will add 50/60 = 0.8333, and for each woman we will add 1.25. In other words, the under represented categories will have a stronger vote than the over represented ones.

**Multiple targets**

The process gets more complicated when more than one variable is involved. Let us imagine we want to apply a weighting on gender and social grade (A,B,C1). If we have the information on how the gender is defined for each social grade (and hence creating a new variable with 6 cells), we can fall back on the one variable weighting. But if we increase the number of questions and responses in the weighting, the target population frequency in each of the sub-cells may not be known.

Another alternative would be to assume that gender and social grade are independent and therefore we should observe on each social grade a 50-50 split on gender. This hypothesis is very restrictive and would very likely skew the results. Therefore the algorithm is an iterative process.

- First the weights are set to 1 for all interviews.
- The system finds which variable is the furthest from the targets.
- The current weights are changed so this variable will exactly fit the targets
- The minimum and maximum weights are applied if they are defined
- If all variables fit the targets then the process will stop otherwise it will start again at b)

After a few iterations the weights should converge to an acceptable solution.

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