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In survey research, it is important to carefully select the question type to gather the data you need. In this blog, we will explore in detail one such question type that gathers quantitative data. This question type is known as ordinal survey questions or ordinal scale questions.
Capturing people’s responses in a way it will be of better use to your research is the most challenging thing, especially when the survey runs deep and long. Sometimes you just want to get quick one-word or direct, measurable answers to most of the questions and don’t expect the respondents to reply with long paragraphs. In such cases, ordinal scales really come handy.
An ordinal or ordered scale helps you to study the respondents’ attitudes towards your research topic through a defined set of ordered responses or answer options provided with the question itself. You might have come across a lot of survey questions with options like: “very good” “good” “average” “bad” “very bad”. This is the ordinal scale.
The ordinal scale basically presents the respondent with answer options to choose from depending on which one suits their views and opinions the most. The only limitation is that sometimes the meaning is unclear between similar options like “very satisfied” and “satisfied. It can be relative but not exact.
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Ordinal questions in a survey are best used when you want to gather data about the attitudes or opinions of the target audience on a particular topic. It is best used when the response options can be set up in an order on a scale. Here are some use-case examples of ordinal scale survey questions.
Do respondents “agree” or “strongly agree” with a take on an issue?
This question type is a great way to gather nuanced data about the respondents’ opinions. By providing respondents with a range of options from “strongly agree” – “strongly disagree,” you can gain insight into the degree to which respondents hold feeling about a particular topic.
Do respondents find a particular statement “false,” “mostly false,” “mostly true,” or “true”?
It can also help you gauge the perceptions or beliefs of the survey respondents. You can use it to identify areas where customers may hold misunderstandings or misconceptions about your brand.
For example, you can gauge customers’ perspectives about your brand value. The question can also help you learn if your target customers understand what your brand does and what value the products offer.
Is a certain employee “more productive,” “just as productive,” or “less productive” than other employees?
Ordinal survey questions can help you compare or evaluate relative performance. For example, you can help it to identify where certain employees may be excelling. This can help you identify areas where some employees might need additional training.
Is a customer “very satisfied,” “satisfied,” “dissatisfied,” or “very dissatisfied” with a recent purchase?
Our fourth use case is that ordinal scale survey questions can also help you gauge the satisfaction levels of the customer and uncover sentiment. It can help you identify what contributes to poor customer experience so you can make necessary changes and improve customer satisfaction.
Ordinal survey questions are a powerful tool to gather detailed and nuanced data. To make the most of this question type, you need to design the question and select the appropriate response options carefully. In the next section, we’ll show you how you can design ordinal question surveys.
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Ordinal survey questions are the ones where the answer options provided are ranked according to their significance or importance depending on the questions.
There are two basic types of ordinal survey questions:
Unipolar: has one dimension and the zero point at the end of the scale.
Bipolar: has two dimensions and the zero point is in the middle of the scale.
Ordinal questions are said to have 2 to more than 100 options, but the key to choosing a perfect scale for your questions lies in how much of a detailed answer you wish to have.
Ideally, it is ok to have 4 or 5 answer options for each question.
More options: it will create more generic and nuance between responses and will leave you with more data to analyze in the end.
Fewer options: it will be lighter on response load and there will be a low chance of respondents skipping the questions just because it looked like a “burden”
It is advised to have 5-7 options for bipolar ordinal scales and 4-5 options for unipolar ordinal scales.
The common question that arises when people start to frame ordinal survey questions is “should I start with positive or negative options?” everyone gets too sceptical regarding whether the first options influence the respondents choice or not. The truth is it doesn’t matter. Research has proved that keeping the positive option “very satisfied” or the negative option “very dissatisfied” does not make any effect on how the respondents actually feel about the question asked.
So the key here is to choose whichever option you want to put first, but be consistent. Make sure you follow the same pattern through the survey.
Any ordinal question looks like this:
How would you agree/disagree with the fact that our website assures data security?
The problem here is, you will get your answer and it is although a very easy practice to copy-paste the options all over the survey. But directing the options to the construct of interest will help you get more focused and specific answers. This practice will also help you in the data analysis and the cognitive load of the responses. So the construct of interest focused questions would look like this:
How secure or insecure is our website regarding your data?
Make sure to use specific metrics or measures to your questions rather than having vague and confusing quantifiers. Example:
How often do you exercise?
Now, these quantifiers are going to be interpreted differently by different respondents. For some the meaning of “rarely” and “occasionally” might mean the same. In such cases, it is helpful to be more specific with your questions and the ordinal scale. Example:
How often have you exercised in the past week?
This gives a frame of reference to the respondents and measure their exercise routine and match it to the suitable given options.
