Efficient vs Inefficient Market Research Data Processing
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Rim weighting is a technique commonly used to weight market research data to known targets - e.g. age groups, region, gender. The technique will allow you to weight to each variable (question) independently. MRDC can provide you with a free working model in Excel if you email email@example.com.
The purpose of this article is to explain how rim weighting works. Before you read this article, you should be familiar with the principles of target weighting. If you are not, you should read the appendix which explains what target weighing is first.
The article only explains how rim weighting works and some basic checks that you should apply.
If you want a working model in Excel, please email firstname.lastname@example.org and you can receive our free rim weighting calculator.
Rim weighting is special form of a target weighting. It can be a practical tool to use when you have targets (or populations) to which you wish your data for 2 or more variables, but not targets for the interlocking cells for these 2 or more variables.
For example, you may know that your target sample should be:
This is known as ‘rim weighting targets’. You may have more than 2 variables, which is where rim weighting is likely to be your chosen method. However, read the notes in section 4, as you should not see this as a panacea for all your sampling errors.
If you do not know the percentages or figures for the interlocking cells, you will not be able to use ‘standard’ target weighing. To use ‘standard’ target weighting, you would need to know the following, for example:
Generally, the targets for interlocking cells are better than rim weighting. However, where there a number of variables comprising the targets or where many items within each variable, ‘standard’ target weighting may be inappropriate or impossible to apply.
Rim weighting works by what is known as an iterative target weighting process. In other words, the software (assuming it is capable of performing rim weighting) will calculate targets for the first rim. In the example, above, this would apply weighting factors that would achieve 50% males and 50% females.
After applying this weighting factor, it is high improbable that the targets of 40% for 16-34 year olds and 60% for 35+ year olds would be achieved.
The software would, therefore, calculate a multiplicative weight that would adjust the data so that 40% for 16-34 year olds and 60% for 35+ year olds is achieved. The application of this multiplicative factor would almost certainly mean that the targets for males and females would no longer equate to 50% each.
Now, the iterative process begins. The software would now apply another multiplicative factor, so that the gender was weighted to 50% males, 50% females. Then, it would re-weight to 40% 16-34 year olds and 60% 35+ etc etc.
As the program performs the iterations, the data gets closer and closer to the targets. In some cases, it may be impossible to reach the exact targets. Most software programs that can handle rim weighting will have a fixed number of iterations that it will attempt before it gives up. In some cases, due to the structure of your sample and the laws of mathematics, it may not be able to achieve your desired targets; in some cases it may be completely impossible as your sample is so far skewed or biased to be able to reach the targets you are seeking. There are some notes in Section 4 about this.
Where there are more than 2 variables being used as rim weighting targets, the iteration process will pass through each of the variables in turn before it starts again at the first one.
There are checks and cautions that you should consider (in no particular order of importance):
Need a working example?
Just email email@example.com and he will send you a working example in Excel that you can use to calculate rim weights yourself.