AEA365 https://aea365.org/blog A Tip-a-Day by and for Evaluators Mon, 11 May 2026 14:56:22 +0000 en-US hourly 1 DVR TIG Week: The Graphing Mistakes I Made Early On—and What Finally Helped Me Fix Them by Najat Elgeberi https://aea365.org/blog/dvr-tig-week-the-graphing-mistakes-i-made-early-on-and-what-finally-helped-me-fix-them-by-najat-elgeberi/ https://aea365.org/blog/dvr-tig-week-the-graphing-mistakes-i-made-early-on-and-what-finally-helped-me-fix-them-by-najat-elgeberi/#respond Fri, 15 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33025 Hi Everyone, my name is Najat Elgeberi, and I work as an evaluation specialist at the University of Nevada, Reno Extension. In this post, I highlight several mistakes evaluators should avoid when using graphs. When I first began analyzing data and turning findings into visuals, I assumed my job was to make charts look impressive. I used bright colors, crowded legends, bold borders, and whatever chart style the software offered by default. At the time, I thought that if a graph looked polished, it would also look professional. Over time, I learned almost the opposite: the more decoration I added, the harder I made the graph for people to read. A good graph is not the one with the most design features. It is the one that helps readers understand data quickly, accurately, and confidently.

Lessons Learned

Color should guide attention, not create work for the reader

One of my earliest mistakes was using too many colors. I assigned a different color to every bar or category because it seemed like a way to add clarity. In reality, I was adding work for the audience. Readers had to keep moving between the graph and the legend, trying to remember what each color meant. Eventually, I realized that color should be used carefully and with purpose. Now I try to use a restrained palette, keep most elements quiet, and highlight only the one or two points that deserve attention. That small change made my graphs feel calmer, but more importantly, it made the message easier to find.

A before-and-after illustration of one of my earliest habits: using many colors, heavy gridlines, and crowded legends. The revised version uses a restrained palette and highlights only the most important result.
Figure 1. A before-and-after illustration of one of my earliest habits: using many colors, heavy gridlines, and crowded legends. The revised version uses a restrained palette and highlights only the most important result.
Design charts for comparison rather than decoration

Another mistake was confusing visual excitement with effective communication. I sometimes used decorative charts, including 3D bars, heavy outlines, dark backgrounds, and other embellishments that made the display feel dramatic. The problem was that these choices competed with the data. They made it harder to compare values and easier to become distracted by the design. Learning the basic principles of data visualization helped me understand that clean 2D graphs, light gridlines, and clear labels are not boring. They respect the reader’s time. Once I stopped trying to make charts look flashy, I was finally able to make them useful.

A before-and-after illustration showing how decorative elements can interfere with comparison. The improved version removes unnecessary visual noise and lets the data do the work.
Figure 2. A before-and-after illustration showing how decorative elements can interfere with comparison. The improved version removes unnecessary visual noise and lets the data do the work.
A graph is not clear unless all readers can follow it

A third lesson was realizing that a graph is not successful if only some people can read it well. Early in my work, I relied too much on red and green or on color alone to distinguish categories. Later, I learned how difficult that can be for readers with color-vision deficiency. I also learned that a graph should still make sense when colors are muted, printed in grayscale, or viewed in less-than-ideal conditions. Now I use color-blind-safe palettes, direct labels, line styles, and other cues that do not depend on hue alone. This improved both accessibility and clarity.

A before-and-after illustration of a common accessibility problem. The revised version uses a color-blind-safe palette, direct labels, and multiple visual cues so that the chart remains understandable for more readers.
Figure 3. A before-and-after illustration of a common accessibility problem. The revised version uses a color-blind-safe palette, direct labels, and multiple visual cues so that the chart remains understandable for more readers.

Looking back, I do not think these mistakes happened because I did not care about quality. I think they happened because many of us learn software before we learn design principles. We discover how to create charts long before we understand how people actually read them. My work improved when I started asking different questions: What do I want the audience to notice first? What comparison matters most? Would this still work for someone reading quickly, printing in black and white, or struggling to distinguish color?

My biggest lesson is that data visualization is not about decoration. It is about judgment. It is about choosing forms, colors, labels, and emphasis in ways that support understanding. I still revise graphs often, but now revision feels purposeful. I am not adding more. I am removing what gets in the way. In my experience, that is when a graph begins to do what it is supposed to do.

