One of the challenges faced in conducting surveys is the difficulty in knowing if the responses are truthful or false summations. While there is not a lot of information regarding the frequency of the number of lies people tell.
It is a false assumption to believe that participants in a survey would consistently speak the truth, especially when it is not socially responsible for being branded a liar, even in the face of anonymity. Lies often occur in surveys where people are asked to reveal personal details; most often, the answers provided are not true.
In this post, we will check out the statistics on lying and honesty in surveys, the motivations for untruthful responses, how to sift the lies in a survey, ways to get the truth, and the effect of lies on survey analysis.
According to a survey conducted by creditdonkey.com on lying amongst over 1200 American men and women, the following was discovered;
Survey Findings: http://www.creditdonkey.com/lying.html
In the publication “Unpacking variation in lie prevalence: Prolific liars, bad lie days, or both?“ co-authored by UWL Professor Tony Docan-Morgan, in Communication Monographs, the flagship journal of the National Communication Association.
The study reviewed 116,366 lies told by 632 participants over several days. The participants admitted to their lies daily via an online survey. An average of 75 percent of the respondents were mostly truthful or told 0-2 lies daily.
It was also discovered that most lies were insignificant, for example, admitting to liking a gift they disliked. On the other hand, 6 percent of the respondents had almost the same levels of lying, and the difference was that they had days when they lied more frequently.
Unlike most previous studies, the publication examined lies over a while compared to
daily survey behavior. The study’s authors discovered that the frequency of lying varies daily depending on the parties involved.
It is no longer news that survey respondents lie during surveys. As some survey data experts suggest, close to 50 percent of people would provide dishonest answers during a survey or fill the survey with some half-truths.
While this information may be discouraging, this situation can be better understood or forestalled when we know why people lie on surveys. Sometimes it may stem from people trying to present themselves in a better light than others, as depicted in the CBS news survey before the presidential election in 2018.
A survey was conducted to determine whether Americans were ready for an African American president. The responses were affirmative, mostly to make them seem open-minded and receptive to change and equality.
In another survey that dates back to the 1960s administered to Cornell students, it was discovered that the students provided a higher SAT score on the survey compared to their actual scores.
Here we see that certain survey questions prompt respondents to become defensive, especially when asked questions that may present them in a poor state to others if the truth is told.
Hence, it is deduced that respondents may find certain questions probing and embarrassing and prefer to lie than admit to something they find shameful.
Survey questions that asked people if they read tabloid newspapers were met with a non-affirmative response. However, the sale of tabloids has continued to rise by millions weekly. People want to be accepted and tend to lie when they believe their position is not socially acceptable; this concept is known as a social desirability bias.
A good example of this is depicted when Americans are asked if they possess guns; only 25 percent of American families admitted to this, whereas statistics of purchases show that 50 percent of American families own firearms. With the rise of gun violence, people would rather decline to admit they own firearms in other to avoid difficult conversations that may arise from being honest.
People also lie in a bid to be polite. Asking for feedback on a user experience on a new product or website might be met with positive responses, all in a bid to appear kind and polite, especially in this age of internet trolls spewing negativity. People would rather opt for kindness to encourage effort into the process.
Pop-up surveys, which will not let users progress to the next thing without a response, are usually met with a lie, as the users are often in a hurry to get to their destination and would provide random responses without giving it thought to get through.
It is shocking to know that sometimes people lie simply because they are prolific liars and have the habit of saying the exact opposite of the truth.
There are a few tips to help with lie detection in surveys and filtering dishonest responses. The first step is to analyze the quality of the survey results using these 6 dimensions;
Time: If the time spent on filling out the survey is below the standard amount of time required to fill out survey, it’s a red flag. For instance, responding to a question should usually take 3 minutes but be answered in 60 seconds or less. This means the participant sped through the responses without giving any real thought to them.
Length: The length of the comments is way shorter than the prescribed minimum. For instance, an open-ended question that requires an average of 100 words, if the respondents were careful in answering, is answered with 20 words, which indicates a problem.
Straight-lining: When the questions are met with similar answers, especially with multi-choice responses. Here participants pick random answers to get the survey over and done with.
Skipped Questions: This refers to the random skipping of survey questions by the respondents.
With this guide listed above, any response that falls into the categories listed should be set aside for further investigation to ascertain the responses’ genuineness or falsehood.
Inconsistency: Contradictions in survey participants’ responses depict dishonesty, carelessness, or both. Using multiple filters would help to identify inconsistencies in the surveys.
For instance, one of the survey questions asked how much time they spend listening to the radio weekly. Filters can be applied by follow-up questions to check which programs they listen to the most and the least.
This can be filtered down further with questions that ask if they listen to the radio.
Including Repetitive Questions
Including deliberate, repetitive questions in your survey allows for easy spotting of incorrect responses. Attempts to lie would be easily identified with this technique.
Mistruths in survey research analysis are misinformation about the true picture of things. This erodes the confidence level of the result of the research analysis. The immediate effect of this on surveys is bad data. According to a survey by Gartner, companies estimated an average of $15 million per year in losses. Bad data caused by lies in the survey causes a loss of money and weakens vital business activities.
Another effect of lies on research analysis is Misreporting, which in this case is the non-deliberate reporting of untruthful responses. It is often regarded as a response error.
For instance, during the COVID-19 outbreak, misrepresentation of information during a survey out of the fear of isolation could have a deadly effect on the rest of the population under the assumption of the non-existence of the virus in certain locations. The truth would have enabled preparedness and dispatch of self-care kits and appropriate medication.
Some other effects of lies on surveys include;
For instance, the opportunity to identify the right prospects due to the wrong information is lost due to the wrong analysis. Similarly, inadequate information can affect the ability to develop products according to the true market needs. This will result in competitors taking over your market share if they have reliable and more accurate data.
This is one of the most common effects of lying in surveys. For instance, during the beta launch phase of a product, respondents proffer wrong answers, and the organization goes into full production based on the research analysis. The products fail, and the acceptance rate is poor. The result is a total loss of all resources invested in the product.
Poor Customer Experience
Customers seek personalized services tailored to their needs, and the best way to attract buying clients is to offer a unique customer experience. Achieving customer feedback via surveys is mostly employed.
If the data collected and analyzed is wrong, the result will be an absolute mismatch of customer preference with suggested products, which would be an absolute disaster. The result would be a loss of customers and a poor retention ratio of existing customers.
Wrong Business Decisions
There are various ways to foretell future market demands and needs, and one way is to use emotions and innate instincts. The sure way is to employ the use of data analysis.
As we all know, research analysis is usually based on inputted data. This erroneous data could lead to wrong business decisions based on inaccurate data.
Wrong information, which could occur based on false information, could harm the reputation of a business negatively. Negative reviews on a site because of misinformation could discourage other customers from purchasing a product due to a false review. There is no way the customer can detect lies in reviews.
How to get the truth in surveys & increase respondent participation and honesty
In other to minimize errors in surveys and encourage truthful responses, addressing the reasons behind the lies is the first step, and this can be addressed by;
There is one more technique considered one of the best ways to inspire more truthful answers. This technique lays a foundation for the truth by using a gentle introduction to get answers. For example, instead of asking respondents if they had any formal education? Questions like did you enjoy school would encourage a truthful response, as it provides room to explain the lack of formal education.
Creating surveys is a great way to get insight into customer preferences and behavior. However, to ensure accuracy in the data collected, it is important to acknowledge that not all respondents would be truthful. Hence identifying ways to encourage truthfulness and putting measures in place to weed out lies is vital to the success of any survey.
This is crucial before the effects of lies on research analysis can be damaging and far-reaching consequences.
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