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Archive for the ‘Data Visualization’ Category

Uninspired by Bars? Try Dot Plots

Friday, March 17th, 2017

Thanks to Jessi Van Der Volgen and Molly Knapp at the NNLM Training Office for allowing us to feature their assessment project and for providing the images in this post. 

Are you tired of bars?

I don’t mean the kind of bars where you celebrate and socialize. I mean the kind used in data visualization.  My evidence-free theory is that people still succumb to using the justifiably maligned pie chart simply because we cannot face one more bar graph.

Take heart, readers. Today, I’m here to tell you a story about some magic data that fell on the NEO’s doorstep and broke us free of our bar chart rut.

It all began with a project by our NNLM Training Office (NTO) colleagues, the intrepid leaders of NNLM’s instructional design and delivery. They do it all. They teach. They administratively support the regions’ training efforts. They initiate opportunities and resources to up-level instructional effectiveness throughout the network. One of their recent initiatives was a national needs assessment of NNLM training participants. That was the source of the fabulous data I write about today.

For context, I should mention that training is one of NNLM’s key strategies for reaching the furthest corners of our country to raise awareness, accessibility and use of NLM health information resources. NNLM offers classes to all types of direct users, (e.g., health professionals; community-based organization staffs) but we value the efficiency of our “train-the-trainer” programs. In these classes, librarians and others learn how to use NLM resources so they, in turn, can teach their users. The national needs assessment was geared primarily toward understanding how to best serve “train-the-trainer” participants, who often takes multiple classes to enhance their skills.

For the NTO’s needs assessment, one area of inquiry involved an inventory of learners’ need for training in 30 topic areas. The NTO wanted to assess participants’ desired level and their current level of proficiency in each topic.  That meant 60 questions. That was one heck-of-a-long survey. We wished them luck.

The NTO team was undaunted!  They did some research and found a desirable format for presenting this set of questions (see upper left). The format had a nice minimalist design. The sliders were more fun for participants than radio buttons. Also, NTO designed the online questionnaire so that only a handful of question-pairs appeared on the screen at one time.  The approach worked, because NTO received responses from 559 respondents, and 472 completed the whole questionnaire.

Dot plots for four skill topic areas. Conducting literature searches (4=Current; 5=Desired) Understanding and searching for evidence-based research ( Current-3; Desired=5) Develop/teach classes (Current-3; Desired=5; Create videos/web tutorials Current-2; Desired=4)

The NEO, in turn, consulted the writings of one of our favorite dataviz oracles, Stephanie Evergreen. And she did not disappoint.  We found the ideal solution: dot plots!  Evergreen’s easy-to-follow instructions from this blog post allowed us to create dot plots in Excel, using a few creative hacks. This approach allowed us to thematically cluster results from numerous related questions into one chart. We were able to present data for 60 questions in a total of seven charts.

I would like to point out a couple of design choices I made:

  • I used different shapes and colors to visually distinguish between “current proficiency” and “desired proficiency.” Navy blue for current proficiency was inspired from NNLM’s logo. I used a complimentary green for the desired proficiency because green means “go.”
  • Evergreen prefers to place labels (e.g., “conducting literature searches”) close to the actual dots. That works well if your labels consist of one or two words. We found that our labels had to be longer to make sense. Setting them flush-left made them more readable.
  • I suggested plotting medians rather than means because many of the data distributions were skewed. You can use means, but probably should round to whole numbers so you don’t distract from the gaps.

Dot plots are quite versatile. We used the format to highlight gaps in proficiency, but other evaluators have demonstrated that dot plots work well for visualizing change over time and cross-group comparisons.

Dot plots are not as easy to create as the default Excel bar chart, but they are interesting.  So give up bars for a while.  Try plotting!





My Favorite Things 2016 (Spoiler Alert: Includes Cats)

Wednesday, December 21st, 2016

Little figurine of Santa standing in snow, holding gifts

During gift-giving season every year, Oprah publishes a list of her favorite things. Well, move over, Oprah, because I also have a list. This is my bag of holiday gifts for our NEO Shop Talk readers.

