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by Amelia Kohm
Each of us has a number of tools we use regularly in our consulting practices. A well-worn item in many of our toolboxes is the survey. Whether we are doing strategic planning, marketing, organizational development, or evaluation, we often need information best gathered through a survey of current or potential clients, participants, audience members, board members, staff, etc.
It’s easy enough to create a survey on Survey Monkey or the like. It's harder to get an adequate number of responses. And even when you do, the respondents might not fairly represent the larger group you want to know about. But let’s say you get past these hurdles. There’s still a major hurdle ahead of you: extracting meaning from your data.
Surveys include different types of questions. Perhaps the most common one is the Likert scale question which asks respondents to indicate how much they agree or disagree with a particular statement using a five to seven point scale.
Many consultants and organizations will assign numerical values to response options (5 for strongly agree, 4 for agree, 3 for neutral, 2 for disagree, and 1 for strongly disagree) and then compute averages across respondents. But there is so much more information in those numbers than averages can tell you, including:
The extremes: Averages can’t tell you what were the lowest or highest ratings on any given statement.
What most respondents said: Let’s say an average response is 3. This number doesn’t tell you if most people responded with a 3 or if half responded with a 5 and half responded with a 1. More broadly, averages can’t tell you how spread out the data is. Are there similar numbers of responses at each point in the scale or do they bunch up around certain values?
What subgroups think and feel: Even though the overall average might be high, the average might be low for some subgroups within your group of respondents. Perhaps respondents from a certain neighborhood, for example, had very different opinions than the group overall.
You can extract and show this type of information using data visualization tools like Tableau. Compare this simple list of averages of responses to several survey questions . . .
. . . to the chart below which shows the range of responses to each survey statement, the proportion of responses for each rating, and the overall average across survey statements (the gray vertical line) in addition to the averages which appear in the gray circles.
Moreover, the interactive version allows you to “drill down” into the data and see if whole group results hold for subgroups.
If you are going to go to the trouble of conducting a survey, make sure to squeeze all of the information you can from the data you collect.
Amelia Kohm, PhD, is the founder of Data Viz for Nonprofits and has more than 20 years of experience studying, funding, and evaluating human services. Data Viz for Nonprofits (nonprofitviz.com) delivers high-quality, low-cost visualizations that help organizations to quickly grasp their data, improve their work, and show their impact.
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