It is important to ensure that you effectively perform survey analysis to extract valuable insights from the survey data. Here are some steps to follow in order to conduct survey analysis productively.
01. Structure survey questions for easy analysis:
When you design your surveys, make sure the questions are structured to help you gather precise data, which makes analysis easier. Here are some survey design tips on how you can structure the survey questions:
- Use closed-ended questions as it provides pre-defined response options which are easy to code and analyze.
- Make sure the response scale is consistent throughout the survey. If you use 0 for a negative response and 5 for a positive response, use the same order for other scale questions as well.
- Avoid double-barreled questions, as these can lead to unclear responses.
02. Review your research questions:
Consider the data you have collected in the context of the research question. It is important to review your research question before you begin data analysis. This help you determine if the survey aligns with your purpose. Reviewing the main research question ensures that you have the data you need to make effective decisions.
03. Clean your collected survey data:
Before you perform data analysis, clean and organize the survey data. Online survey analysis tools automate data cleaning to ensure consistency and accuracy in data. Here’s how it prepares your data for analysis:
- The tool removes duplicate responses that can skew the analysis.
- It removes invalid data, such as incomplete responses or those that contain nonsensical responses.
- It checks for data that falls outside the expected range of response.
- The tool then standardizes the data to ensure consistency across the survey data.
- Finally, it categorizes the data into relevant groups. For example, if the questions are related to the customer’s product experience, the tool will categorize data by product type.
04. Use cross-tabs to look for patterns & trends:
Cross-tabulation is an important step in quantitative data analysis. It helps break down data into sub-groups and compare relationships between variables.
For example, say you want to gauge customers’ satisfaction with different products from your company. You can cross-tabulate and show the satisfaction level for each product type.
This helps you look for patterns and trends across survey data. You can perform cross-tab in your survey data in multiple ways with multiple sub-groups across different questions.
05. Consider a qualitative analysis to uncover hidden intent:
Qualitative data can provide contextual insights into your quantitative data. Gather respondents’ unfiltered opinions and use text and sentiment analysis to break them down into meaningful insights. The online survey analysis tools can help map the words used by respondents to positive or negative sentiment.
With text analysis, you can uncover hidden meanings and identify themes and intent. When you are looking for data analysis software that can analyze qualitative data, make sure it also offers sentiment analysis.
06. Compare responses across different groups:
To make the most out of the survey data, compare responses across different groups of respondents. You can categorize them based on demographics, product purchase, or some other variable. This will help you identify patterns in behavior, attitude, preferences, and experience across different groups.
With this insight, you can create targeted marketing and identify groups where tailored outreach efforts can boost conversion.
Additional read: How to analyze survey data?