Of all the techniques used in quantitative market research, weighting can be one of the most useful…and one the trickiest to apply properly. Among the various weighting approaches available, rim weighting is an especially valuable addition to the researcher’s toolbox.

The “rim” in rim weighting comes from the acronym for Random Iterative Method. The name may sound complex, but like any kind of weighting, it’s a solution to a fairly straightforward problem – the need to adjust a sample so that it is representative of the target population. This need arises frequently in market research cases where low response to a survey among certain segments leads to a dataset that is not representative of known population characteristics. For example, if a researcher knows that her target population is split evenly among gender lines, yet 65% of the survey responses are from women, she may need to use weighting during the analysis to allow for the skewed response pattern.

Of course, only having to worry about the proportionality of just one characteristic is easy. Unfortunately, we often have to ensure that our data matches the population in a variety of ways – not just gender, but also age, income and any number of other traits.  That’s where rim weighting comes in. The technique allows the analyst to adjust multiple characteristics in a dataset all at the same time in a way that it ultimately keeps the different characteristics proportionate as a whole.

To use a very simple example, let’s say that we know that a target population of college students is divided as follows in terms of gender and age distribution:

 Male 40% Female 60%

 18-24 70% 25-34 25% 35 or older 5%

Now, let’s say that we conduct a student survey, and the demographics of our respondents look like this:

 Male 30% Female 70%

 18-24 65% 25-34 20% 35 or older 15%

When we analyze the results, we know that we want to use weighting based on the known distributions of the student population. Rim weighting allows us to weight both characteristics at the same time. It does this by using an algorithm that distorts each variable as little as possible. The ultimate result is a weighted data that closely matches the target population across all the pre-defined dimensions.

Rim weighting is useful when you know some characteristics of your target population, but you aren’t sure about the relationship between them. In the case of the example above, we knew that 40% of our population were males and 5% were 35 or older, but we didn’t know what percentage of the population were males who were also 35 or older.  By making adjustments to multiple characteristics at the same time, rim weighting infers that information for us.

That last point is important, because it shows one of the limitations of rim weighting. If there is a strong relationship between two characteristics, for example household size and marital status, rim weighting based on those characteristics would probably produce an inaccurate result.  Rim weighting is also most effective when the actual values don’t differ a great deal from the target values. (That is true of weighting in general, as we have written about here.)  In the example of our student survey, if the respondent pool was 5% male and 95% female, rim weighting would not produce as accurate of an analysis.

Keeping those limitations in mind, rim weighting is an extremely valuable analysis tool to market researchers in the right situation.

Research & Marketing Strategies (RMS) is a market research firm located in Syracuse, NY. If you are interested in learning how we can help you turn data into actionable insights, contact our Business Development Director Sandy Baker at SandyB@RMSresults.com or by calling 1-866-567-5422.