When data are analyzed, they don’t automatically tell you a story or indicate how to act on them. In order to act on the data, you need to make meaning of the data through a process of interpretation. This is the point when you look at the analyzed data and say “so what?”
This step helps you determine potential explanations for why the data came out the way they did so that you know what actions to take as a result.
For example, what does it mean if there is an increase in bystander incidents from the beginning of your intervention to the end of your intervention? Or what might it mean if the incidents did not increase?
Involving program participants in interpretation of data can provide rich and critical insights. This involvement can be relatively informal. For example, in activity-based assessment, data are collected in various sessions of a curriculum-based intervention to gauge learning integration along the way. It’s encouraged to bring concerning (or “unsuccessful” data) in to the following session to facilitate dialogue about why the participants may not have integrated the learning in the way you expected (Curtis & Kukke, 2014). This gives indications about whether the struggle was related to the evaluation instrument itself, the curriculum content, the facilitation, or something happening with the participants.
More formal options for participatory data interpretation include facilitating meetings where preliminary analyses are shared with stakeholders, and they are invited to offer feedback, reflections, and additional questions (Pankaj, Welsh, & Ostenso, 2011).
For one example of how to implement this, see the guide to using Data Placemats and What? So what? Now what? sections of the Training and Capacity Building Activities guide.
Participatory Analysis: Expanding Stakeholder Involvement in Evaluation (PDF, 9 pages) Published by the Innovation Network, this short guide offers advice, tools, and case studies related to involving stakeholders in data analysis.
Data Analysis: Analyze and Interpret (Online Course, free account required to log in) In part three of the NSVRC Data Analysis course, you will be able to identify types of data, analyze your data, and interpret your data with averages, changes over time, and differences between groups.
Once you’ve analyzed and interpreted your data, it’s time to answer the question: Now what?
If you are engaged in participatory data interpretation, these questions might be answered in that process, and then your job is to make good on the changes.
The “now what?” phase can help you figure out what might need to be shifted about what you’re doing and how you’re doing it. When looking at your data, you will want to consider the following questions.
What do we need to adjust about the evaluation process or tools?
Sometimes you will discover that the evaluation process or tools you used did not give you sufficient information to make judgments about the intervention or its implementation, which means your primary point of action will be to make changes to the evaluation itself to yield better data in the future.
For example, you might discover, as some other preventionists have, that the young people you work with complete their surveys haphazardly, circle the spaces between answers and write snarky comments in the margins. Or you might hold a focus group and discover that none of the participants has much to say in response to the questions you asked them. Either of these situations could indicate a problem with the questions/items you’re using or the methods of survey administration and focus group facilitation themselves.
What do we need to adjust about the nuts and bolts of the intervention (i.e., the program components)?
Perhaps the data show you that particular aspects of your programming are less effective than other aspects. For example, one preventionist noted that the data she collected from program participants consistently showed that they seemed to be integrating messages about sexism more so than they were integrating lessons about racism. It was clear that something needed to be tweaked about the discussions related to racial justice to make them more relevant and compelling to the participants.
What do we need to adjust about program implementation (e.g., the way it is facilitated, the skill-sets of the implementers)?
It also might be the case that the components of your program need very little tweaking while the implementation needs more tweaking. For example, maybe you are not reaching the right people or maybe the people doing your community organizing or program facilitation need additional skill-building to be more effective in their work.
Communicating About Your Evaluation
In addition to using data to make changes in the ways outlined above, you also need to communicate about your evaluation to your funders and community partners. This is part of accountability and also a way to celebrate your successes and help others learn from your work.
This communication might be relatively informal (a mid-evaluation update at a committee meeting) or might be more formal (a full evaluation report or presentation). Regardless of the occasion, the way you communicate about the program and its evaluation matters. Remember, this is your story to tell – make it compelling! Consider which angle of the story you want to tell and the purpose of telling your story. You might tell the story in slightly different ways to different audiences and to meet different purposes. For example, maybe your board of directors wants to see numbers, but your community partners would rather hear stories about your work.
Ways to Communicate about Your Data and Evaluation
Data visualization (also called data viz, for short) is exactly what it sounds like, ways of presenting data visually. As a field of practice, data viz draws from scientific findings and best practices in the graphic design and communication fields to help create powerful, data-driven images. The ever-popular infographic is a data viz tool that allows you to highlight important points with meaningful images in a succinct, easy to read way.
