Satisfaction survey analysis using statistics
Source: www.spss.com
Copyright SPSS, Inc. 2004


Satisfaction surveys are an important tool for assessing the satisfaction of your customers, employees, patients and readers. Without a reliable way to analyze the responses, however, you risk making important decisions based on incomplete or superficial information.

Spreadsheets and databases give you simple summaries and basic row-and-column math. To best interpret and understand survey responses, you need in-depth analysis unavailable in spreadsheets and databases. When you use statistical software for satisfaction survey analyses, you get the most value from your data.

With statistical analysis, you can translate your survey responses into meaningful information and gain more insight into the responses. More insight, in turn, leads to better decisions. Using statistics to analyze your survey data helps ensure you’ll be delighted with the results.


12 ways statistics are better than spreadsheets for satisfaction surveys

Whether you are a beginner or a savvy, experienced survey researcher, these 12 ways show you how to better analyze your survey responses and present your results using statistics. They demonstrate why statistical software is a necessity for your analytical solution. A brief summary of the 12 ways appears at the end of this paper.

1. Use all your data efficiently
Combining and manipulating data holds the key to important results.
For a thorough analysis of your satisfaction survey, you need flexible data management. Combining responses from separate studies can be the key to spotting trends or patterns in your data. For example, merge the responses from your 1995 survey with the 1996 survey, and you can compare satisfaction scores between quarters or years to monitor improvements over time.

2. Know when there’s a problem with your data
Unusual responses affect the results of your satisfaction analysis and influence the decisions you make. It is important to know whether an unusual response is the result of a data entry error, and should be corrected, or whether it reflects a true relationship that exists in the data and should be considered in your decision.

3.Work easily with words, instead of numbers
Responses to satisfaction surveys use many questions with answer choices in categories, such as Male/Female, Yes/No, age ranges and scales of 1-5. When dealing with unfamiliar data, it can be difficult to remember what every coded answer represents. Often it’s more intuitive to work with words rather than numbers.

Satisfaction surveys often re-use questions and response options from previous surveys or within the same survey. For example, one survey may include several items asking about a customer’s rating of various products, all on a scale of 1 = poor to 5 = excellent, and coding “no answer” responses as 9.

4. Get accurate results even when some data are missing
For many reasons, satisfaction survey respondents do not always answer every question. Missing responses occur when a question is not applicable, a respondent refuses to answer, or the respondent simply doesn’t know the answer. Gaps in your data influence your analysis and results.

5. Understand the big picture to make good decisions
Tables of numbers alone do not tell the full story in your data. Sometimes it’s necessary to see a picture of your data to completely understand the results. A visual representation of your data often helps identify problems or opportunities you may not have discovered in the numbers. A spreadsheet would tell you averages and ranges, but a statistical graph, such as a boxplot, shows you more information.

6. Make better decisions by knowing what’s significant
It’s not enough to look at simple reports and try to draw conclusions from them. Often you notice differences or relationships that look interesting. For example, satisfaction scores may differ between groups. Perhaps new customers are more dissatisfied with your delivery times than long-time customers. Or, satisfaction with store A looks lower than store B. Perhaps the satisfaction score from this year is lower than last year. But are these findings really important? Are the differences enough to be “statistically significant?”

7. Save time and money using small samples
Sometimes, you survey less than 50 respondents or only a handful of people return the questionnaire. Other times, you want to subset your data into small groups. For example, you analyze results by department, but many departments have only a small number of employees. 

8. Separate the apples from the oranges
Satisfaction may differ among groups. Before you make recommendations, it’s important to track down where the differences occur and by how much. Are there differences by type of customer, region, patient type, age, gender or user vs. non-user? By identifying key differences, you can target your efforts where they will be most valued.

9. Look at your data from all the angles
A key to successful satisfaction survey analysis is looking for relationships in the responses. What you first see in your results often triggers additional questions. For example, how do the results from group A compare to group B? 

10. Present multiple responses clearly
Multiple response questions are common to satisfaction surveys. These questions ask the respondent to “check all that apply” or select more than one response. Multiple responses pose a challenge to presenting results. This is because you can calculate statistics and percentages based on the total number of all answers (within responses) or the total number of people who answered (within respondents). For these questions you need to look at your results in both ways.

11. Get your point across effectively
It’s important to display results that highlight the information you want to emphasize. If your audience doesn’t understand the point, then your thorough analysis is wasted. Get your point across clearly with reports that are easy to read and easy to interpret. Since important business decisions are based on the results of your survey, make sure you communicate clearly.

12. Use the right tool for the job to save time and increase productivity