Organizing and Interpreting Complex Sets of Survey Data

Large surveys make the task of data analysis much more complex. In order to uncover meaningful information from your large survey efforts, you will need to define a logical process for organizing and interpreting survey data at the start. While there are numerous techniques you could employ, the following offers a series of reliable techniques for working with complex sets of survey data.

Organize by Priorities

Asking respondents to rank the importance of items makes it possible to organize data into a single actionable table. For example, if you are conducting a survey to determine features that should be include in a new product, ask participants to respond important or not important for each feature. Organize this data into a table that highlights the percent of participants saying the feature is important and you will have a clear visualization of which features are important to your sample group. Below is an example of a chart with ranked features.

Organize by Variables

You can understand relationships by asking respondents to rank the importance of items based on variables. For example, ask participants to respond important or not important for each feature at different price points. Organize this data as a scatter plot to understand how price impacts the importance of each feature. See the example scatter plot below.

Compare Segments

Breaking data into segments, such as geographic location or gender, provides an entirely different perspective. You may find that certain features are important in one location but not another. For instance, heated front and back seats in a vehicle would be more important to consumers who live in colder climates vs warmer climates. If your product distribution strategy varies by location, comparing these types of segments in your survey data will prove helpful.

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