제품

SurveyMonkey는 모든 사용 사례와 요구를 다루도록 구축되었습니다. 제품을 둘러보고 SurveyMonkey로 어떤 효과를 누릴 수 있는지 알아보세요.

온라인 설문조사의 글로벌 리더로부터 데이터 기반 인사이트를 얻으세요.

100개 이상의 앱 및 플러그인과 연동하여 업무 효율성 향상

정보 수집과 결제를 위한 온라인 양식을 만들고 맞춤화하세요.

빌트인 AI을 통한 더 나은 설문조사 작성과 빠른 인사이트 발견

시장 조사에 필요한 모든 것을 갖춘 솔루션

기본서식

비즈니스에 대한 고객 만족도와 충성도를 측정

고객을 만족시켜 지지자로 만드는 것이 무엇인지 파악

실행 가능한 인사이트를 얻어 사용자 경험을 개선

잠재 고객, 참석자 등으로부터 연락처 정보를 수집

다음 이벤트를 위해 쉽게 RSVP를 받고 확인

다음 이벤트 개선을 위해 참석자가 무엇을 원하는지 파악

참여도를 높이고 더 나은 결과를 이끌어낼 인사이트를 발견

참석자들의 피드백을 받아 회의 운영 방법을 개선

동료 피드백을 통한 직원 성과 향상

더 나은 코스를 만들고 교수법을 개선

학생들이 코스 자료 및 프레젠테이션을 어떻게 평가하는지 파악

신제품 아이디어에 대한 고객의 생각을 파악

리소스

설문조사 및 설문조사 데이터 사용에 대한 모범 사례

설문조사, 비즈니스를 위한 팁 등에 관한 블로그

SurveyMonkey 이용에 대한 튜토리얼 및 사용법 가이드

일류 브랜드들이 SurveyMonkey로 성장을 견인하는 방법

Common types of survey bias and how to avoid them

Administering preliminary market research using surveys can save businesses on costs and time. However, accurate feedback and insights are contingent on the surveyor asking unbiased questions that get honest answers. 

While it may seem impossible to completely avoid biased survey questions, there are ways to order your questions to avoid answer bias. You can also strategically plan to use certain question types to help ensure you’re not adding your own bias to the survey design. When you're conducting any type of research—whether its gathering data for a political poll or feedback on a new product idea, honest feedback will provide the most accurate data. 

Get pre-written survey templates, created by experts, to help ensure you’re asking the right questions, the best way.

You may already have an opinion about the topic being researched, but it is possible to collect unbiased responses from people taking your survey. Bias can affect the outcome of a survey. This article will teach you how to reduce bias in a survey. You’ll get a better understanding of the different types of survey bias and what they look like. 

Survey bias is a deviation of feedback based on certain influences by the surveyor and respondent. Sampling bias certainly plays a part in how unbiased feedback and insights can be. 

Sampling bias is when certain people are systematically more likely to be chosen in a sample than others, otherwise known as purposive sampling. Purposive sampling has its advantages in certain situations, particularly for smaller groups. Yet, when it comes to sampling a larger population, it’s critical to reduce the amount of bias in your surveys for the most accurate insights. 

Tip: Learn 4 leading types of bias and how to prevent them from impacting your next survey results.

Response bias are skewed insights from respondents whose answers deviate from how they actually feel. The response can be a result of many factors. Speeding through surveys to finish them quickly may result in answers that are biased. For example, a user may only complete the multiple choice answers and not the text responses. Other biases found in responses may result from respondents not disclosing demographic information in a survey. They might not understand the question, or they’re just not comfortable answering. It’s possible that through hasty purposive sampling, the survey might not be relevant to respondents. It could also be the survey structure that encourages a particular answer. Overall, there are essentially seven types of response bias: 

  • Demand - Demand reflects the respondents’ pressure of taking a survey. As a result, their behavior and opinions change based on advanced knowledge and assumptions about the questionnaire.
  • Social desirability bias - Social desirability reflects respondents’ desire to answer a question in a way they believe is morally or socially acceptable. It’s a type of conformity bias.
  • Dissent bias - Dissent bias is when respondents answer survey questions negatively. They may not understand what the survey is for, or they might have difficulty understanding the questions. 
  • Acquiescence bias - Acquiescence bias means all survey answers are positive. This feedback might be the result of administering a survey too soon. The consumer should have enough time to properly assess the product or service.
  • Extreme response bias - Extreme biases are seen in answer selections like “Strongly agree” or “Strongly disagree.” Here, respondents choose extreme answers even it’s not their actual viewpoint.  
  • Neutral responding - Neutral responding is the opposite of extreme bias. Respondents consistently choose an unbiased answer. This type of response bias reflects disinterest from the respondent.
  • Question order bias - Question order bias reflects the order in which the questions are asked. This arrangement can negatively or positively affect the respondent's answer. The order of survey questions must be structured wisely.