Art Exhibits

There are two websites with galleries of data visualizations that are really fun to visit. The first,  Information is Beautiful , has wonderful examples of data visualizations, many of which are interactive. My favorites from this site are Who Old Are You?   (put in your birth date to start it) and Common MythConceptions. The other is Tableau Public, Tableau Software Company’s “public commons” for their users to share their work.  My picks are the Endangered Species Safari  and the data visualization of the Simpsons Vizapedia.  And, in case  you’re wondering what happened to your favorite Crayola crayon colors, you can find out here.


Nancy Duarte’s The Secret Structure of Great Talks is my favorite TEDtalk. Duarte describes the simple messaging structure underlying inspirational speeches. Once you grasp this structure, you will know how to present evaluations findings to advocate for stakeholder support. I love the information in this talk, but that’s not why I listen to it over and over again.  It’s because Duarte says “you have the power to change the world” and, by the end of the talk, I believe her.

Dot plot for a fictional workshop data, titled Participant Self Assessment of their Holiday Skills before and after our holiday survival workshop. Pre/post self-report ratings for four items: Baking without a sugar overdose (pre=3; post-5); Making small talk at the office party (pre=1; post=3); Getting gifts through airport security (pre=2; post-5); Managing road rage in mall parking lots (pre=2; post-4)

I also am a fan of two videos from the Denver Museum of Natural History, which demonstrate how museum user metrics can be surprisingly entertaining. What Do Jelly Beans Have To Do With The Museum? shows demographics with colorful candy and Audience Insights On Parking at the Museum  talks amusingly about a common challenge of urban life.


If you want to try your hand at creating snappier charts and graphs, you need to spend some time at Stephanie Evergreen’s blog. For example, she gives you step-by-step instructions on making lollipop charts, dot plots , and overlapping bar charts. Stephanie works exclusively in Excel, so there’s no need to purchase or learn new software. You also might want to learn a few new Excel graphing tricks at Ann Emery’s blog.  For instance, she describes how to label the lines in your graphs or adjust bar chart spacing.

Site Seeing

How about a virtual tour to the UK? I still marvel at the innovative Visualizing Mill Road  project. Researchers collected community data, then shared their findings in street art. This is the only project I know of featuring charts in sidewalk chalk. The web site talks about community members’ reactions to the project, which is also pretty fascinating.


I left the best for last. This is a gift for our most sophisticated readers, recommended by none other than Paul Gargani, president of the American Evaluation Association. It is a web site for the true connoisseurs of online evaluation resources.  I present to you the Twitter feed for  Eval Cat.  Even the  NEO Shop Talk cats begrudgingly admire it, although no one has invited them to post.


Pictures of the four NEO Eval Cats










Here’s wishing you an enjoyable holiday.

A Chart Chooser for Qualitative Data!

Friday, November 18th, 2016

Core Values Word Cloud Concept

When people talk about data visualization, they are usually talking about quantitative data. In a previous post, we explained that data visualizations help people perform three primary functions: exploring, making sense of, and communicating data.  How can we report qualitative data in a way that performs those same functions?

We just got some exciting news from the EvergreenData blog that they have developed a Qualitative Chart Chooser. Seriously–it’s a work of art. Actually two works of art because they have two different chart chooser drafts to choose from.

The way it works is this: you think about the story you want to tell with your data, maybe about how something improved over time because of your awesome project. Then using the chart chooser, you look at the “show change over time” category, and then you could select a timeline, before-and-after “change photos,” or a histomap (what’s a histomap?  Take a look at this one).