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/dvr-tig-week-the-graphing-mistakes-i-made-early-on-and-what-finally-helped-me-fix-them-by-najat-elgeberi/feed/ 0 DVR TIG Week: Why Color Choices Matter More Than We Think in Data Visualization by Najat Elgeberi https://aea365.org/blog/dvr-tig-week-why-color-choices-matter-more-than-we-think-in-data-visualization-by-najat-elgeberi/ https://aea365.org/blog/dvr-tig-week-why-color-choices-matter-more-than-we-think-in-data-visualization-by-najat-elgeberi/#respond Thu, 14 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33026 Hi Everyone, my name is Najat Elgeberi, Ph.D., and I work as an evaluation specialist and assistant professor for program evaluation at the University of Nevada Reno, Extension.

When evaluators create graphs, we often focus on the “big” decisions first: whether to use a bar chart, line chart, scatterplot, or dashboard. One of the most powerful design decisions is sometimes an afterthought: color. Color can either clarify or distort a visual message. Thoughtful color choices help readers identify patterns, separate groups, and understand magnitude. Poor color choices, by contrast, can make a graph harder to read, direct attention to the wrong places, or exclude readers with color-vision deficiency.

Hot Tips

Color should always have a job. If every bar, line, or point is assigned a different hue simply because the software made that easy, the graph may become more decorative than informative. Claus Wilke argues that color should make a figure easier to read, not create a visual puzzle. This is especially important when we display categorical groups. Once a graph uses too many colors, readers must constantly move back and forth between the data and the legend, trying to decode which color belongs to which category. That effort increases cognitive load and reduces comprehension. In evaluation reporting, that can mean stakeholders spend more time decoding the display than understanding the finding.

An illustrative comparison showing how excessive, saturated color can turn a graph into a decoding exercise, while a limited and intentional palette helps readers find the main point more quickly.
Figure 1. An illustrative comparison showing how excessive, saturated color can turn a graph into a decoding exercise, while a limited and intentional palette helps readers find the main point more quickly.

Color also shapes how people interpret order and magnitude. This matters most in heat maps, choropleth maps, and other displays that encode numeric values with a gradient. Not all gradients are equally interpretable. Research on color integrity in visualization emphasizes the importance of perceptually uniform palettes, in which neighboring colors change at a visually even rate. When color gradients are uneven, viewers may perceive abrupt differences where none exist or miss meaningful variation that should stand out. A related problem appears in the familiar rainbow palette. Because its lightness changes non-monotonically, the rainbow scale can highlight arbitrary parts of the data and obscure the true ordering of values. In other words, the palette can introduce a story the data did not intend to tell.

An illustrative comparison showing how rainbow palettes can create false emphasis, while a perceptually uniform light-to-dark scale better supports accurate reading of ordered values.
Figure 2. An illustrative comparison showing how rainbow palettes can create false emphasis, while a perceptually uniform light-to-dark scale better supports accurate reading of ordered values.

The issue is not only accuracy, but also accessibility. A graph is not successful if only some readers can interpret it correctly. A substantial share of the population has some form of color-vision deficiency, and red-green distinctions are a common challenge. If a graph relies only on red versus green to separate categories or show good versus bad performance, some readers may not be able to distinguish those signals reliably. Crameri and Hason recommend using color-blind friendly palettes and checking figures in grayscale to ensure that categories or values still differ in relative lightness. If the graph becomes unreadable, it is a warning sign that the design is doing too much work through hue alone. That is a practice evaluators can adopt immediately.

An illustrative accessibility sequence showing that a graph designed only around red and green can fail for some readers, while a color-blind-safe palette combined with direct labels and contrast improves comprehension.
Figure 3. An illustrative accessibility sequence showing that a graph designed only around red and green can fail for some readers, while a color-blind-safe palette combined with direct labels and contrast improves comprehension.

For evaluation, the takeaway is straightforward: color should support meaning, not compete with it. Use a small number of colors for categorical comparisons. Reserve bright, saturated colors for deliberate emphasis rather than for every element. Choose ordered palettes with steady lightness changes when representing magnitude. When possible, reinforce color with direct labels, patterns, or position so that the graph remains interpretable even without perfect color discrimination. These choices are not merely aesthetic. They shape who can understand our findings, how quickly they can interpret them, and whether the message they take away is the one the data actually support.

The next time you revise a chart, ask the simple question: What is this color helping my reader see? If the answer is not clear, the color choice probably needs another look.