Tip: Eliminate question order bias to improve your survey data.

Non-response bias, also termed systemic bias, is when respondents included in a survey don’t respond. It represents a gap within your feedback and insights that will result in inaccurate data. Non-response bias also represents respondents who participate in a survey but then drop out for any reason. If you're finding that a high percentage of survey takers are not responding to your survey altogether, then you may have to redesign it to ensure they take it.

There are many reasons why a respondent refuses to participate in a survey. It could be personal, or it might have something to do with how the survey is built. Non-response bias can also be the result of timing. Give respondents enough time to complete the survey relative to the feedback they're providing. Don't send a transactional survey one or two weeks after the transaction because your respondents won't remember the interaction. You can even send reminders, but be mindful of the cadence. 

Tip: Response bias and non-response bias are the two main ways to get biased feedback. Discover five ways to avoid non-response errors.

Due to limited data and inaccurate analyses, survey biases can negatively affect research results. Such inaccuracies are the result of responsive and unresponsive biases. More specifically, the errors in survey results impact data issues, poor strategies and investment, low ROI, dissatisfaction, and inconclusive results. Understand how a little bias can cause big issues

  • Data issues - Data issues will come from less than truthful responses in non-response biases. Either one of these issues can cripple you towards achieving a clear and conclusive analysis. 
  • Poor strategies and investment - Deciding the number of participants in a survey sample to match the larger audience is critical for collecting valuable feedback. Strategic planning is necessary to carry out your business objectives.   
  • Low return of investment (ROI) - The time and costs devoted to building a market research survey must be worth the effort. Making business decisions based on imperfect feedback and insights will ultimately result in a low monetary return on your investment.
  • Dissatisfaction - Unsatisfactory survey results can lead to poor business decisions that can snowball into low performance. Low performance might result in unsatisfied investors reducing their contribution and your marketing budget.
  • Inconclusive results - Ambiguous survey results might require a repeated test. This process will extract more time and cost. Also, new sampling may overlap with respondents from the first survey who may refuse to participate in a second one. 

Survey bias can also affect different types of interview surveys. Which survey is most likely influenced by bias? Group interviews, one-to-one interviews, panel interviews, phone interviews, and online surveys can suffer from biased interviewers. It’s impossible not to inherently have a biased position on a subject you’re researching, especially if it benefits your business. However, it's still possible to have an unbiased approach to get the most accurate survey results. 

Acquiring the most accurate survey results means understanding different types of biased survey questions. The six survey bias examples we’ll examine here are leading questions, loaded questions, double-barreled questions, absolute questions, ambiguous questions, and multiple answer questions. With each biased survey question, you’ll see how it can be written in an unbiased way.  

Leading questions involve a surveyor inserting their opinion into the question. This bias influences respondents to answer the question in a way the inquiry suggests is correct. Consequently, this response results in skewed data that won’t help your overall business objective. 

Example:

A good survey question about Company A’s customer service might look like this:

How helpful are the employees at Company A?

  • Extremely satisfied
  • Very satisfied
  • Somewhat satisfied
  • Not so satisfied
  • Not at all satisfied

A leading question might look like this:

Do you think the customer service at Company A is better than your experience with employees at Company B? 

This question suggests that Company A is better than Company B, because the phrasing is too specific. If Company A’s objective is to compare its customer service with a particular company, the question is satisfactory. Having a clear business objective is so important when building a survey. Learn what you need to know about creating good questions for your next survey.

Loaded questions persuade respondents to answer questions a certain way. This type of query is done when surveyors expect too much from respondents. Even with a buyer persona profile, it’s still best to maintain an objective approach with your survey questions. 

Example:

If Company A is a supermarket that also sells pet food, its survey question should be:

Do you currently have a pet where you live, or not?

  • Yes, I do
  • No, I don’t

This question filters out respondents who have pets and those who don’t. In a case like this, you can implement question logic for respondents who answers yes and no. 

If Company A leads its survey with a question like, “What brand of dry food does your dog like?,” the surveyor assumes two things: The consumer has a pet, and it’s a dog. This assumption will lead respondents who don’t have a dog to believe the survey is for dog owners instead of supermarket shoppers. Consequently, they’ll exit the survey leaving you with inconclusive results. 

Tip: Write smarter survey questions and avoid from asking leading and loaded questions.  

Double-barreled questions are two survey questions asked in one. The question persuades the respondent to offer their opinion on two topics, but with only one opportunity to respond. Here is an example of how to avoid double-barreled questions

Example:

Suppose a doctor's office is interested in monitoring its customer service. In that case, they may want to assess their patients' opinions about how they were treated from the time they checked in to any follow-up visits, if applicable. A good survey question for this scenario might look like this:  

Overall, how responsive has our office been to your questions or concerns?