Charts & Graphs
Charts and graphs are visual representations of your data that make it easier for people to understand what the data communicate. Charts and graphs can be made in many computer programs that you might have on hand, including PowerPoint and Excel. These standard graphs and charts might need to be re-designed a bit by you in order to maximize their readability and impact. People have written entire books about this issue. (Seriously! Check out this one, for example, if you really want to nerd out about this.) If you don’t have time to read a whole book but want some good tips on how to work with charts, check out the video inspiration below. Quantitative and qualitative data require different types of visualizations, and it is important to choose a type of visualization that is both appropriate for your data and that also clearly communicates the implications of the data. The default chart that your data processing software chooses might not be the best or most compelling option! Fortunately, there are guides for choosing the correct chart for both qualitative (Lyons & Evergreen, 2016) and quantitative (Gulbis, 2016) data that can help guide you through those decisions when it is time to make them. We highly recommend you run what you create through this data visualization checklist.
Typically, people give a full rundown of their evaluation process and results in an evaluation report. These reports are shared with funders and other community partners. The reports are often long and contain more information than is useful to all interested parties, so some evaluation experts like Stephanie Evergreen recommend a 1-3-25 model that includes a 1-page handout of highlights, a 3-page summary, and a 25-page full report (Evergreen, 2015). Check out Stephanie’s Evaluation Report Layout Checklist; it will help you make sure your content and layout are maximized for easy reading and impact (Evergreen, 2013b).
If you are working with youth and need an interactive way to share data, take a look at Stephanie Evergreen's Data Fortune Teller tool and customizable form.
Infographics provide an opportunity for you to visually represent a variety of data points succinctly and powerfully. Generally, infographics consist of one page worth of data and information to communicate one or maybe two main points. Several online programs offer free or low-cost options for making infographics and include templates, images, charts, and options to upload or input data. Check out Piktochart and the infographic section of Animaker to see examples of what you can do. (Animaker lets you animate infographics to tell a more dynamic story!)
Stephanie Evergreen’s presentation 8 Steps to Being a Data Presentation Rock Star is a fun way to learn the basics about communicating about data (Evergreen, 2013a). While the presentation primarily focuses on creating slide-decks, the skills also correspond to creating data visualizations for reports and other types of communication media.
Communicating and Disseminating Evaluation Results Worksheet (PDF, 2 pages)
Evergreen Data: Stephanie Evergreen is a data visualization consultant who has authored two great books on data visualization. Her website and blog offer useful free resources, including the Qualitative Chart Chooser referenced above. You can join the Data Visualization Academy for more robust assistance.
DiY Data Design offers online courses and coaching around data visualization needs.
Data Analysis: Share Your Findings (Online Course, free account required to log in) In the forth section of the NSVRC Data Analysis Online Course, you’ll learn about data visualization to report and summarize your findings.
Curtis, M. J., & Kukké, S. (2014). Activity-based assessments: Integrating evaluation into prevention curricula. Retrieved from the Texas Association Against Sexual Assault: http://www.taasa.org/wp-content/uploads/2014/09/Activity-Based-Assessment-Toolkit-Final.pdf
Evergreen, S. (2013a, December 19). 8 steps to becoming a reporting Rockstar [Video file]. Retrieved from https://vimeo.com/82318228
Evergreen, S. (2013b). Evaluation report layout checklist. Retrieved from http://stephanieevergreen.com/wp-content/uploads/2013/02/ERLC.pdf
Evergreen, S. (2015). What TLDR means for your evaluation reports: Too long didn’t read (let’s fix that). Retrieved from http://stephanieevergreen.com/wp-content/uploads/2015/11/TLDRHandout.pdf
Gulbis, J. (2016, March 1). Data visualization – How to pick the right chart type? Retrieved from https://eazybi.com/blog/data_visualization_and_chart_types/
Lyons, J., & Evergreen, S. (2016). Qualitative chart chooser 2.0. Retrieved from http://stephanieevergreen.com/wp-content/uploads/2016/11/Qualitative-Chooser-2.0.pdf
Pankaj, V., Welsh, M. & Ostenso, L. (2011). Participatory analysis: Expanding stakeholder involvement in evaluation. Retrieved from http://www.pointk.org/client_docs/innovation_network-participatory_analysis.pdf