Surveys are an invaluable source of data for any organization looking to understand the needs and preferences of their audience. Interpreting survey results is an essential skill for any business or organization looking to effectively use data to drive better decisions. The challenge lies in understanding what the data is telling you, and the implications this has on your organization. In this blog post, we will discuss the different ways survey results can be interpreted and what should be taken into consideration when doing so. We will discuss the importance of having the right team members to interpret the results, how to effectively visualize the data, and how to use the results to inform decision-making. We will also look at what to do with the results once they are interpreted, such as creating action plans and integrating the findings into existing processes. By the end of this blog post, readers should have a better understanding of how to interpret survey results.
- Ask the right questions. …
- For open-ended questions, start broad and drill down. …
- Filter for key phrases. …
- Display results visually. …
- Use other data to understand (and sometimes discount) results. …
- Interpret through the lens of your goals—both overarching and current.
Important terms to know before interpreting results
Review the following terms and their definitions to properly interpret the survey results:
Why is interpreting survey results important?
Strong surveys gather detailed information about customers and offer useful insights into their thoughts and opinions. It’s crucial to understand how to interpret survey results so you can use consumer feedback to grow your business. Understanding your customers’ needs and making educated decisions to improve your goods, services, or business procedures can be aided by interpreting the results of customer surveys. Your business may benefit from the decisions and tactics you adopt in response to survey results by seeing an increase in customer satisfaction and sales.
Ways to measure survey results
You must decide how to sort and categorize the survey responses before you can measure the survey results. Statistical scales can assist you in performing an effective analysis to correctly interpret data and in understanding how to measure survey questions. You can measure survey data using various statistical scales, including:
Ordinal scale
An ordinal scale determines the ranking or order of variables. “Rank these 5 TV shows in order from your favorite to least favorite, with 1 being your favorite and 5 being your least favorite,” is an illustration of how to use an ordinal scale. “.
Since people are ranking their favorite TV shows in numerical order to reveal their overall opinions of each show, an ordinal scale is appropriate to be used in this example.
Nominal scale
Using categories or labels, a nominal scale measures variables that provide qualitative data. Heres an example of when to use a nominal scale:
Which of the following best describes your hair color: Blonde, Brown, Black, Red, or Other? “.
These choices are unrelated to one another, but you can see how many people selected each choice and which choice received the most votes.
Interval scale
Without a true zero, an interval scale measures the order and variations, or intervals, in variables. To gain more understanding of the data, it employs labels, meaningful ordering, and intervals. This scale is useful for quantitative data. An example of an interval scale is time. Time can provide measurements in hourly intervals. An example of measuring time in an interval scale is:
Choose from the following options to indicate what time of day you shop at Watts Superstore: 9AM-11AM, 12PM-3PM, 4PM-7PM, or 8PM-11PM. “.
This survey uses intervals of time to determine which time frame is most and least popular among its respondents. This might enable Watts Superstore to investigate why these times are popular or unpopular and how to make them better.
Ratio scale
A ratio scale has a true zero and measures the spaces between the variables. It makes use of labels, meaningful order, and intervals, much like an interval scale. An example of a ratio scale is:
Choose from the following options to determine your weight in pounds: 0-60, 60-100, 101-170, and more than 170. “.
Use of a true zero marks the primary distinction between a ratio scale and an interval scale. A true zero indicates that a variable or factor being measured by the survey is completely absent. A ratio scale is used, for instance, to measure weight because it can measure an absolute zero.
5 steps to interpret survey results
Consider using the following steps when reviewing and interpreting survey results:
1. Review survey question s for relevance
When analyzing survey data, it’s crucial to keep in mind what you hope to learn or accomplish from the results. Consider the problems you hope to address with the survey and contrast them with the inquiries it poses. This can assist in determining whether the inquiry will yield information relevant to your long-term objectives.
Consider how each question might be related to customer satisfaction, for instance, if you want to know whether customers are satisfied with a product. By determining the likelihood that a customer will use a product in the future, the question “How likely are you to purchase this product again?” could reveal how satisfied the customer is with it.