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/dvr-tig-week-why-color-choices-matter-more-than-we-think-in-data-visualization-by-najat-elgeberi/feed/ 0 DVR TIG Week: AI Can’t Write Your Report, But It Can Make It Better by Arthur Hernandez https://aea365.org/blog/dvr-tig-week-ai-cant-write-your-report-but-it-can-make-it-better-by-arthur-hernandez/ https://aea365.org/blog/dvr-tig-week-ai-cant-write-your-report-but-it-can-make-it-better-by-arthur-hernandez/#respond Wed, 13 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33027 Hello. I’m Arthur Hernandez, and I’ll admit something: a year ago, I was skeptical that AI had much to offer seasoned evaluators when it came to reporting. I’ve been writing evaluation reports for a long time, and I wasn’t convinced a machine could meaningfully improve a process I’d spent decades refining. I was partially right, and usefully wrong.

Hot Tip: Use AI to Pressure-Test Your Findings Narrative

The most valuable AI use I’ve discovered isn’t generating visualizations. It’s stress-testing how I communicate findings. I’ll draft a key findings section and paste it into an AI tool with a prompt like, “Read this as a school board member with no evaluation background. What’s confusing? What questions would you have?” The feedback isn’t perfect, but it consistently catches jargon I’ve gone blind to, unclear transitions, and assumptions about what my reader already knows. It’s like having a fresh pair of eyes at eleven o’clock at night when your report is due in the morning. Every evaluator knows that feeling.

Cool Trick: Let AI Help You Build Layered Reports

Here’s a practical reality of modern evaluation: we need to communicate the same findings to multiple audiences. A technical appendix for the program team. An executive summary for leadership. An accessible brief for community stakeholders. AI can help you adapt a core narrative across those layers. Start with your most comprehensive version, then ask the tool to draft a plain-language summary or identify which findings to prioritize for a one-pager. You’ll absolutely need to review and revise; but it cuts significant time from what is essentially translation work, and translation work is where many of us lose steam.

Lesson Learned: Transparency About AI Use Is Non-Negotiable

I’ll end with something I feel strongly about. As evaluators, our credibility rests on transparency. If AI helped shape your visualizations, draft your narrative, or surface patterns in your data, say so. It doesn’t diminish your work; instead, it demonstrates methodological honesty. The AEA Guiding Principles for Evaluators are clear that integrity is foundational to our practice. I’ve started adding a brief methods note to my reports whenever AI tools played a meaningful role. One sentence. That’s all it takes, and it matters more than we might think as our field sets norms around these tools.

AI won’t replace the evaluator’s voice, judgment, or relationships. But used with care and candor, it can help us produce clearer, more accessible, and more timely work. After forty-some years in this field, I’ll take that.

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/dvr-tig-week-ai-cant-write-your-report-but-it-can-make-it-better-by-arthur-hernandez/feed/ 0 DVR TIG Week: In the Age of AI, Standards Still Come First by Arthur Hernandez https://aea365.org/blog/dvr-tig-week-in-the-age-of-ai-standards-still-come-first-by-arthur-hernandez/ https://aea365.org/blog/dvr-tig-week-in-the-age-of-ai-standards-still-come-first-by-arthur-hernandez/#respond Tue, 12 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33028 Hello. I’m Arthur Hernandez, Vice Chair of the Joint Committee on Standards for Educational Evaluation. During my 40+ years of evaluation research and practice, I’ve watched our field cycle through hand-tabulated data, mainframe printouts, desktop publishing, infographics, and interactive dashboards. Each arrival was heralded as transformative. And to a degree, each one also introduced new ways to get things wrong. Now artificial intelligence is here, and I confess I find the pattern familiar.

Lesson Learned: Every New Tool Arrives Promising Clarity and Risks Delivering Confusion

I remember when desktop charting software first became widely available in the late 1980s. Suddenly everyone could produce colorful graphs, and suddenly evaluation reports were full of misleading visuals, such as truncated axes, three-dimensional pie charts that distorted proportions, decorative elements that obscured findings. The tool made production easy. It did nothing to ensure quality. AI presents the same bargain at a much larger scale. I recently watched a demonstration where an AI tool generated a complete data dashboard from a spreadsheet in under a minute. It was impressive. It was also wrong in ways that would have violated several of the Program Evaluation Standards, particularly around accuracy and transparency. The speed was real. The rigor was absent.

