Dive Deep into the Dynamics of Agree or Disagree Survey Questions


Agree disagree survey questions Descriptive Research
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Survey questions are essential for gathering opinions and insights from a targeted audience to understand their preferences, attitudes, experiences, and behavior. Agree-disagree questions represent a specific type of survey question that offers a straightforward format, allowing respondents to express their agreement or disagreement with a statement. 

There are various factors influencing the way your target respondents interpret and respond to the questions. Comprehending the dynamics of agree-disagree questions is crucial for obtaining reliable and meaningful survey results.

What are the agree disagree questions?

Have you ever interacted with a brand’s customer service for some reason and come across their customer support feedback survey asking questions like: 

Q1. Our customer service agent had the necessary knowledge about your query. 

  • Strongly agree 
  • Somewhat agree 
  • Neutral  
  • Somewhat disagree 
  • Strongly agree 

In survey research, these types of questions are called agree/disagree survey questions, clearly named after the answer options they have. Agree disagree questions allow the respondents to select their level of agreement and disagreement with a stated opinion in the question. 

Generally, agree-disagree questions have a range of answer options ranging from strongly agree to strongly disagree. So, the more detailed options you include, the more specific responses you can get from the respondents about their feelings. 

Although it looks like an easy question to frame and layout, we will discuss more about what tips and tricks you need to keep in mind while asking an agreement-disagreement question. 

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Biases affecting survey responses of agree-disagree questions

In this section, we will strive to comprehend the biases impacting the respondents’ choice of answer to agree and disagree questions. 

Cognitive bias:

Survey responses are susceptible to cognitive biases like confirmation bias and availability bias. 

A respondent may lean towards agreeing with your survey question that aligns with their existing belief regardless of its objective truth; this is known as confirmation bias. For example, a respondent with a positive perception of your brand is likely to agree with a statement supporting the brand’s product quality, even if the evidence is weak. 

Availability bias, on the other hand, leads a respondent to select a response based on recent or memorable events. For example, if a respondent saw a positive review about a product, they are more inclined to agree with a statement positively reflecting the product’s performance. 

Social desirability bias: 

People tend to choose the “agree” option because they think it makes them likable. When you look at a crowd, most of them prefer to have an opinion that will align with the majority rather than having an opinion of their own. This can bring bias in your responses.  

Social desirability bias can result from impression management and fear of judgment. For example, a respondent might agree with environmentally friendly products even if their actual behaviors don’t reflect this preference. 

Straight-lining responses:

Another challenge with agree and disagree questions comes up when the researcher decides to compile all the small questions into a matrix of agree-disagree questions. The main question will ask the respondents to rate the statements below based on their agreement or disagreement with them individually. Although, the grid design makes the respondents less aware of what they are selecting. 

This phenomenon is called straight-lining, where the respondents move through the statement, selecting the same answer options for all of them without thinking. 

Demographic influence:

Factors like age, income, gender, and education can significantly influence how your customers respond to the agree and disagree survey question. For example, younger consumers might agree to adopt innovative technologies in products in comparison to an older demographic. 

When designing survey questions, recognizing and mitigating these biases is crucial to capture accurate and relevant insights. 

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Designing a agree-disagree survey question

We understand the response biases involved in using agreement-disagreement questions in your survey, so let’s see how you can carefully design the question to avoid gathering biased data. 

How many answer options should you include?

In 2010, Lietz put forth a study on questionnaire design issues, concluding that between a 5-option scale and a 7-option scale, the 7-option scale is more reliable. The 7-option scale gives more options for the respondents to choose from. 

Her study advised using “weekly agree and weekly disagree” to the previous answer options. In contrast to Lietz’s study, in 1971, Jacoby & Mattell suggested that 3-point scales are enough when it comes to gathering responses to close-ended questions. More options bring in more noise, hence making it difficult to make sense out of closely related answer options like “Somewhat agree” and” weekly agree”, which have a very thin line between them. 

Should you include the “Neither agree nor disagree” option?

Studies have proved that including a middle option like “Neither agree nor disagree” or “Neutral” attracts 6-23% of respondents. The key concern with such middle options is that people tend to choose this option as a resort to just answer the question, which will satisfy the researcher by limiting cognitive effort.  

In 2010, Sturgis highlighted the real meaning behind “Neither agree nor disagree,” which can either be that the respondents “Don’t know” their opinion or they don’t have an opinion of agree or disagree. After researching the respondents who opted for “Neither agree nor disagree” and asking them what exactly they mean by that, the majority of them said that they did not have an opinion and that “Don’t know” would have been a better categorization. 

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The dynamics of the agree-disagree question play a pivotal role in shaping the survey results. Its simplicity makes it versatile and applicable in diverse fields such as market research, social sciences, healthcare, and more. 

To mitigate biases and enhance the reliability of survey results, crafting unbiased questions, considering the order, and pilot testing the surveys are crucial steps. The field of survey research is dynamic, and embracing robust survey software can contribute to improving the survey research process.

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