With the presidential election in the United States in full swing there has been a lot of talk about the validity of political polls. This includes discussion on how to appropriately weight data. In this mini-series, we unlock the truths behind these weighting myths and misconceptions.
Many clients are very hesitant to weight data. A lot of this is due to fear based on some misperceptions.
- First, there is a misperception that data manipulation is bad. Weighting does involve data manipulation, but so does setting quotas on survey completes. In both cases the sample is being forced to look a certain way.
- Weighting does reduce base sizes, but there are ways to minimize this impact. For example, instead of weighting using five age breaks weight using three age breaks.
- Outliers can influence data in weighting. For example, if one respondent spends $5,000 on shoes annually and they receive a high weight, data may be inflated. Spending a little time analyzing outliers before weighting data can help minimize this problem.
- Weighting does add some complexity and time to the study. However, weighting is built into data processing programs and automatically produces diagnostics which make it easy to access the results and make adjustments. Spending a little time now can save hours in analytic time trying to make sense of the data. Also, the cost and time for weighting usually falls far short of the cost and time involved in a bad business decision.
Weighting is no replacement for appropriate sampling, but it can help bring imbalances in line. There are a number of reasons why weighting can be beneficial:
- First, it helps make the sample more representative of the target population.
- It can help adjust for differential response rates by weighting up groups who under respond and weighting down groups who over respond.
- It can also allow for comparisons across samples by driving consistency. This can be important in trackers and even concept and ad tests. For example, if the objective is to pick a winning idea among several and there are differences in the samples the wrong decision could be made.
- Weighting is a cost effective method compared to others – no additional completes are needed.
- Finally, it may be the only proven tool left to assure a representative sample. If everything that has been done at the sampling and fielding phases still results in some imbalances, weighting is the one thing that can be used.
There are two key types of weighting – cell and RIM weighting. Generally, RIM weighting is preferred over cell weighting. RIM weighting is an iterative process that is designed to attempt to weight all variables by iteratively adjusting the weights for one variable, then for another, etc, until an adequate solution is yielded that brings all variables into line with their respective targets. Since RIM weighting uses non-interlocking variables more variables can be used because there are fewer cells. This means a smaller sample size is needed and can also provide more stable results. RIM weighting only requires knowledge of the distribution of each variable separately while knowledge of the relationship between variables is not required.
Next week, we’ll review how to appropriately weight and evaluate the weighting scheme.