The feedback from survey responses is meant to give you insight into your target audience’s perspective and help you make better decisions. But if people don’t answer your questionnaire honestly, you won’t have valuable insights to improve your products or make better choices.

Attention check questions are a type of survey question that tests whether respondents are paying attention to the survey instructions and content. They help you identify and filter out respondents who are not engaged, dishonest, or answering questions mindlessly.

Understanding Attention Check Questions

Before we dive into the purpose and benefits of attention-check questions, here‘s some background knowledge of what attention-check questions are:

Attention check questions in surveys are questions that test if participants are paying attention to the survey. They help you ensure that respondents are not randomly clicking or skipping through the questionnaire.

What Are the Different Types of Attention Check Questions?

Most attention-check questions require you to listen to or read the question to pass them. These questions are also random, meaning you would only notice them if you were carefully going through the questions.

1. Instruction-Based Attention Checks

These instructs you to follow specific instructions, such as selecting a certain option or typing a word. For example, “To show that you are paying attention, kindly select the third option from the left”

2. Content-Based Attention Checks

These questions require you to recall or apply something from previous questions. For example, “Which of these designs was among the options presented previously?”

3. Response-Based Attention Checks

These questions assess whether your answers are logical and relevant to the research’s context or scenario. For example, “Rate your satisfaction with this product. (Please select one of the following options: Extremely Dissatisfied, Dissatisfied, Neutral, Satisfied, Extremely satisfied)”

E-learning Attention Checks 

This typically checks your understanding and retention of the course content. For example, in most online courses, you have to pass quizzes that test your knowledge about each module before moving to the next.

Examples and Scenarios Illustrating Attention Check Questions

User Experience Research Surveys

A survey about the user experience on a shopping app with an update can add instruction-based attention check questions. For example, “Kindly click on the cart icon to show you are paying attention to the survey”. 

For You: User Experience Research Survey Template

Gaming Surveys

Most games require cognitive skills, so gaming surveys typically have logical attention check questions to verify if respondents are their target audience by testing their problem-solving skills and logical reasoning.

Why Attention Check Questions Are Essential

  • Improves Data Quality

Attention checks enable you to spot inattentive or careless respondents who are answering randomly. This allows you to filter out low-quality responses that may affect your results or conclusions.

  • Minimizes Survey Bias

Another benefit of attention-check questions is that they help you to minimize response biases triggered by social desirability and acquiescence. They also help you detect fraudulent or dishonest responses that may come from bots, repeat responders, or paid participants.

Read More: Response vs Non Response Bias in Surveys + [Examples]

Designing Effective Attention Check Questions

The following are guidelines to help you design effective attention-check questions:

  • Appropriate Placement and Frequency of Attention Checks

Distribute the attention check questions throughout the survey, but not too frequently. Too many attention checks can annoy respondents and make them abandon the survey. 

  • Writing Unambiguous Attention-Check Questions

Ensure your attention check questions are easy to understand and answer. They should also be relevant to the content of the survey.

  • Create Realistic and Plausible Distractors

Don’t make your distractors too obvious, your attention check question should be a response the participants would choose if they are not paying close attention. For example, if the survey is about product design preferences, a distractor might be “I don’t know.”

  • Balance Attention Check Difficulty Level

The difficulty level of attention check questions should be balanced to capture varying levels of respondent attentiveness. 

If the attention checks are too easy, it will be difficult to distinguish between respondents who are paying attention and respondents who are not. Also, if it is too difficult the questions may frustrate respondents who are actually paying attention, and cause them to abandon the survey.

Consider Cultural and Contextual Relevance

Finally, make sure the questions are easy for your target demographic to understand. For instance, a question such as “How many inches is a picture frame?” might not be relevant to someone who uses inches to measure length.

Analyzing and Interpreting Attention Check Data

Identify Patterns and Trends in Attention Check Responses

Find out how often respondents fail attention checks. Also, determine the particular questions respondents are failing, and cross-reference them with relevant data such as demographics, time of day, and other survey variables.

For example, are respondents who don’t complete attention checks unhappy with your products? If the answer is “yes,” it’s likely they’re failing your attention test because they’re not interested in your products, not because they are distracted or trying to cheat.

How to Handle Different Types of Attention Check Failures

1. Intentional Failures

These are respondents who deliberately give wrong or random answers to the attention checks, either to speed up the survey or to sabotage the results. You can identify them by looking for inconsistent or illogical responses, such as selecting the same option for every question or choosing contradictory answers for the same question.

2. Unintentional Failures

These respondents miss the attention checks due to distraction, fatigue, or misunderstanding. In most cases, their answers are plausible but incorrect, such as picking an answer that’s close to the right answer or an answer that fits a different context.

