Choice Grids in Surveys: Definition, Examples, and Use Cases
Choice grids are one of the popular multiple-choice question types in surveys.
They are used for gathering complex data efficiently and are valuable for both researchers and respondents.
In this article, we’ll explain what you need to know about the choice grid in surveying, how it works, some common choice grid question types, and when to use it.
What is a Choice Grid in Surveying?
Different from other survey question types, a choice grid survey allows respondents to answer and evaluate multiple options using the same set of criteria.
Choice grids consist of rows and columns, making them more visually appealing to respondents’ eyes. And the more aesthetic the survey question is, the higher the response rate.
Choice grids also allow respondents to answer multiple questions at once. This is extremely useful for comparing brands, services, or behaviors.
One of the most common scenarios where choice grids are used is to evaluate respondents’ satisfaction levels across different features.
Or just as simple as a frequency rating survey, where respondents need to rate how frequently they use a feature based on a scale of 1 to 5.
How Choice Grids Work: Anatomy of a Choice Grid
The choice grid survey question type has two primary elements:
- Rows: used for representing items, features, or questions.
- Columns: used for representing response options.
Row options can vary from a long and detailed question to just a single feature name. And column options can be formatted in different formats like Likert scales, rating scales, binary options, etc.
Here’s an example of a choice grid question.
Benefits of Using Choice Grids in Surveys
Choice grids are efficient for respondents since they consolidate multiple questions into one matrix grid or table, reducing the survey length.
They also help researchers to analyze patterns across multiple variables much easier, as choice grids have a fixed set of response options (columns).
Moreover, choice grids allow respondents to consider multiple factors simultaneously, making the response more consistent.
Once a respondent answers a question, they can easily track back the previous answers and make comparisons. This ensures the accuracy of the survey and results.
Last but not least, choice grids are versatile because they can adapt to various contexts and scales. They vary from product satisfaction to product usage frequency, employee feedback, and more.
Common Types of Choice Grid Questions
Here are the 4 most popular types of choice grid questions.
1. Likert Scale Choice Grids
A Likert scale is a psychometric scale invented by Rensis Likert and is mostly used in surveys or research questionnaires.
When combined with choice grids, Likert scale choice grids help measure degrees of agreement and disagreement of specific criteria.
Here’s an example of a Likert scale choice grid question.
2. Binary Choice Grids
As its name implies, Binary choice grids usually only have two response options to let the respondents choose between.
The value of the binary options varies from Yes/No to True/False, or precisely two product names, services, features, or opinions.
Here’s an example of a binary choice grid question.
3. Frequency Choice Grids
Frequency choice grids can capture how often respondents engage with a feature, a company, or how frequently they do a behavior.
Here’s an example of a frequency choice grid question.
4. Importance vs. Satisfaction Grids
Another popular yet unique choice grid is the importance vs. satisfaction grid.
Other multiple choice grids usually let the respondents tick the radio button to answer. However, this choice grid type allows respondents to directly input the numbers for each question.
This is flexible, as you can measure both the importance and the satisfaction with specific features and questions.
However, the freedom it gives may confuse the respondents as they don’t know what to fill in. That’s why you should provide a clear instruction when using this choice grid type.
Here’s an example of an importance vs. satisfaction choice grid question.
When to Use Choice Grids in Surveys
Based on the four choice grid types and examples above, you should have an idea of what choice girds are and when you should use them.
Although choice grids can be used in many scenarios, they are best for product feedback, employee engagement, and market research surveys.
Choice grids in product feedback surveys are extremely effective in measuring satisfaction across multiple features using the same scale. The same happens with employee engagement surveys, where you can access various aspects of employee job satisfaction.
Market research or brand surveys are another use case of choice grids, as they can be used to compare preferences or opinions across various attributes or items.
Those are the cases when you should use choice grids in surveys. However, you shouldn’t use them in all types of survey questions.
For example, surveys with complex and lengthy response options aren’t suitable with choice grids, which may lead to fatigue or confusion.
Moreover, not all respondents can understand the matrix or grid structure, especially in younger groups. So, it’s essential to research your survey audience before using choice grids.
Overall, survey length, survey complexity, and respondent familiarity are the three criteria when considering using choice grids in your surveys.
