Social desirability bias is a type of response bias that occurs when survey respondents answer questions in a way that will make them look good or socially desirable.
This can occur for many reasons, including the desire to please the interviewer or researcher, fear of being judged by others, and not wanting to admit certain aspects of their lives.
Social desirability bias can have serious implications on the accuracy and validity of survey data as it distorts responses and skews results.
To combat this issue, researchers must be aware of social desirability biases and take steps to minimize its effects in their studies.
Let’s explore what social desirability is, how it affects surveys, and what techniques you can use to reduce its impact on your research findings.
What Is Social Desirability?
Social desirability is a type of cognitive bias in which people tend to present themselves in the best possible light when answering surveys or providing feedback.
It is a behavior in which a respondent consciously or unconsciously adjusts his or her responses to questions in order to make himself or herself seem more acceptable, desirable, and admirable to others.
This often happens when respondents feel they are being judged by the researcher or interviewer or they fear that their answers may be shared with other people.
Examples of social desirability:
- A respondent might underreport income or exaggerate their educational background in order to make themselves seem more successful.
- Respondents may falsely deny engaging in risky behavior such as drug use, alcohol consumption, or criminal activity out of fear of being judged by others.
- People may answer questions about their opinions in a way that would be more socially acceptable, even if it does not accurately reflect their true beliefs.
Social desirability bias can have a significant impact on the validity of survey data.
Respondents may adjust their answers in order to present themselves in a favorable light, which can lead to inaccurate data and inflated results.
If not accounted for, this can lead to misinterpretations of the survey results and invalid conclusions about the target population or issue being studied.
Techniques for Reducing Social Desirability Effects on Survey Results
Researchers can work to reduce the impact of social desirability bias on surveys. Below are just a few methods for doing so.
1. Make the Survey Anonymous or Confidential
Making the survey anonymous or confidential can help to reduce social desirability bias, as respondents will be less concerned about their answers being judged by others.
Confidentiality can be achieved by using a third-party service like ProductLab to host the survey and collect data anonymously.
2. Ask Open-Ended Questions
Open-ended questions allow respondents to provide more detailed and honest answers since they are not constrained by the options provided in a closed-ended question.
Open-ended questions can help to reduce the effects of social desirability bias since respondents will be less likely to adjust their responses in order to present themselves favorably.
3. Avoid Leading Questions
Leading questions can influence respondents’ answers and lead to inaccurate data. These types of questions suggest a certain response or contain information that could sway the respondent’s answer.
As such, it is important to ask neutral questions that do not give away any assumptions about the expected responses.
4. Consider Randomized Response Techniques
Randomized response techniques involve randomly assigning respondents to answer either a sensitive or non-sensitive question, thus making it difficult for them to know which question they are answering.
This method helps to reduce social desirability bias as it makes it more difficult for respondents to adjust their answers in order to be perceived favorably.
Read more on the randomized response technique here.
5. Include Follow-Up Questions
Asking follow-up questions can provide additional insights into the respondent’s answers.
Follow-up questions allow researchers to gain more accurate and detailed data because respondents are less likely to adjust their responses in order to make themselves appear better.
6. Opt for Online vs. In-person Surveys
In-person surveys can be more susceptible to social desirability bias, as respondents may feel uncomfortable with the interviewer or fear their answers being shared.
Online surveys provide a more anonymous and confidential setting for respondents, helping to reduce the effects of social desirability bias on survey results.
7. Use Blind Surveys
Blind surveys involve hiding respondents’ identifying information, such as their name or company affiliation.
Opting for blind surveys can help to reduce the effects of social desirability bias, since respondents will be less likely to adjust their answers in order to appear more favorable based on who they are associated with.
8. Keep the Purpose of the Survey Unknown
Making the purpose of the survey unknown to respondents can help to reduce social desirability bias.
This is because respondents will be less likely to adjust their responses based on what they think the researcher wants them to say, since they don’t know the purpose of the study.
For example, if residents of a building are given a survey on areas for improvement due to a surplus in the budget, they may be more likely to advocate for their own interests over necessary relief.
9. Don’t Create a “Wrong” or “Bad” Answer
When designing a survey, you’ll want to avoid creating questions that have “right” or “wrong” answers. The phenomena can encourage respondents to adjust their answers in order to appear more favorable, leading to inaccurate data.
Instead, opt for open-ended or neutral questions that allow for honest responses. Even slight wording mistakes can completely influence results.
For example, if a survey asks participants to choose their care level for helping the less fortunate but begins the question with a morality quote on why humans should help one another, the answers may be skewed.
More people are likely to answer they are interested because that seems to be the right answer despite their personal interest level being low.
