## What is Weighting?

In research studies, survey data can become skewed when certain segments within a population are over or under represented. Even the most well-planned samples often end up with too many respondents from a single gender, age group, or ethnicity.

Weighting is applied to correct this. It is used to adjust the results of a study to bring them more in line with what is known about a population.

## When to Use Weighting?

Consider using weighting techniques when your survey sample has been considerably skewed. To accomplish this, there are two popular methodologies:

1. Cell-Based Weights: In this method, the researcher specifies the weight/multiplier for each specific group. The multiplier is calculated by dividing the population [desired state] by your sample.

For example, suppose you’ve surveyed 100 respondents- 80% plant-based eaters and 20% meat lovers. It’s a fair assumption to argue that the overall results are biased due to the large percentage of plant-based eaters. If you know that only 30% of the population is plant-based, and 70% are meat-eaters then the weights you would apply to your data set would be:

• Plant-based: 30/80 = 0.375
• Meat-eaters: 70/20= 3.5

2. RIM/ Raking Weights (Random Iterative Method): This method is used when you are weighting multiple variables that are overlapping in nature. Raking is iterative proportional fitting. With raking, a researcher chooses a set of variables where the population distribution is known, and the procedure iteratively adjusts the weight for each case until the sample distribution aligns with the population for those variables.

For example, a researcher might specify that the sample should contain 48% male and 52% female, 40% with a high school education or less, 31% who have completed some college, and 29% college graduates. The process will adjust the weights so that the gender ratio for the weighted survey sample matches the desired population distribution.

Next, the weights are adjusted so that the education groups are in the correct proportion. If the adjustment for education pushes the sex distribution out of alignment, then the weights are adjusted again so that men and women are represented in the desired proportion. The process is repeated until the weighted distribution of all of the weighting variables matches their specified targets.