Hot Tip: Use the Program Evaluation Standards as Your AI Review Checklist

Here is my practical suggestion, and it is a simple one. Before you share any AI-assisted visualization or report, run it through the lens of the Program Evaluation Standards. Ask yourself: Is this accurate? Is it transparent about its methods and limitations? Does it serve the information needs of its intended users? Is it fair in how it represents the people and programs being evaluated? These questions predate AI by decades, and they remain the right questions. Because good tools alone do not automatically produce good evaluations.

Rad Resource: Pair AI with Culturally Responsive Evaluation Frameworks

Something that warrants particular attention: AI tools carry embedded assumptions about how data should look, who the audience is, and what counts as clear communication. These assumptions tend to reflect dominant cultural norms. I would encourage any evaluator using AI for visualization to ground their work in culturally responsive evaluation frameworks. Stafford Hood, Rodney Hopson, and Henry Frierson have done essential work reminding our field that how we represent data is never culturally neutral. The Urban Institute’s Do No Harm Guide offers practical guidance for applying that awareness to visualization specifically. No AI tool I have encountered asks whose perspective is being centered in a chart. That question remains ours to ask.

I have been at this long enough to know that the tools will keep changing. What endures is our obligation to get it right, not just to get it done.

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/dvr-tig-week-in-the-age-of-ai-standards-still-come-first-by-arthur-hernandez/feed/ 0 DVR TIG Week: Beyond the Default Chart: How AI Can Help Evaluators Tell Better Data Stories by Chunling Niu https://aea365.org/blog/dvr-tig-week-beyond-the-default-chart-how-ai-can-help-evaluators-tell-better-data-stories-by-chunling-niu/ https://aea365.org/blog/dvr-tig-week-beyond-the-default-chart-how-ai-can-help-evaluators-tell-better-data-stories-by-chunling-niu/#respond Mon, 11 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33029 Hello! I’m Chunling Niu, and I chair AEA’s Data Visualization and Reporting TIG. Let’s talk about something I see constantly in evaluation reports: default charts. The bar graphs and pie charts that Excel auto-generates with zero thought about whether they’re actually the clearest way to make your point. Nowadays AI tools offer us new ways to break out of that rut.

Rad Resource: AI-Powered Tools for Smarter Chart Selection

Picking the right chart type is one of the most consequential, and most under-trained skills in evaluation reporting. Tools like Claude and ChatGPT make surprisingly good sounding boards. Describe your data structure, your key message, and your audience, then ask for visualization recommendations with rationale. I did this recently for a mixed-methods evaluation and got a suggestion for a connected dot plot I hadn’t considered. It ended up being far more effective than the grouped bar chart I’d been planning. Pair this with the Financial Times Visual Vocabulary, which categorizes chart types by the relationship you’re trying to show. It’s a fantastic quick reference.

Hot Tip: Draft Your Data Story Before You Design Anything

Before you touch your visualization software, try asking an AI tool to help you articulate the story your data tells. Paste in a summary table and prompt: “What are the three most important patterns here for a funder audience?” This forces you to nail down your message before you start designing, which almost always produces stronger visuals. Think of it as a thought partner for sharpening your focus, instead of a replacement for your interpretive lens.

Lesson Learned: Polished Doesn’t Mean Accurate

A word of caution. AI tools can now generate complete visualizations from data, and the outputs are getting sleeker. But “sleek” is not the same as “right.” I’ve tested several and found misleading axis scales, color choices that fail for color blind readers, and labels that obscure rather than clarify. Stephanie Evergreen’s work on effective data visualization remains essential foundation reading. After all, AI tools should build on those principles, not leapfrog them.

Bottom line: AI is a powerful drafting partner, but your evaluator expertise is what turns a chart into a communication tool!

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/dvr-tig-week-beyond-the-default-chart-how-ai-can-help-evaluators-tell-better-data-stories-by-chunling-niu/feed/ 0 DVR TIG Week: AI as Your Data Visualization Co-Pilot: Getting Started Without Getting Lost by Chunling Niu https://aea365.org/blog/ai-as-your-data-visualization-co-pilot-getting-started-without-getting-lost-by-chunling-niu/ https://aea365.org/blog/ai-as-your-data-visualization-co-pilot-getting-started-without-getting-lost-by-chunling-niu/#respond Sun, 10 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33016 Hey there! I’m Chunling Niu, chair of the Data Visualization and Reporting TIG here at AEA. If you’ve been AI-curious but aren’t sure where to start when it comes to visualizing and reporting your data, this post is for you.

