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.
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.
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.
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”.
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.
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.
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.
The following are guidelines to help you design effective attention-check questions:
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.
Ensure your attention check questions are easy to understand and answer. They should also be relevant to the content of the survey.
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.”
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.
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.
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.
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
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:
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.
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.
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.
Several unexpected respondent behaviors can be handled when analyzing data from attention check questions. These behaviors include:
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.
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