Understanding the Social Desirability Scale
The social desirability scale is a tool that measures the tendency of people to conform their answers to socially accepted values and beliefs.
This scale can be used to detect if respondents are adjusting their answers in order to appear more favorable. The goal of this scale is not to judge or evaluate the respondents, but to measure the level of social desirability bias in survey data.
When including a social desirability scale with your survey, it is important to keep the questions general and neutral in order to avoid influencing responses.
Also, make sure that the scale is compatible with the type of survey being conducted; for example, an online survey may need to be adjusted for a different scale than an in-person interview.
Other Types of Survey Biases to Avoid
Understanding and combating survey bias is crucial for creating effective surveys with viable data. ProductLab’s complete guide to survey bias can be found here. We’ve also broken down a few of the most common types for you right here!
1. Response Bias
Response bias is a type of survey bias that occurs when respondents provide inaccurate or biased responses due to a variety of factors, including their desire to make themselves look favorable or their lack of understanding or knowledge on the subject.
2. Nonresponse Bias
Nonresponse bias is a type of survey bias that occurs when some respondents are more or less likely to respond than others.
This can lead to inaccurate results, as the responses of those who did not participate may have been different from those who did.
To reduce this type of survey bias, researchers should ensure that their survey is accessible to all potential respondents and encourage participation through incentives or reminders.
3. Sampling Bias
Sampling bias is a type of survey bias that occurs when the sample used does not accurately represent the population of interest.
This can lead to inaccurate results as the data collected may not be representative of the population as a whole.
To avoid sampling bias, researchers should ensure that their sample is randomly selected and that it accurately reflects the population of interest.
4. Confirmation Bias
Confirmation bias is a type of survey bias that occurs when respondents provide answers that confirm their existing beliefs or preconceptions.
For example, if a respondent is asked about a certain policy and they have already formed an opinion on the matter, they may answer the question in line with their existing beliefs.
To avoid this type of survey bias, researchers should ask neutral questions that do not presuppose a certain answer.
5. Order Bias
Order bias is a type of survey bias that occurs when the order in which questions are asked influences the responses given.
To counteract this, researchers should randomly order the questions in their surveys or ask the same question multiple times throughout the survey using different wordings.
6. Observer Bias
Observer bias is a type of survey bias that occurs when the researcher’s presence or involvement in the data collection process influences the responses given by participants.
To reduce this type of survey bias, researchers should use anonymous surveys and ensure they are not present during interviews or focus group discussions.
7. Survivorship Bias
Survivorship bias is a type of survey bias that occurs when a researcher overlooks or ignores those who did not make it to the end of the study.
To reduce this type of survey bias, researchers should ensure they are collecting data from all participants throughout the duration of the study and take into account any dropouts.
8. Acquiescence Bias
Acquiescence bias is a type of survey bias that occurs when respondents tend to agree with the questions they are asked, regardless of their true opinion.
To reduce this type of survey bias, researchers should ask balanced questions that do not contain leading language or require yes or no responses.
9. Interviewer Bias
Interviewer bias is a type of survey bias that occurs when the interviewer’s personal biases, beliefs, or attitudes influence the responses given by participants.
To reduce this type of survey bias, researchers should ensure their interviewers are properly trained and monitored during interviews.
Conclusion
Social desirability bias can significantly impact the validity of survey data, as respondents may adjust their answers in order to make themselves appear more desirable.
To reduce the effects of this bias, researchers should consider employing techniques like making surveys anonymous and confidential, asking open-ended questions, avoiding leading questions, using randomized response techniques, and including follow-up questions.
By implementing these methods, researchers can ensure that the survey data they collect is more accurate and reliable.
FAQs
Q: What is the importance of avoiding survey bias?
A: It is important to avoid survey bias in order to ensure that the data collected through surveys are reliable and accurate. If survey bias is not taken into consideration, it can lead to misleading or erroneous results which can cause researchers to draw incorrect conclusions. Therefore, researchers should employ strategies to reduce the effects of survey bias in order to ensure that the data they collect is valid and useful.
Q: What is the social desirability scale?
A: The social desirability scale is a tool used to measure respondents’ inclination to provide socially desirable responses. It consists of a series of questions that measure how likely an individual is to present themselves in a favorable light when responding to survey questions. By including this type of question in surveys, researchers can identify and adjust for any potential social desirability bias.
Q: What are some strategies for reducing survey bias?
A: Some strategies for reducing survey bias include making surveys anonymous, asking open-ended questions, avoiding leading questions, using randomized response techniques, and including follow-up questions. Researchers should take into account any potential sources of survey bias such as order bias, observer bias, survivorship bias, and acquiescence bias.