I talk with evaluators every week who feel caught between excitement and overwhelm when it comes to AI. Here’s what I keep telling them: you don’t need to become a data scientist. You just need to be willing to experiment, and to stay critical while you do it.

Hot Tip: Start with What You Already Do, Then Let AI Help Stretch It

The easiest entry point? Use AI to speed up tasks you’re already doing. If you build charts in Excel or Google Sheets, try describing what you need in plain language to a tool like ChatGPT, Claude, or Microsoft Copilot. Something like, “I have survey data with five Likert-scale items across three stakeholder groups. What’s the best chart type and why?” You’ll get a solid starting point you can refine instead of staring at a blank screen.

Cool Trick: Use AI to Generate Alt Text for Your Visualizations

This one’s a gamechanger for accessibility. After creating a chart, paste a screenshot into an AI tool and ask it to draft descriptive alt text. It saves real time and helps us meet accessibility standards that we all know matter but often let slide under deadline pressure. The Web Accessibility Initiative has great guidance on writing effective image descriptions for data visualizations, bookmark it.

Lesson Learned: AI Doesn’t Replace Your Evaluator Judgment

Let me be real: AI can suggest chart types, color palettes, and even narrative summaries of your data; but it has zero understanding of your stakeholders, your context, or the political dynamics around your findings. I’ve seen AI recommend visualizations that would completely miss the mark for a community-based audience. Always gut-check: does this serve my audience, or does it just look slick? Your expertise in knowing what a finding means and who needs to hear it is irreplaceable.

Our Data Visualization and Reporting TIG is here to help you navigate all of this. Jump into our conversations, share what you’re trying, and let’s learn together.

The American Evaluation Association is hosting Data Visualization and Reporting (DVR) Week with our colleagues in the DVR Topical Interest Group. The contributions all this week to AEA365 come from DVR TIG members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/ai-as-your-data-visualization-co-pilot-getting-started-without-getting-lost-by-chunling-niu/feed/ 0 Washington Evaluators Week: Making Evaluation Findings Relevant: Reflections from an International Evaluator by Tristi Nichols https://aea365.org/blog/washington-evaluators-week-making-evaluation-findings-relevant-reflections-from-an-international-evaluator-by-tristi-nichols/ https://aea365.org/blog/washington-evaluators-week-making-evaluation-findings-relevant-reflections-from-an-international-evaluator-by-tristi-nichols/#respond Sat, 09 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33058 How do you ensure your evaluation data is used? From project design, to stakeholder engagement, to report writing and data sharing, evaluators have many opportunities to help ensure the data we collect are actually used for decision-making. In this series of blog posts, Washington Evaluators draws on our experiences in Washington, DC and beyond to share best practices for making evaluations useful.

Asalamalekum!!  My name is Tristi Nichols, and I am a principal at Manitou, Inc, and I currently serve as the Secretary for Washington Evaluators.

Evaluation only matters if someone appreciates it—and then uses it.  Making evaluation useful is less about methodology and more about relationships. It is about meeting people where they are, asking clarifying questions early, staying on message, and focusing on clients’ needs for future decision-making. If we do that well, the findings will not only be embraced but showcased.

Throughout my career in international evaluation with the United Nations and NGOs, I have learned that usefulness does not happen at the end of a project. It starts at the beginning and continues throughout every phase of the evaluation process.  Some of the organizations that I have worked with are the United Nations Office of Internal Oversight Services (OIOS), UNFPA Independent Evaluation Office, and CARE Angola, Care Zambia, and Care Somalia (part of Care Canada).

Hot Top: Start with Decisions in Mind

Beginning with design, before drafting an Inception Report, a single survey question, or an interview guide, I ask a simple question: *What decisions do you need to make now?* Not six months from now. Not “in general.” I mean specific decisions tied to real timelines.

In global contexts, it is not just the client’s needs that matter, but also those of governments and communities. For instance, an official from a Ministry of Women and Children Affairs may want to focus on general access to maternal health services, while a donor or NGO partner may need to address the Leaving No One Behind (LNOB) agenda. These priorities are not in conflict, but trying to address all concerns equally can contribute to response fatigue. Finding the right balance requires nurturing relationships and identifying where compromise is possible.