  • Extremely responsive
  • Very responsive
  • Somewhat responsive
  • Not so responsive
  • Not at all responsive

The double-barreled survey question you want to avoid looks something like this:

How responsive was our team during your visit with us, and did someone follow up with you after the appointment?

Wording questions this way will likely result in a one-answer response that doesn’t quite satisfy the data you’re looking for within that double-barreled query.

Absolute questions require respondents to be 100% certain about the answer they provide in a survey. Such questions may require a yes or no. They will also include words like “always,” “never,” “every,” or “all.” Responses like this clump together assumptions leading to invalid cataloging that neglects influential variables.  

Example:

Did Product X’s Outdoor Bug Repellent eliminate every mosquito?

  • Yes, it did
  • No, it did not

The probability of an outdoor repellent getting rid of every single mosquito is unlikely. The respondent will most likely answer no. However, the product might reduce the number of mosquitoes within the perimeter of its use. In this scenario, we're missing critical information to help assess the product's effectiveness. A better question could be worded like this:

How satisfied are you with the reliability of Product X?

  • Extremely satisfied
  • Very satisfied
  • Somewhat satisfied
  • Not so satisfied
  • Not at all satisfied

Providing a selection of answers like this allows respondents to rank the product's effectiveness. This type of ranking will help the researcher analyze how well Product X performs. 

Ambiguous survey questions leave room for interpretation because the wording isn’t clear. The query may be too broad or lacks clarity. The use of abbreviations, acronyms, and terminology also contributes to ambiguous questions. Questions may be vague or particular to the business industry. Ambiguous questions allow respondents to interpret queries in a way that makes sense to them, resulting in an obscure response.

Example:

A business objective for a certain dentist’s office is to get referrals. One of the questions they might want to ask patients could look like this:

How likely are you to encourage others to visit our office?

  • Extremely likely
  • Very likely
  • Somewhat likely
  • Not so likely
  • Not at all likely

An ambiguous question might look like this:

Do you think your friends and colleagues would like us?

Also, avoid asking broad questions that persuade respondents to rephrase the query, so it makes sense to them. Their interpretation might be different from the true intent of your question. 

Multiple answer survey questions provide a more controlled approach to collecting feedback and insights. However, the challenges include phrasing the options to avoid inconclusive responses. A good practice is to create answers with choices that don’t overlap.

Example:

If you’re trying to get an assessment of a sample population’s annual income, phrase the survey question like this:

How much money did you personally earn last year?

  • $0 - $19,999
  • $20,000 - $49,999
  • $50,000 - $79,999
  • $80,000 - $99,999
  • $100,000 or more

Don’t do this because it makes the ranges unclear to the respondent:

How much money did you personally earn last year?

  • $0 - $20,000
  • $20,000 - $50,000
  • $50,000 - $80,000
  • $80,000 - $100,000
  • $100,000 or more

Providing overlapping answers eliminates choices. A respondent earning $50,000 a year can be categorized in the second and third categories. In the previous example, that respondent clearly belongs in the third category. Details like this matter when analyzing feedback and insights. Get more tips about writing good survey questions and use survey templates with pre-written questions.

When surveys are done correctly, they can yield valuable feedback and insights that will help you make better-informed business decisions. To get the most honest and unbiased feedback from your surveys, refrain from imbuing personal opinions into questions. Avoid framing survey questions that influence respondents to answer a certain way. Be clear and concise. Keep your language simple to avoid misinterpretation. Provide straightforward answers for better assessment results. 

SurveyMonkey offers customizable surveys for any to industry to help you achieve any business objective. Select a plan that works for you today.

도구 키트 디렉터리

역할 또는 업계에서 피드백을 활용할 수 있도록 돕기 위해 디자인한 도구 키트를 살펴보세요.

SurveyMonkey Enterprise 기능 살펴보기

SurveyMonkey의 엔터프라이즈급 보안 기능, 개인정보 보호 및 규정 준수 기준, 데이터 연동 서비스로 규모에 맞게 피드백을 관리하세요.

최고의 브랜드들이 SurveyMonkey로 성장을 견인하는 방법

SurveyMonkey를 사용하여 성공적인 제품과 사용자 환경을 만들어 성장을 주도하는 일류 브랜드들의 대열에 합류하세요.

적시에 제공되는 브랜드 추적 인사이트로 브랜드 전략을 강화하세요

시간의 경과에 따라 브랜드 상태를 관리하고, 브랜드 인지를 높이며 브랜드 인식을 개선할 수 있는 도구를 이용하세요. 무료로 가입하여 오늘 브랜드 추적 설문조사를 사용해 보세요.