RIM weighting stands for Random Iterative Method. RIM weighting is a form of survey weighting the data supplied by market research respondents.
It is a technique commonly used to weigh market research data to certain demographics such as age, gender, geography, income, etc. RIM weighting becomes more challenging and complex when weighing several different demographics, rather than just one.
This blog post will cover why market research analysts use RIM weighting, the benefits of this type of survey weighting, and why it is best to consult a market research company if wanting to use RIM weighting in your survey data.
Why is RIM Weighting Used in Market Research?
RIM weighting allows research analysts to weigh each variable and question as an individual entity to assure each data point and demographic is accurately represented.
For example, let's say a bank or credit union is completing member satisfaction surveys on the phone. The bank or credit union dials through 500 phone numbers, with no idea as to who will complete the survey. A large majority of older men may answer the phone and as a result, very few middle-aged women or people of other demographics will have completed the survey.
As a result, the member satisfaction survey is largely skewed to the feedback provided by older men and is not representative of their entire member base.
On the back end, an online survey company can weigh the data to match the financial institution’s member population to make sure all members and customers are represented evenly.
What are the Benefits of RIM Weighting?
While RIM weighting can involve many steps and iterations to be just right, it can be beneficial for those conducting market research studies.
The main benefit of RIM weighting is the survey data will match the population or customer base you are researching.
When reviewing the survey feedback, researchers are able to review a smaller number of respondents as a true comparison to how a larger demographic of people think and feel.
This form of survey weighing is most commonly used when researchers are recruiting without a screener survey or have accounted for the screening criteria when sampling for respondents.
Additionally, by using RIM weighting, market researchers can reduce the bias in their sample data and improve the accuracy of their findings, allowing them to make more reliable inferences about the target population.
How Do You Calculate RIM Weight?
- Sample Collection: When conducting a market research survey, researchers gather data from a sample of individuals or entities within the target population. The sample is often chosen using various methods like random sampling or quota sampling, depending on the research objectives.
- Population Distributions: To ensure that the survey findings are applicable to the target audience, researchers need to ensure that the sample closely resembles the demographic and other relevant distributions of the actual population.
- Initial Weighting: After data collection, the sample may not fully reflect the characteristics of the population. Some groups might be overrepresented, while others might be underrepresented. Initial weighting is applied to adjust for these imbalances based on the sample's known characteristics.
- RIM Weighting: RIM weighting takes the initial weighting a step further. It is an iterative process that applies multiple rounds of adjustments to the sample data. During each iteration, the weights are updated to bring the sample closer to the population distribution. The process is repeated until convergence is achieved or until a predetermined criterion is met.
- Convergence: Convergence refers to the point where the sample distributions closely match the known population distributions. At this stage, the RIM weighting process stops, and the final weights are applied to the data.
Why Consult a Market Research Firm for RIM Weighting?
With RIM weighting being more complicated in nature, it is recommended to contact a market research firm if you wish to use the RIM weighting data technique. This is because:
RIM weighting involves an advanced analytics tool to perform in the back-end of your survey data which is not possible with most research studies conducted in-house.
Market research analysts understand the correct algorithm to use to not impact the data in a negative way. As with any survey weighting it is important to make sure the weights aren’t too extreme otherwise the feedback from a few respondents can count as a lot more. This results in poor and misrepresented data quality.
Let's go back to the example above of a member satisfaction survey being evenly split among men and women.
After reviewing the data, it appears 50 of the responses are from males and only 10 are from females. It is not as simple as multiplying the feedback of the women by 5 to be even with the male population.
This algorithm is dependent on multiple different factors rather than just looking at gender - an algorithm only commonly used by research analysts.
A way to avoid this misrepresentation, a market research firm like Drive Research would rather take this criteria into account before analyzing the data.
A market research firm would recognize the client needs an even split of female and male responses in their sampling plan to minimize the impacts of the survey weighing.
In this example, instead of using RIM weighting techniques, a market research firm would likely recruit more women to take the survey and close the study for males. Doing so will save the client money as it is a more effective way to collect survey respondents and less time and effort for the market research company.
Frequently Asked Questions About RIM Weighting
What is RIM weighting?
RIM weighting" (Random Iterative Method weighting) is a statistical technique used in market research to adjust sample data to match known population distributions. It is a form of post-stratification weighting that helps researchers make their survey samples more representative of the target population they are trying to study.
What is a good RIM weighting efficiency?
A higher RIM weighting efficiency indicates that the sample data has been effectively adjusted, reducing bias and making it more representative of the target population, resulting in more accurate survey findings. The desired level of efficiency may vary depending on research objectives and context, but researchers generally seek a balance between reducing bias and minimizing potential errors introduced through excessive weighting.
What is an example of RIM weighting?
Let's say you're running a survey in a town with an even male and female population, but a majority of the survey respondents are males. But, this is not an accurate representation of the town’s population. Therefore research data analysts may choose to use RIM weighting to make the female responses count more than the male in order to have a more even split.
Final Thoughts
RIM weighting is an important method that provides useful data on survey populations.
This data can be used to better understand survey responses, and lead to an overall better sense of respondent demographics. What's more, this type of survey weighting technique will allow researchers to grasp the proportional elements of survey responses.
In turn, this can lead to better consumer outreach strategies for the client.
This technique requires specialized knowledge to be useful, as it can become complicated quickly. It's key that weighting is conducted by experts in the field, which is why hiring an outside firm is often necessary.
Contact Our Online Survey Company
Drive Research is a national market research company located in Syracuse, NY. Our team has the knowledge and tools to design a robust market research study, should it be the right fit for your business.
Interested in learning more about our market research services? Reach out through any of the four ways below.
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Emily Rodgers
A SUNY Cortland graduate, Emily has taken her passion for social and content marketing to Drive Research as the Marketing Manager. She has earned certificates for both Google Analytics and Google AdWords.
Learn more about Emily, here.