Most of the time especially in bipolar scales, the ordinal survey questions have a scale that is not equally balanced for the respondents to choose their option from. Example:
How would you rate our customer service?
As you can see, the first three options imply the “positive” response while it is a 5 pointer scale. Hence, making it an unbalanced ordinal scale. This gives the respondents very few to no options to choose from if they have a negative response or something slightly less.
The more appropriate way to frame this question is:
How would you rate our customer service?
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In this section, we will see some commonly used ordinal survey questions and patterns. Here we will show you some examples of ordinal scale survey questions to give you inspiration for your survey design.
How would you rate the efficiency of our products?
How much are you satisfied with our work environment?
How many connections do you have on LinkedIn?
These are a few of the ordinal survey questions examples. There are more question types you can use along with this to design an engaging survey. Leverage an online survey tool that offers 100+ question types, from basic to advanced question types.
We have learned how you can design ordinal question surveys and also look through five different ordinal survey question examples. Now, let’s explore some advantages of using this question type in surveys.
Ordinal scale questions allow respondents to express their level of agreement and disagreement about a particular topic. For that reason, it is the most commonly used survey question to measure attitude and opinion.
For example, you can use an ordinal question to ask respondents their level of agreement/disagreement with the statement – “I believe the customer support offers an effective solution.”
Using a scale ranging from “strongly agree” to “strongly disagree,” you can gather respondents’ opinions regarding customer support. It can also help you understand if you need to deep dive more into the experience delivered by customer support.
Multiple choice, or rating questions, ordinal survey questions offer more flexibility in their response option than any other. You can ask multiple ordinal questions in multiple formats to make your survey interactive and keep respondents engaged. The flexibility in responses enables you to gather nuanced responses that accurately reflect respondents’ experiences.
For example, an ordinal question asking employees how often they experience racism in the workplace can offer respondents a scale of 0 to 7 ranging from never to always. It can help you understand the frequency and also give you a glimpse of the employee experience.
Ordinal scale survey questions use clear and concise language and offer response options that are easy to interpret. The question and its intention are easy to understand, so the respondent doesn’t need specialized knowledge to understand the question. This makes it more likely that respondents will provide meaningful responses.
Additionally, the response options used standardized order, which ensure that the data is easy to analyze.
Ordinal scale survey question is easy to interpret and allows you to gather nuanced data. However, it may not be ideal to rely on one question type to design an entire survey. Here are some reasons why you shouldn’t use it as a stand-alone question.
Ordinal survey questions provide quantitative data, which means you gather limited information on respondents’ satisfaction, opinion, attitude, and experience. The responses indicate the level of agreement or disagreement but don’t provide additional context on why they feel that way.
The data makes it difficult to extract meaningful insight. To gather reasons for respondents’ choice of response, use open-ended questions. Ask respondents to share why they feel that way so you can understand the respondents’ experience.
The “somewhat agree” may have different meanings to different respondents depending on their experience. This makes it difficult to compare responses between different groups. Additionally, it also makes it difficult to put the responses into context without the respondents’ explanation for their choice of response.
While ordinal scale question can help measure attitude and opinion, it has some limitations. When designing a survey, you must carefully consider the advantages and disadvantages of the question type to ensure successful research.
Ordinal survey questions are a valuable tool for gaining insight into respondents’ attitudes, opinions, and preferences. The question allows respondents to indicate their level of agreement and disagreement on a scale. It provides a more nuanced understanding of your survey question.
When creating a survey, it’s important that you consider the question types you use. Ordinal scale survey questions may offer insightful data, but they are not always the best choice. You must consider the goal of your survey, the target audience, and the type of data you want to gather before deciding on the question types to use.
Leverage online survey tools that give you access to 100+ question types to create interactive surveys. Select the type of question based on the data you hope to gather and design a branded survey with a smart flow.
An ordinal scale survey question asks survey participants to rate their opinion, experience, or agreement on a scale that has a specific order. In the ordinal scale, however, the responses don’t have consistent distance.
A common example of an ordinal scale is the Likert Scale.
Here are some examples of ordinal scale survey questions that you can use in your survey:
Ordinal scale survey question offers more nuanced data than dichotomous survey question. The easy language and precise response options make it easy for participants to respond.
It provides you with structured data that is easier to analyze.
You can use descriptive statistics to analyze ordinal scale data. You can use descriptive models such as frequency distributions, mean and median scores, and measures of variability such as range and standard deviation.
It is important that you consider the context in which you gather the responses, such as sample size and the demographics of the population.
You can use inferential statistics to compare between groups. You can use statistical models such as chi-square tests or t-tests.
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