Learn how weighting is applied to your report data.
Random Iterative Method (RIM) weighting is used to calculate the weight factors that are applied to adjust your data when you create a weighting scheme with one or more variables. This process is also commonly referred to as Raking or Iterative Proportional Fitting.
When RIM weighting is applied, it iteratively adjusts the weights of respondents step-by-step until the sample matches the target population for each variable in the weighting scheme. The algorithm applies the following processing to the survey data:
- It adjusts the weights for one variable at a time, so that the sample distribution matches the population target for that variable.
- After adjusting one variable, it adjusts the next variable, and continues to iterate through this process for each variable.
- The iterative adjustment of each variable in turn continues until the sample matches the target proportions for all variables simultaneously, within an acceptable margin of error. This point when all variables closely match the target proportions is called convergence.
- In multi-variable weighting, the last variable is weighted most accurately, but the difference in weighting accuracy between the first and last variable is minimal.
- In certain cases (for example, when the variables are too closely related to each other), it may not be possible to produce a weighting scheme.
RIM weighting for gender and age example
You have conducted a survey and you want your sample to match the known population proportions for age within each gender.
| Survey response proportions | Target population proportions |
|---|---|
|
The results of your survey include the following percentages:
|
The desired percentages after weighting are the following:
|
The distribution of the unweighted survey responses includes the following values:
| Gender | Age | Unweighted responses |
|---|---|---|
| Male | 18-34 | 20 |
| Male | 35-54 | 16 |
| Male | 55+ | 14 |
| Female | 18-34 | 14 |
| Female | 35-54 | 10 |
| Female | 55+ | 10 |
The RIM weighting algorithm calculates the following adjustments by dividing the target population proportions by the survey proportions:
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Iteration 1: Adjust the Gender variable
Adjust the weight factors to match the gender targets:
-
Males are overrepresented by 10%, so their weight is adjusted down. The weight of each male response is reduced.
Male weight = 0.5/0.6 = 0.83 -
Females are underrepresented by 10%, so their weight is adjusted up. The weight of each female response is increased.
Female weight = 0.5/0.4 = 1.25
In iteration 1 the unweighted responses are multiplied by the Gender weight factors:
Gender Age Weighting Calculations Weighted Count Male 18-34 20 * 0.83 = 16.6 16.6 Male 35-54 16 * 0.83 = 13.28 13.28 Male 55+ 14 * 0.83 = 11.62 11.62 Female 18-34 14 * 1.25 = 17.5 17.5 Female 35-54 10 * 1.25 = 12.5 12.5 Female 55+ 10 * 1.25 = 12.5 12.5 The Weighted Count for the Male entries increases, and it decreases for the Female entries.
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Iteration 2: Adjust the Age variable
Adjust the weight factors to match the age targets, using the current weights from the gender adjustment:
-
Reduce the weight of the 18-34 group.
18-34 weight = 0.30/0.40 = 0.75 -
Increase the weight of the 35-54 group.
35-54 weight = 0.40/0.31 = 1.29 -
Increase the weight of the 55+ group.
55+ weight = 0.30/0.29 = 1.03
In iteration 2 the previously calculated weighted response values for Gender are multiplied by the Age weight factors:
Gender Age Weighting Calculations Weighted Count Male 18-34 16.6 * 0.75 = 12.45 12.45 Male 35-54 13.28 * 1.29 = 17.13 17.13 Male 55+ 11.62 * 1.03 = 11.97 11.97 Female 18-34 17.5 * 0.75 = 13.13 13.13 Female 35-54 12.5 * 1.29 = 16.12 16.12 Female 55+ 12.5 * 1.03 = 12.88 12.88 The values in the Weighting Calculations column are the values calculated in iteration 1 multiplied by the Age weight factors.
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Iteration 3: Re-adjust the gender variable
Repeat the adjustment for gender using the updated weight factors calculated for the Age variable.
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Iteration n: Re-adjust age and gender until convergence
Continue to adjust each variable in turn using the weight factors calculated for the previous variable until the sample matches the target proportions within an acceptable margin of error.
The final set of weight factors can be downloaded using the Export Weight Factor .XLSX link. The output includes the final weight factor for each combination of variables (e.g. Male 18-34, Female 55+, etc.) after all of the iterations have completed and convergence is reached.