2. Use cross-tabulation to filter data results
In order to find connections or relationships between the variables in the data, one data analysis technique called cross-tabulation divides the data into comparable rows and columns. Cross-tabulation is sometimes referred to as the use of data tables. Once you’ve decided on the measurement scale to use, you can use cross-tabulation to give your results more context or meaning. This can assist you in comprehending the potential reactions of various variables or factors to particular outcomes.
For instance, Kitten Cosmetics wants to know if customers who are 27 or older or customers between the ages of 18 and 26 are better at purchasing its new makeup line. From the survey results, the business then creates a cross-tabulation to determine the number of sales by age group. The company may be able to determine from the cross-tabulation that consumers 27 spend $10 more on the new makeup line on average each month.
3. Identify any trends or patterns
After reviewing survey responses, you can quickly come to conclusions by looking for patterns and trends. For instance, a business might use a survey to determine whether to keep selling a product. They discover a pattern in the survey data that indicates many respondents are currently satisfied with the product and intend to buy more of it in the future. This information can encourage the business to keep selling the product because it is still useful and popular with customers.
4. Identify the statistical significance
Examine survey results to identify any factors that might have an impact on the statistical significance of your data. Statistical significance in survey analysis evaluates the accuracy and dependability of data. Statistical significance also means that survey results accurately reflect the opinions of the intended audience. If survey data from a sample group has statistical significance, it can be used to estimate how a population thinks or behaves. It’s crucial to make sure your sample size is sufficient to deliver reliable results because larger sample sizes might result in more accurate data.
5. Use correlation vs. causation to draw a conclusion
Making precise inferences from survey results is a crucial component of survey interpretation. Take into account the correlation and causation between variables to help you assess the accuracy of your conclusions. Examine survey trends to determine which variables have a direct impact on one another and which ones are merely correlated.
Ryan is compiling information, for instance, on attendance and grade point average. He discovers that pupils with high attendance rates also have high GPAs. After that, he discovers a connection between attendance and GPA. Since there may be an unidentified variable influencing attendance and high GPAs, he does not draw the conclusion that poor attendance results in low GPAs. Prior to drawing any conclusions or making assumptions about the data, analyze and identify all variables.
Tips for interpreting survey results
Consider the following advice after reviewing how to interpret survey results:
Use a software program to streamline interpreting results
Interpreting data manually is a traditional form of survey analysis. Modern technology can allow you to streamline interpreting survey results. Usually, software programs are best for managing large-scale data measurements. Excel is a typical piece of software used for analyzing survey results. Excel is useful for carrying out basic measurements, such as categorizing different survey response types into data fields and computing the percentages of each survey response type.
Show visual results
Visual results, such as charts or graphs, may occasionally be simpler to understand than written results. The best data visualizations may highlight relationships, patterns, and changes. Charts, graphs, and diagrams can be used to organize survey data and illustrate the meaning of the data.
Establish benchmarks
Think about benchmarking to gather information in the future to compare to other surveys. Benchmarks can create a baseline value or number to assist you in tracking data over a long period of time. Important information to help you improve your processes, products, or services can be produced by understanding how results change from their initial baseline value or benchmark.
Please note that Indeed is not affiliated with any of the businesses mentioned in this article.
How to Analyze Results from a Survey | Step by step guide
FAQ
What is a good result for a survey?
A survey response rate below 10% is considered very low. A good survey response rate is anything above 50%. In the end, the quantity of responses you receive for your survey—rather than the response rate or completion—is what matters most.
How do you Analyse qualitative survey results?
- Prepare and organize your data. Get your notes, documents, or other materials ready, print your transcripts, and
- Review and explore the data. …
- Create initial codes. …
- Review those codes and revise or combine into themes. …
- Present themes in a cohesive manner.
How do you Analyse the results of a quantitative survey?
- Make simple comparisons to identify customer preferences. A sample question from a survey with multiple choices that was created to identify user preferences
- Utilize cross-tabulation graphs and charts to contrast outcomes from various audience segments.
- Analyze scale data using mode, mean, and bar charts.
How do you interpret survey results using Likert scales?
A series of statements will be presented to you if you’re participating in a Likert survey, and you’ll be asked to choose whether you “strongly disagree,” “disagree,” “slightly disagree,” “are undecided,” “slightly agree,” “agree,” or “strongly agree.” The researchers conducting the study will assign a point value to whichever response you select, and
