Weighting efficiency
The rim weighting efficiency (which is a figure which gives an indication of how well
balanced the sample is) will depend on many factors, including sample design
factors such as the level of under of over sampling.
If the data for many respondents needs to be weighted heavily up or down (e.g. the
rural population which is under-sampled, as decided by the industry, is heavily upweighted), the efficiency percentage will be low.
However, by addressing a small number of the outliers in the sample, the weighting
efficiency can be increased substantially. The reason for this is that the more the
under sample (or for that matter the oversample) is reduced, the more well balanced
the sample becomes and the greater the efficiency percentage becomes.
By capping outliers and maximum weights, the ratio between the smallest and the
largest weights will become smaller and therefor improve the efficiency.
Alternatively some of the cells within the RIM weighting variables may be collapsed
e.g., collapse age group 4yrs – 6yrs and 7yrs to 10 yrs into a 4yrs to 10yrs RIM.
If the rim weight scheme uses a large number of variables, some may be removed,
particularly those that are extreme in terms of being weighted upwards or
downwards. Although this may be done it is not necessarily a good solution as
important discriminators of viewing behavior may be excluded and the sample
design maybe impacted.
In practice there will not be a change in ratings or share when this is done, but it
could reduce the variability of results especially for smaller target markets.
So what is the effect of lower efficiencies?
It reduces the reliability of the sample, or in other words, the margin of error
increases. This does not mean that the results are incorrect, but only that the
statistical sampling error is bigger than it would have been if the effective sample
was larger.
Example:
You have to weight some data that you are working with to get closer to a well
balanced sample.
Say your weighting efficiency is 55%.
How do you determine your effective sample?
Your effective sample size is your achieved sample size multiplied by the weighting
efficiency - so multiply your sample size by 0.55
Say you have 1,000 respondents in your sample, then your results have an effective
sample size of 550, so your answers would be as reliable as if you'd asked 550
people instead of a thousand.
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What does this mean in practice?
This does not mean that the results are incorrect, but only that the statistical
sampling error is bigger than it would have been if the effective sample was larger.
In other words, you pay the price in terms of increases in the margin of error if you
need to deviate from the ideal sample profile because of the need to over sample
under represented demographics or under sample over represented demographics.
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