Hot Tip: Don’t Forget to Laugh

A little humor goes a long way. Keeping things light—so that we can laugh together about how long surveys can be—helps build the kind of rapport that leads to honest conversations and, ultimately, a valued dataset.

Stakeholder engagement in multicultural settings also requires gathering honest feedback in more than one way. This might mean holding smaller group discussions instead of large forums or creating anonymous ways for people to share candid feedback.

Hot Tip: Ask Questions and Listen

My doctoral advisor, Dr. Jennifer Greene, once told me that “evaluation starts with silence.” That means listening more than talking. People are far more likely to use findings that they helped to develop and shape.

When it comes to reporting, clarity beats fancy wording every time. Plain language is not a downgrade; it is a strategy. If a program manager has to reread a sentence three times or pause to interpret its meaning, I have already lost them. Similarly, I focus on answering the “so what?” question as directly as possible. What does this mean for your program next month or next quarter? Why does it matter in this context?

I also ask clients—explicitly—how they want findings presented for different stakeholder groups. Do they need a short brief for senior leadership? Slides for a community meeting? A detailed annex for technical colleagues? There is no single “right” product. There is only what is useful in that context.

Hot Tip: Share Your Work

Finally, data sharing is critical. In international work, access can be uneven, and stakeholders’ attention bandwidth varies widely. A short in-person briefing with clear talking points may work for one group, while an attractive printed summary may be more effective for another.

Rad Resources

The American Evaluation Association is hosting Washington Evaluators Week with our colleagues in the Washington Evaluators local affiliate group. The contributions all this week to AEA365 come from Washington Evaluators members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/washington-evaluators-week-making-evaluation-findings-relevant-reflections-from-an-international-evaluator-by-tristi-nichols/feed/ 0 Washington Evaluators Week: “Tricks” for Stakeholder Engagement from Academic Assessment by Ashima Singh https://aea365.org/blog/washington-evaluators-week-tricks-for-stakeholder-engagement-from-academic-assessment-by-ashima-singh/ https://aea365.org/blog/washington-evaluators-week-tricks-for-stakeholder-engagement-from-academic-assessment-by-ashima-singh/#respond Fri, 08 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33059 How do you ensure your evaluation data is used? From project design, to stakeholder engagement, to report writing and data sharing, evaluators have many opportunities to help ensure the data we collect are actually used for decision-making. In this series of blog posts, Washington Evaluators draws on our experiences in Washington, DC and beyond to share best practices for making evaluations useful.

I am Ashima Singh, a consultant who began her career in evaluation, went to higher education (HE) assessment and accreditation (both specialized evaluations), and then hung up her own shingle at Ashima Singh Consulting (ASC). I learned key lessons in HE that have informed my work at ASC.

Accreditation is a high-stakes quality assurance evaluation necessary for HE institutional sustainability and fiscal viability. Assessment – a significant part of demonstrating compliance with accreditation standards – is faculty, staff, and administrators routinely and systematically reflecting on the impact of their work on student learning and unit and institutional goals and making adjustments as they go.

HE personnel often perceive assessment as a threat to their safety and academic freedom. Increasing public and political scrutiny of HE adds tension to a fraught environment where successful accreditation is necessary and yet crucial parts of it fundamentally alienate key stakeholders. Initially, selling a used car seemed easier than getting HE personnel to embrace assessment. They needed a functional relationship with assessment, one that restored their agency and left room for creativity. I learned facilitating the shift was easiest when using a heart-first strategy.

Cool Trick: Speak a Shared Language

If English was the only shared language between me and a friend, I would not speak to them in Hindi. Similarly, using evaluation-specific language with stakeholders unfamiliar with it placed hurdles between them, me, and their engagement with assessment. So, instead of relying on them to understand me, I worked to be understood. I used stories, metaphors, and similes to remove barriers.

An education program, for example, had set a 70% pass rate as a benchmark for success. So, I turned that into a visual of a 100 people walking into a hospital and only 70 walking out alive. That image quickly revealed the benchmark would neither be informative nor give them any bragging rights. They giggled and eagerly discussed what benchmarks made sense instead. Humor worked.

