How to Analyze Quantitative Survey Data
Quantitative data is information comprising numbers or quantities. This kind of data is usually used in various research projects and is generally obtained from questionnaires, polls, and surveys. While qualitative surveys give open-ended questions, quantitative surveys mostly use a close-ended question format. Quantitative surveys may require answers to questions like “How much?”, “How fast?”, “How often?” or can ask respondents to provide simple “yes” and “no” answers. There are also scaled-question surveys that consumers can use to rate their experience with particular products on a scale, for example, one to ten. Surveys also allow making predictions on customer behavior and whether they will buy a given product. Collecting survey data is one thing, but successfully analyzing it is another – it is a challenging task. Here are some steps and methods that can be useful in this process.
1. Sample size
Make sure you have enough feedback on your survey before analyzing the data. The response rate may vary depending on which method you are using to conduct a survey. Online surveys reach a broader audience compared with traditional ones. If you are not sure how many people you need to survey, you can do a sample size calculation. There are many online tools that let you determine the right number of responses.
2. Survey participants and questions
You can get hundreds of respondents to take part in your survey and yet not get any desired results with it. That’s because you should have a clear target group, especially if you are doing a market survey. For instance, sending surveys to both long-time and new customers might result in different outcomes. These two groups of people may not feel the same about your product because one is using it for a long time and can have a biased response, and the other is a new customer who still hasn’t formed any opinion on it. You can do two surveys with different sets of questions according to that criteria and get two sets of responses.
3. Cross-tabulation
If your participants are all from different backgrounds, demographics, etc., you can use this method to analyze the data. This method puts the respondents in subgroups according to their place of living, gender, age, social status, etc. Using cross-tabulation ensures that you know how your target group answered the questions and that your surveys aren’t overrun by the “non-target” group. You should have a large enough sample size because every time you put people in small subgroups, your sample size for each subgroup decreases. As mentioned in paragraph number one, the sample size calculation is helpful in this situation.
4. How are the questions answered?
The next step is to look at the feedback on the different questions that are part of your survey. Check for common responses and see if they are repeated enough times to form a trend. It is generally accepted that you can draw a conclusion from your research survey if you see a trend. It is also possible to compare your survey results with previous ones if they are available. This way, you can check different patterns that have changed between past and current surveys.
5. Software
Large amounts of data can sometimes make it impossible to analyze it without using a proper tool. SurveyMonkey, Google Forms, Zoho Survey, Typeform, and similar software can make the process fully automated and reduce the turnaround time.