Top Mistakes People Make with Survey Analysis Software

Survey analysis software makes the job of analyzing survey data a whole lot easier, but it doesn’t make the task foolproof. Because humans are involved with the analysis process, there is always room for human error. Multiple errors can quickly stem from the top mistakes people make with survey analysis software, and we’ve listed the top three below.

Attempting to Answer Questions that Weren’t Asked

Although most surveys are created with clear objectives on what the survey set out to accomplish, some folks will still try to answer questions that were not part of the survey’s original scope. Attempting to read between the lines, or provide answers to questions that weren’t even asked, typically involves making assumptions based on responses to questions that were asked.

In other words, it involves plain and simple guesswork. And guesswork is never a good thing when it comes to accurate analysis. Remedy this mistake by sticking only to questions you asked, data you collected and information you actually know.

Altering Data to Make Up for Poor Survey Design 

Once you break out the survey analysis software, you may notice a few areas where you want information that wasn’t provided due to bad question design. Let’s say you wanted a mean and median of total household income of your respondents, but your survey only asked to indicate income using a scale of values. This precludes you from being able to calculate the precise median and mean for household income of your respondents because you have a range of values for each response and not an exact data point (although median and mean can be estimated if you assume each response is equal to the midpoint of the scale).

Another example is trying to analyze a multiple-choice question that allows for multiple answers as if it were a single-select question that allows only one answer. Data analyzed in this way will never accurately compute.

Avoid this mistake by working with the data you have, not trying to create data you want. You can also file this away as a lesson on poor question design the next time you’re creating a survey

Projecting Data to People that Didn’t Respond

One more common mistake is trying to project survey findings on an audience that was either not part of the survey respondents or not adequately represented. An example here could be conducting an employee survey asking about satisfaction with benefits. Since the survey was open to all employees, you may assume the results reflect the entire employee base. But that may not necessarily be the case.

If the demographics of the survey respondents match the demographics of the employee base as a whole, the results may indeed reflect the entire employee base. But if, say, 80 percent of survey respondents were married with children, and only 42 percent of the employee base is married with children, the results would be skewed toward employees who are married with children.

Benefits, and satisfaction levels with those benefits, may be vastly different for employees who are single, married or married with children. Applying the results to the entire employee pool in this case would not be wholly accurate.

Steer clear of this error by ensuring you carefully review who responded to your survey – and making sure you don’t apply their answers to people who did not respond.

Another way to avoid survey analysis mistakes is to ensure your survey is set up to generate the type of data you want for the answers you need. Survey analysis software is a remarkable tool for gaining insights into your collected data, but it’s crucial to design questions that collect the right data to meet your overall aim.