Why Use Weighted Averages in Survey Results?

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In summary, the speaker showed the results of a company survey which had weighted averages for questions on communication and skill utilization. The purpose of the weighted averages seems to be to skew the results in favor of what the company wants to see, rather than accurately reflecting the responses of the employees. This practice of weighting can be seen as cheating and may not serve a purpose in this particular example.
  • #1
DaveC426913
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We got some results back from a company survey today. The speaker showed us the numbers and told us they had weighted the averages.

Say, there were two questions:

Code:
                                    All the time | often | sometimes | rarely
How well does our company communicate?   60%     |  30%  |    10%    |   0
How much are your skills utilized?	 40%     |  50%  |    10%    |   0

I asked why.

They said that they had given more weight to the aspects that are more important, for example, we are interested in the positives of our company, so they were weighted heavier. They might have set them as All the time = 1, often = .75, sometimes = .5 and rarely = .25.

It seems to me, all this does is skew the results in favour of what they want to see. What is the purpose of weighted averages in this example?
 
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In that specific example? Nothing, as far as I can tell. It's as you say: they wanted to skew the results in their favour. The results don't even mean the same thing anymore (I'm not sure what "60% of respondents said 'always'" is supposed to mean when they took a weighted average).
 
  • #3
You are correct, they seem to be skewing the results, period...there is not proper weighting here.

If they had gotten answers from offices in two different cities, one with 2000 employees and one with 3000 employees and they had only received 100 surveys from each of the offices, I can see weighting them so that the 100 answers from the 3000-employee building represent 60% of the population and the answers from the 2000-employee building represent only 40%...other than that...giving more weight to one answer over other one is just cheating.

my 2 cents
 

1. Why is a weighted average used in data analysis?

A weighted average is used in data analysis to give more importance or significance to certain data points over others. This is particularly useful when dealing with large sets of data that may have outliers or extreme values that could skew the overall average. By assigning weights to each data point, the weighted average takes into account the relative importance of each point and provides a more accurate representation of the data.

2. How is a weighted average calculated?

A weighted average is calculated by multiplying each data point by its assigned weight, adding up all the products, and then dividing the total by the sum of the weights. The formula for a weighted average is: (x1*w1 + x2*w2 + ... + xn*wn) / (w1 + w2 + ... + wn), where x represents the data points and w represents the weights.

3. What are the advantages of using a weighted average?

One advantage of using a weighted average is that it provides a more accurate representation of the data by giving more importance to certain points. It also allows for the inclusion of outliers without significantly affecting the overall average. Additionally, a weighted average can be useful when comparing data sets with different sample sizes or when dealing with data that has varying levels of importance.

4. When should a weighted average be used instead of a regular average?

A weighted average should be used instead of a regular average when the data set contains points that have varying levels of importance. This could be due to different sample sizes, outliers, or data that has different levels of relevance. In these cases, a weighted average will provide a more accurate representation of the data.

5. How can a weighted average be interpreted?

A weighted average can be interpreted as the average value of the data points, taking into account their relative importance or significance. It can also be seen as a balance point, where the weights assigned to each data point represent the force pulling on that point, and the weighted average represents the equilibrium point. Essentially, the weighted average is a more precise measure of central tendency compared to a regular average.

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