Complete Guide on Weighting Survey Data
Guide to Weighting Survey Data: Pros and Cons
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The ultimate goal of all research studies is to make sense of how a population thinks, responds, and behaves. Sometimes, while choosing the target population of interest, you might go for a specific group of respondents.
This chosen sample could comprise your current customer base or a segment that meets set criteria. However, at other times, the population of interest might comprise a larger population.
In any case, the chosen sample for your research study is often not representative of your desired population. And to that extent, a little aligning by weighting survey data might be an appropriate technique to make your research more relevant and insightful. Thus, weighting in market research is primarily used to bring the research results in line with your knowledge of the population of interest.
Suppose in collecting survey data; you end up with too many male respondents than women while your relevant target market comprises more women than men. Similarly, at times too many older respondents complete the survey than enough young people.
For all such circumstances having a disproportionate number of respondents from any one group or segment can lead to sampling error and sampling bias. Weighting survey data could be a sound idea, especially if you want your sample to represent the whole population.
However, when it comes to weighting in market research, there could be a lot of confusion about when to use it and if to use it at all. Here is a complete guide on weighting in market research and the pros and cons of weighting data!
What is Weighting Survey Data?
Weighting is a statistical technique used to make adjustments in survey data for sampling errors. So even if you don’t attain a representative sample of a population of interest, you can still adjust the data by using this technique.
What researchers do is apply a weighting factor to scale each respondent up and down as necessary. If your survey data has lesser female respondents, you can scale it up by assigning more weight to each respondent.
As a result, each respondent doesn’t count as one respondent but might count as 0.8 respondents or 1.3 respondents, depending on the representation needed.
For instance, you conducted a survey in which only 30% of the respondents were males. And since the general census population comprises 50% males, you would have to make adjustments for the remaining 20%.
Using weighting in your research will then allow you to bring the results in line with the overall characteristics of the population.
As a general rule of thumb researchers assign:
- For an underrepresented segment, a certain weight that is larger than the value of 1
- For respondents that belong to an over-represented category, they assign a weight less than 1
The Primary Objectives of Weighting in Market Research
As long as you have done the work upfront by sampling your target population in a representative way that aligns with your research goals, there is no need for weighting. Instead, you can move on to the next step of analyzing your data.
Weighting techniques are only needed if you find any discrepancies between the actual population that should target your analysis and the breakdown of the consumers who responded to your survey.
As a result, no accurate conclusion can be drawn from your survey. Below are two primary objectives of weighting survey data:
To Simulate Population of Interest
Whether you have sampled from the entire US population or just from your office staff, opinions are bound to vary everywhere.
Weighting data, therefore, allows you to understand how those opinions or behavior differ across various demographic segments. This technique helps you simulate your desired population for your research to represent everyone in that group.
To Control Variables
The population across the country can be anything from retired voting citizens to dog owners and car enthusiasts. What is important to understand is that each segment is unique and thus, holds its own representative sample.
Therefore, to gain accurate insights through market research, you have to rely on auxiliary variables. These are the variables that have been measured in the survey, and their distribution is known.
Suppose you conducted a survey in 2016 with a random sample of consumers. So when you rerun the same survey in 2021, you do not have to take another random sample. Instead, you can weight the data to match the sample from 2016 to control variation in sampling.
Weighting the data will allow you to eliminate any differences between the 2016 and 2021 sample populations and their possible effects on the results. Doing so ensures that you are only comparing changes regarding the tools and not changes in population.
Pros and Cons of Weighting Data
Like any other technique for manipulating a data set, there are pros and cons of weighting data. Some of the most significant ones include:
Pros of Weighting Survey Data
- Allows you to make adjustments to the sample so that it has correct representation for accurate results.
- Allows researchers and insight professionals to find adequate representation for larger data sets so that each segment of the large data set is not over or under-represented.
- Ensures that responses from hard-to-reach demographic segments are also included in the survey results equal to the population.
- Eliminates chances of biases and the influence of challenges during data collection for accurate final survey outcomes.
Cons of Weighting Survey Data
- Can over or under-represent respondents’ views who might not be an accurate representation of the entire demographic segment.
- Can affect all analysis, including reported descriptive statistics (means, medians, modes) and inferential statistics such as regressions and coefficients.
- Can cause more variation in findings if the standard deviation of responses increases due to weighting.
- Can inadvertently introduce biases into sampling.
- Can distort data if a certain segment is weighted higher than it should be, particularly when respondents with high weights have a low base size in the sample. For instance, if only 10% of your data set were corporate employees, each corporate respondent would be counted five times in the survey results.
To Weight or Not to Weight?
While it might feel like that you should never weight survey data after going through the pros and cons of weighting data, it wouldn’t be fair to give up this great technique for good.
Instead, a better approach would be never to weight data to adjust or correct for missing data. In fact, you should use it more for oversampling rather than under-sampling.
Less is More and Try to Avoid Extremes
It would be best if you also avoid a range of weights that is too large. For example, if some respondents get weights of 5 and others are getting weights of 0.5, your results are definitely going to be flawed and unreliable.
Therefore, you must try to minimize the sizes of the weights. As a standard, make sure never to weight a respondent less than 0.5 or greater than 2.0.
If you cannot do without weighting data, then it is best to weight by as few variables as possible. The more the number of weighting variables, the greater the risk that one variable’s weighting might meddle with the weighting of another variable.
In survey research, having a representative sample of the population of interest is of vital importance. But despite a perfectly designed sampling plan, you often end up over or under-sampling a particular segment.
And weighting survey data can be a fitting statistical technique to overcome unintentional sampling bias in research.
So in weighing survey data, you assign a certain coefficient or individual weight to each case in your sample. This coefficient is then used to multiply the case to gain the desired characteristics of the sample.
While weighting survey data might be a great way to make your research more relevant, it is a correction technique, nevertheless. So on the fundamental level, the concept is about altering the research data that you have collected.
For this reason, it is recommended that researchers should avoid relying on this technique as much as possible. Instead, they can take out time to identify and target a represented balanced sample for a survey rather than opting for weighting.
Not only will it save you time, but it will also prevent your research from compromising on authenticity.
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