How to Analyze the Impact of Attention Check Failures on Survey Outcomes

These attention check failures can affect the quality and reliability of your survey results. If you ignore these attention check failures and include the responses in your data anyway, they can introduce bias and errors into your  survey data

Potential Approaches for Treating Attention Check Failures in Data Analysis

There is no one-size-fits-all approach to address attention check failure in data analysis; different approaches have their pros and cons. You have to choose the approach that best fits your research objectives.

Here are some common approaches to handling attention check failure in surveys:

  • Exclude Failed Responses-  This reduces the bias and errors in your data, but it can also reduce your sample size and representativeness.
  • Adjust the weights or scores of attention check failures– You can do this by eliminating a certain percentage of failed responses. While this method preserves your sample size and representativeness, it can also introduce uncertainty and error in your data.
  • Use multiple imputations or techniques– You can also use statistical methods to replace the missing or incorrect responses from attention check failures. This improves the completeness and accuracy of your data, but it can also complicate your analysis, and drive up research expenses.

Advanced Techniques and Innovations

  • Adaptive Attention Check Designs

An adaptive attention check changes the difficulty or frequency of the check depending on the respondent’s behavior.

For example, using logic jump to skip some attention checks for respondents who have shown high engagement. You could also increase the number of attention checks for those who have failed previous ones. This way, you can reduce the burden on attentive respondents and discourage mindless responses.

Machine Learning and Natural Language Processing (ML/NLP)

ML/NLP makes your attention checks more challenging and less predictable. They allow you to generate realistic scenarios that involve respondents using their cognitive and logical reasoning skills.

For example, you can use ML/NLP to generate paraphrases of questions, detect synonyms or antonyms of words, or create distractors that are plausible but incorrect.

Incorporating Attention Checks in Mobile and Online Survey Platforms

First, you need to choose a survey platform like Formplus that allows you to incorporate attention-check questions. Next, integrate your attention checks into the survey platforms.

For example, you can add conditional logic to attention-check questions. So, if the respondents choose the wrong answer, the logic jump automatically closes the survey. 

This simple technique helps you to instantly identify attention checks from uncompleted responses.

Challenges and Limitations

Potential Limitations and Biases Associated with Attention Checks

  • Bias: Attention checks can introduce bias into survey data if they are not realistic or plausible. For example, if the attention check questions are too difficult, they may unfairly penalize respondents who are paying attention.
  • Increases Respondent Burden: Attention checks can frustrate respondents, especially if they are too many or irrelevant. These frequent or irrelevant interruptions often lead to a high survey abandonment rate.

Read – Response Burden in Surveys: Implications & Alleviations

  • Ineffectiveness: Attention checks may not be effective in catching all respondents who are not paying attention. For example, respondents who are deliberately trying to sabotage the survey can pass the attention checks because they are already anticipating the attention checks.

How to Overcome Challenges in Designing Effective Attention Check Questions

  • Write unambiguous questions: The questions should be easy to understand and answer, even for respondents who are not paying close attention.
  • Create realistic and plausible distractors: The distractors should not be outrightly incorrect; make them similar to the correct answer and relatively hard to detect.
  • Balance the difficulty level: The attention-check questions should be difficult enough to catch respondents who are not paying attention, but not so difficult that they frustrate respondents who are paying attention.
  • Educate respondents about attention checks: Inform respondents about the purpose of attention check questions at the beginning of the survey. This helps to reduce frustration and reduce the survey abandonment rate.

Handling Unexpected Respondent Behaviors and Strategies for Data Analysis

Several unexpected respondent behaviors can be handled when analyzing data from attention check questions. These behaviors include:

  • Respondents who answer all attention check questions correctly: Respondents who answer all attention check questions correctly may be paying attention or professionally beating the attention checks.
  • Respondents who answer all attention check questions incorrectly: Not all respondents who answer all attention check questions incorrectly are not paying attention, some genuinely don’t understand the questions. Ensure you factor in the difficulty level of the questions when analyzing the survey data.
  • Mixed responses: Respondents who answer some attention-check questions correctly and others incorrectly may be paying attention but making careless errors. They could also be professional survey cheaters trying to game the system.


Attention check questions enable you to identify distracted respondents, bots, and professional survey cheaters. This improves your survey data quality by filtering out poor-quality responses.

However, attention checks are not always effective, especially against well-experienced survey cheaters. But with the tools and methods mentioned in this guide, you can effectively spot attention-check errors and adjust your data to preserve the validity of your research.

  • Moradeke Owa
  • on 8 min read


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