Surveyors sometimes weight their data to make the findings more representative of another set of information. For example, a law firm might realize that it has gotten too few responses from some demographic strata, such as manufacturers or companies with more than $5 billion in revenue. The firm might want to correct for the imbalance so that it can present conclusions respecting the entire population (remember, the survey captures but a sample from the population). The firm could weight the manufacturers or large companies that they got more heavily to create a sample more in line with reality.
How might such a transformation apply in surveys for the legal industry? Let’s assume that a firm knows roughly how many companies in the United States have revenue over $100 million by each major industry. Those known proportions enable weighting. If the participants materially under-represent some industry or revenue range, the proportions in each industry don’t match the proportions that we know to be true. One way to adjust (weight) the data set would be to replicate participants in industries (or revenue ranges) enough to make the survey data set more like the real data set.
In a rare example, CMS Nabarro HealthTech 2017 [pg. 19] states explicitly that the analysis applied no weightings.
King Spalding ClaimsProfs 2016 [pg. 10] explains that it calculated the “weighted average experience” for certain employees. This might mean that one company had fewer employees than the others, so the firm weighted that company’s numbers so that the larger companies would not disproportionately affect the average age. In other words, they might have weighted the average by the number of employees in each of the companies. As a matter of good methodology, it would have been better for the firm to explain what they did in order to calculate the weighted average.
White Case Arbitration 2010 [pg. 15] writes that it “weighted the results to reveal the highest ranked influences.” This could mean that a “very important on” rating was treated as a four, a “quite important” rating as a three, and so on down to zero. If every respondent had given one of the influences on choice of governing law the highest rating, a four, that would have been the maximum possible weighted score. Whatever the sum of the actual ratings were could then be calculated as a percentage of that highest possible rating. The table lists the responses in decreasing order according to that calculation. This is my supposition of the procedure, but again, it would have been much better had the firm explained how it calculated the “weighted rank.”
Dykema Gossett MA 2015 [pg. 5] does not explain what “weighted rank” means in the following snippet, but the firm may have applied the same technique.
On one question, Seyfarth Shaw RE 2017 [pg. 10] explained a similar translation: “Question No. 3 used an inverse weighted ranking system to score each response. For example in No. 3, 1=10 points, 2=9 points, 3=8 points, 4=7 points, 5=6 points, 6=5 points, 7=4 points, 8=3 points, 9=2 points, 10=1 point”
Miller Chevalier TaxPolicy 2017 [pg. 6] asked respondents to rank the top three. The firm then used an inverse ranking to treat a 1 as 3, a 2 as 2 and a 1 as 1 and summed to reach a weighted rank (score).
Sometimes surveys use the term “weight” to mean “rank”. Here is an example from Berwin Leighton Risk 2014 [pg. 6].