Cool Trick: Start at the (Far) End

Unpleasant tasks rightly call for justification. So, I walked stakeholders through “why” questions until they saw that assessment extended beyond measuring progress towards goals. Instead, it was a strategic step toward program sustainability, funding, development, meeting stakeholder needs, determining resource needs, and so on. People began to see assessment as a useful tool that met their needs. As their apprehension ebbed, curiosity flowed in, and their shoulders relaxed. What seemed threatening started to look like “what if” possibilities. One group realized that their assessment data filled a crucial gap in a grant application. Thus, relaxed shoulders became my desired outcome for all meetings.

Cool Trick: Track Outcomes of Data-Motivated Decisions

Stakeholders may not know that assessment and evaluation tell their program’s story across time. I invited people to think about their family album as a living history of personal growth and time with loved ones. That image was effective in helping them see that evaluation and data are snapshots of their work. I designed a matrix as a resource to help stakeholders document data-motivated decisions and their outcomes – their program history. This returned agency to the people responsible for the program.

Research shows that emotional (heart) thinking has rapid automaticity which, when paired with analytic (head) thinking, leads to better decision-making. The cool tricks I shared are not tricky at all but are still powerful in helping stakeholders build a functional relationship with evaluation. The easiest way to people’s head is through their heart.

Rad Resources

The American Evaluation Association is hosting Washington Evaluators Week with our colleagues in the Washington Evaluators local affiliate group. The contributions all this week to AEA365 come from Washington Evaluators members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/washington-evaluators-week-tricks-for-stakeholder-engagement-from-academic-assessment-by-ashima-singh/feed/ 0 Washington Evaluators Week: Diaper Banks, Crisis Response, and Organizational Decision Making by Malasia Williams https://aea365.org/blog/washington-evaluators-week-diaper-banks-crisis-response-and-organizational-decision-making-by-malasia-williams/ https://aea365.org/blog/washington-evaluators-week-diaper-banks-crisis-response-and-organizational-decision-making-by-malasia-williams/#comments Thu, 07 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33060 How do you ensure your evaluation data is used? From project design, to stakeholder engagement, to report writing and data sharing, evaluators have many opportunities to help ensure the data we collect are actually used for decision-making. In this series of blog posts, Washington Evaluators draws on our experiences in Washington, DC and beyond to share best practices for making evaluations useful.

My name is Malasia Williams, I am the Evaluation Coordinator at the National Diaper Bank Network (NDBN). At NDBN, our goal is to make sure every child, family, and individual in the U.S. has access to the material basic needs they require to thrive. We maintain a member network of more than 300 basic needs banks that work in local communities throughout the United States.

During the shutdown of the federal government in October 2025, NDBN evaluated how our members, each independent 501c3 organizations, and the families they serve were coping with the economic shocks resulting from the shutdown. To assess the impacts of the government shutdown, we developed a survey for staff at member organizations. We administered two surveys, one in October 2025 (N=56) and one in November (N=74) 2025. In each survey, we asked questions about first-time visitors to basic needs banks and funding needs to assess how they changed throughout the shutdown.

These surveys showed us that, as a network, our members were able to quickly and effectively adapt their distribution and organizational structure to better reach those affected by the shutdown. We saw NDBN members expand their operations to meet demand, create special distribution events, secure crisis funding, and simplify the enrollment process to quickly serve families in need. Our leadership team used the evaluation findings to better understand how we, as a network, can further mobilize assistance for members during times of crisis and when economic shocks are anticipated in certain communities.

Lessons Learned: Use Evaluation Findings to Look Ahead

We know that when economic shocks take place, diaper banks are better able to support their community when there are strong partnerships in place before the economic shock or at the beginning of a government shutdown. Since many of our members distribute products through partner agencies, the survey allowed us to understand with whom our diaper banks partnered. These data provide us with the ability to replicate and scale these partnership models to better direct resources and support to diaper banks so we are prepared for future economic shocks.

Lessons Learned: Use Evaluation Findings to Pivot

Based on how diaper banks reported the multiple ways they mobilized to meet the increased demand, we adjusted some of our strategic planning to better assist members during these critical times. Specifically, we enhanced technical assistance and mobilization of resources to those areas most impacted by the government shutdown

Lesson Learned: Visualize Evaluation Findings for Easy Use

We used the survey data to map members impacted by the government shutdown, making it clear which geographies have a large percentage of federal employees who may be impacted by government shutdowns. This facilitated efficient deployment of resources to members in those areas and enables us to do the same during any future government shutdowns.

Now knowing how diaper banks respond in times of crisis, we can better plan, execute, and distribute resources to reach families in need.

