Analysing Community Engagement Feedback: Is Your Data Sorted?

Bang the Table’s Australian Practice Lead, Dan Popping, provides essential tips on planning an engagement project and how the building blocks of data in the design process lead to a better chance of success.

Is your Community Engagement data sorted?

Community Engagement Database

I saw this picture on the internet the other week and I was instantly struck by the simple message it conveyed; “analyzing data can be a complex task”. It started me thinking about the importance of data in a community engagement project.

I’ve been working in the community engagement sector for a long time now, and over the years I’ve seen the profession evolve. I’ve also seen an improvement in the quality of engagement projects being planned and delivered.

More recently, however, I’ve seen a notable change in both the volume and the way organizations are ‘reporting back’ community engagement results and ‘closing the loop’ on their engagement projects.

For example, the Shire of Hume recently used an infographic to report back outcomes from the first engagement stage (of a multi-staged process) to reiterate what they’d heard and what they’ve done with the input received. More recently, Inner West Council is providing regular updates on their engagement projects and provide an ‘outcomes report’ to share the collective data received and what they heard from their community.

Hume City Council Infographic

When organizations are open and transparent in using community input/feedback into their decision-making processes, raw and analyzed data will help to justify and explain why hard or unpopular decisions are being made. 

Houston, we have a problem!

But what if you get to the end of your engagement process, and you realize that you don’t have the right data. Maybe you asked the wrong questions. Perhaps the responses did not match your expectations. Well, there’s not much you can do now.

The answer moving forward is in the image above. By considering how you want to sort, arrange and analyze your data when you are designing/planning your engagement process, you will have a better chance for success. By considering this in the planning phase you will think about your engagement data when choosing the methodology and tools, be more likely to ask the right questions, which will help you to collect key information (e.g. demographic, geographic, or interest levels). So you can effectively sort, filter, and group your data to gain key insights and community sentiment.

Quantitative vs. Qualitative Feedback

Community engagement data is often referred to as either quantitative or qualitative. You will likely collect both types so it is important to know the difference.

Quantitative data refers to the type of data that is measured in numbers. For example, how many people responded ‘yes’ to support a new library being built? On the other hand, qualitative data is non-numerical data and often captured as an additional comment, observation record, documented lived experience, or an idea.  In this example, it could be comments received for why people ‘do not support a new library’. 

Planning your engagement with a view to what you will do with your data is therefore very important. With this in mind, you will start thinking about the data (blocks) you are trying to collect, the type of questions you will ask, and how you plan, filter, and compare this data, to gain insights from your community and/or stakeholders.

Analyzing Your Community Data

Filtering is an easy and common way to start analyzing your data. For example, you may plan to filter your data or survey results by a specific question you asked, i.e. age of respondent. In this example, you are planning to report on the number of people who responded via age categories. This will help to know what age our respondents are and if you have heard from a wide cross-section of your community, or are perhaps unrepresented in a particular age group.  You could apply a second filter and then look at the ages of people who responded, who also said they did not support the new library. This may provide insights into what age cohorts support or do not support your proposal. You might present this data in a chart, graph, or diagram to show the ages of people who support and don’t support the new library. 

Comparing responses is the other common way to analyze data, essentially stacking it ‘side-by-side’ for a particular comparison filter. For example, you might compare the number of responses of support from neighboring residents compared to that of the general community. This may provide greater insight into their differences of opinions, based on their proximity and perceived impact.

You can continue your analysis even further by taking a deeper dive and reading all the comments provided by respondents who identified as a ‘neighboring resident’ and also said they ‘do not support the new library’. 

Community Data Analysis Tips

To get you started, here are a few tips to consider when planning your next engagement project and being clear about what you are going to do with all the great information in your community data.

  • Start with the end in mind. Plan for the end data you need/want to collect and design your engagement accordingly.
  • Consider early, who and how you intend to analyze the data and what reports will be written.
  • Consider some questions to help you group and filter your community data, like interest areas, demographic data, geographic information, or level of impact.
  • Don’t ask questions to your community if you don’t intend to use the data.
  • Use a combination of both quantitative and qualitative questions.
  • Be careful about using too many ‘qualitative’ questions and what will you do with all that ‘verbatim data’. 
  • Share your insights, analysis, and engagement reports with all stakeholders and participants.
  • Consider how you will combine data/feedback from multiple sources or different engagement tools (eg online, paper-based, video, etc).
  • Don’t ask questions you already know the answer to.
  • Be mindful about anonymous participation, you might get lots of data/feedback, but what will be the integrity of the content?
  • Trial and test your engagement questions with others first, modify and finalize as appropriate.

There are many different ways to sort and analyze your data, and EngagementHQ has a number of built-in reporting and data analytic tools to assist and guide you through your analysis and reporting. 

Get in contact if you are interested in finding out more.

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