Best Practices for Designing Choice Grid Questions
So, how to use choice grids in surveys?
To design practical choice grid questions, you must first keep the grid simple by limiting the number of rows and columns, as well as using clear labels.
Although choice grids are designed to show a set of answers and questions at once, too many rows and columns can overwhelm respondents.
Once the row and column numbers are limited, pay attention to their labels.
Are the labels concise enough so respondents can easily understand them at a few first tries? If yes, then you’re good to go. Otherwise, consider changing the wording and make it short.
Moreover, try to avoid using open-ended text fields in the question, as they can be hard to analyze and can confuse the respondents.
And let’s not forget about mobile optimization. You should optimize your choice grids to work generally on smaller screens.
Once all the steps are completed, pre-test your choice grids with a small group first to identify any problems. You never know what’s going to happen.
After the pre-test step, your choice grid questions are ready to go!
How to Analyze Data from Choice Grids
Once the choice grids survey is completed and you have enough data, it’s time to analyze the results to achieve your business goals.
Here are three ways to analyze data from your choice grids.
1. Aggregating Responses Across Rows or Columns
This method requires calculating the average number of respondents’ answers for each row or column.
Based on the average number, you can immediately know which criteria are highly rated and vice versa.
Here’s an example of a choice grid results with one new column (Total Average) and one new row (Column Average).
| Criteria | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree | Total Average |
|---|---|---|---|---|---|---|
| Product Quality | 5 | 10 | 20 | 40 | 25 | 3.7 |
| Customer Support | 15 | 25 | 20 | 30 | 10 | 2.9 |
| Pricing | 10 | 20 | 30 | 25 | 15 | 3.1 |
| Ease of Use | 2 | 8 | 15 | 50 | 25 | 3.9 |
| Feature Variety | 7 | 13 | 25 | 30 | 25 | 3.5 |
| Column Average | 7.8 | 15.2 | 22 | 35 | 20 | – |
How to Calculate the Total Average For Each Row
Let’s say Strongly Disagree has a point of 1, Disagree is 2, Neutral is 3, Agree is 4, and Strongly Agree is 5. And let’s take the Product Quality row as an example:
- Multiply each count by the Likert scale value:
- Strongly Disagree (5 responses) = 5 × 1 = 5
- Disagree (10 responses) = 10 × 2 = 20
- Neutral (20 responses) = 20 × 3 = 60
- Agree (40 responses) = 40 × 4 = 160
- Strongly Agree (25 responses) = 25 × 5 = 125
- Sum the total score: 5 + 20 + 60 + 160 + 125 = 370
- Divide by the total number of responses:
- Total responses = 5 + 10 + 20 + 40 + 25 = 100
- Average = 370 / 100= 3.7
A higher average score indicates a greater level of agreement or satisfaction, while a lower score means negative responses.
How to Calculate the Column Average For Each Column
Let’s take the Agree column as an example:
- Sum the counts for each column: 40 + 30 + 25 + 50 + 30 = 175
- Divide by the total number of criteria (in this case, 5): 175 / 5 = 35
Same as the Total Average number, the higher the column average is, the better.
2. Identifying Patterns
The second method for analyzing data is to compare the overall value of each answer, or better, compare the average numbers in the above method to indicate which feature or criterion needs improvement.
In the table above, Easy of Use has an average score of 3.9 and 3.7. Product Quality highlights that these criteria are highly valued by respondents.
In contrast, Custom Support only has 2.9, suggesting there are areas for improvement in this criterion.
3. Using Visualization Techniques
There are many ways to visualize the survey data for further research purposes. And one of the common ways is to use Heatmap.
Instead of displaying the number of respondents’ rates for each criterion, some polling tools have an option to show the data by colors.
Typically, darker colors represent a high level of satisfaction, while lighter colors represent the opposite.
Conclusion
That’s everything you need to know about choice grids in surveys and their effectiveness for collecting data for business success.
Choice grids are an excellent option for gathering complex data while ensuring efficient, consistent, and insightful surveys.
So, by leveraging choice grids and survey data analysis, you can streamline data collection, deepen your understanding of customer preferences, and ultimately make more strategic business decisions.