Rad Resources

Want to learn more about NDBN’s member banks and diaper insecurity, check out these rad resources:

The American Evaluation Association is hosting Washington Evaluators Week with our colleagues in the Washington Evaluators local affiliate group. The contributions all this week to AEA365 come from Washington Evaluators members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

]]> https://aea365.org/blog/washington-evaluators-week-diaper-banks-crisis-response-and-organizational-decision-making-by-malasia-williams/feed/ 1 Washington Evaluators Week: When the Data Exists But Don’t Land: Lessons on Making Evaluation Useful by Fatima Léonora Baldé https://aea365.org/blog/washington-evaluators-week-when-the-data-exists-but-dont-land-lessons-on-making-evaluation-useful-by-fatima-leonora-balde/ https://aea365.org/blog/washington-evaluators-week-when-the-data-exists-but-dont-land-lessons-on-making-evaluation-useful-by-fatima-leonora-balde/#respond Wed, 06 May 2026 06:00:00 +0000 https://aea365.org/blog/?p=33061 How do you ensure your evaluation data is used? From project design, to stakeholder engagement, to report writing and data sharing, evaluators have many opportunities to help ensure the data we collect are actually used for decision-making. In this series of blog posts, Washington Evaluators draws on our experiences in Washington, DC and beyond to share best practices for making evaluations useful.

Hi! I’m Fatima Léonora Baldé, an M&E and communications consultant based in Dakar, Senegal. I work with a youth development program under the Mastercard Foundation and with a zero-waste civil society organization tracking its communications impact. My work has pushed me to sit with a question that doesn’t get asked often enough: useful to whom, and in what room?

I did not set out to write a thesis about evaluation use. I set out to understand why Senegal had the architecture of a functional national evaluation system, dedicated units, formal mandates, published reports, but still struggled to translate that infrastructure into decisions that changed things on the ground. What I found reshaped how I think about the evaluator’s job.

The gap was not a data problem. The data existed. It was a translation problem: between what evaluations produced and what decision-makers needed, between the timing of reports and the timing of decisions, and between who owned the findings and who was expected to act on them. Whether data gets used depends less on the dashboard and more on what happened long before you walked in with it.

Here are the lessons that have stayed with me.

Lesson Learned: Start with the decision, not the indicator.

Early in a project I was monitoring for a Dakar-based organization, we spent weeks building a social media performance framework before anyone had asked the program team what decisions they actually needed to make. When we finally did, half our indicators were irrelevant to them. Now I open every M&E design conversation with one question: what would you do differently if this data told you X instead of Y? If nobody can answer that, we need to redesign before collecting a single data point.

Lesson Learned: Utilization is designed in, not added on.

The evaluations most likely to be used were those where intended users were identified before data collection began, not after the report was written. Stakeholder mapping is not a courtesy step. It is the architecture of use. Build the conversations into the process, not onto the end of it.

Lesson Learned: Context shapes what “useful” means.

Dense technical reports written for international donors rarely traveled downstream to the national program staff or community actors who needed them. One of the core insights I took from the Made in Africa Evaluation framework is that accountability must flow in both directions. When it only moves upward toward funders, findings serve compliance. When it also moves horizontally toward communities and local actors, they can serve learning.

Rad Resources

  • The Made in Africa Evaluation framework, developed through AfrEA, offers a values-based lens for evaluation practice rooted in African contexts and is generative for any evaluator, not just those working on the continent.
  • CLEAR-FA at CESAG is building regional M&E capacity across francophone West Africa. The African Evaluation Journal publishes practitioner-facing research from across the continent.

Evaluation data does not use itself. As evaluators, we are responsible not just for the rigor of our methods but for the journey our findings take after we hit send. That responsibility begins at the design stage, and it never really ends.

The American Evaluation Association is hosting Washington Evaluators Week with our colleagues in the Washington Evaluators local affiliate group. The contributions all this week to AEA365 come from Washington Evaluators members. Do you have questions, concerns, kudos, or content to extend this AEA365 contribution? Please add them in the comments section for this post on the AEA365 webpage so that we may enrich our community of practice. Would you like to submit an AEA365 Tip? Please send a note of interest to AEA365@eval.org. AEA365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators. The views and opinions expressed on the AEA365 blog are solely those of the original authors and other contributors. These views and opinions do not necessarily represent those of the American Evaluation Association, and/or any/all contributors to this site.

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