This chart chooser is a very cool tool. But I wouldn’t wait until it was time to report findings to use it. One thing that we at the NEO suggest is that when you are first planning your project, you should think about the story or stories you want to tell at the end of your project. Maybe when you’re thinking about the story you want to tell, you could look at all these different qualitative charts in the chart chooser.  Which ones would you like to use? Do you want to tell the story of how your program aligns with the goals of your institution (you could try indicator dots)? Or maybe you want to show how the different parts of your project work together as a whole (a dendrogram might work). By looking at these options before you design your evaluation plan, you can be sure that you are gathering the right data from the beginning. Backing up even further in your planning process, if you are having trouble trying to decide what story or stories you want to tell, this Qualitative Chart Chooser can give you ways to think about that.

Here is some more information on qualitative data visualization and storytelling from NEO Shop Talk:

Qualitative Data Visualization, September 26, 2014

More Qualitative Data Visualization Ideas, December 18, 2014

Telling Good Stories About Good Programs, June 29, 2015

DIY Tool for Program Success Stories, July 2, 2015


What’s in a Name? Convey Your Chart’s Meaning with a Great Title

Friday, October 7th, 2016

Some of you may be working on conference posters and paper presentations for Fall conferences.  And some of those will probably include charts to demonstrate data representing a lot of hard work on your part.  In most cases you have minutes to use that chart to get your audience to understand the data.

Stephanie Evergreen has great advice for displaying chart data.  She literally wrote the books on it: Presenting Data Effectively and Effective Data Visualization.  Her recent blog post is about one of the simplest and most powerful changes you can make to effectively present your chart data: “Strong Titles Are The Biggest Bang for Your Buck.

What many of us do is present the data with a generic title, like “Attendance rates.” Then the viewer has to spend time working through the data and you hope that they see what you wanted them to.  What Stephanie Evergreen proposes (backed by persuasive research) is to give your charts a clear title that explains what the data shows. Your poster or paper is almost certainly making a point.  Determine how your chart supports the point of your presentation and state that in the title.  Here are some reasons why:

  • It respects your viewers’ time
  • It forces you to be clear about the point you want your data to make
  • It makes the data more memorable

Stephanie Evergreen’s post has some great examples of how a good title can really improve the impact of the chart.  In addition, here is an example from the NEO webinar Make Your Point: Good Design for Data Visualization.

Looking at this original chart, you might notice that in each activity the follow-up showed an increase over the baseline.  If you, the viewer, didn’t have a lot of time, that might be all you noticed.

Chart with title: Comparison of emergency preparedness activities from baseline to follow-up

With a simple change of title , you can see that the author of this presentation is highlighting the increased number of continuity of services plans.  This is designed to enhance the point of the presentation, and not waste the viewers’ time. Also, note that the title is left justified instead of centered.  Because the title is a full sentence, a left-justified format is easier to read.

Chart with title: The biggest improvement in emergency preparedness from baseline to follow-up was the number of network member organizations reporting that they had or were working on a service continuity plan.

So, while Shakespeare might have been correct when he wrote “What’s in a name? that which we call a rose / By any other name would smell as sweet,” what if the presenter was trying to show the fortitude of Texas antique roses to survive in harsh weather conditions, and the viewer only noticed how sweet the rose smelled?  Maybe the heading “A Rose” sometimes isn’t enough information.








Happy Fourth of July in Numbers!

Friday, July 1st, 2016

4th of July graphic image

Before holidays we sometimes do a post on the value of putting your data in a visually understandable format, perhaps some kind of infographic.

As I write this, some of you may be sitting at your desks pondering how you will celebrate U.S. Independence Day. To help turn your ponderings into a work-related activity, here are some examples of Fourth of July Infographics.  Since some of them have numbers but no dates (for example the number of fireworks purchased in the US “this year”) you might use them as templates for the next holiday-based infographic you create yourself.

If you like History, the History Channel has a fun infographic called 4th of July by the Numbers.  It includes useful information such as:

  • the oldest signer of the Declaration of Independence was Benjamin Franklin at 70,
  • the record for the hot dog eating contest on Coney Island is 68 hotdogs in 10 minutes, and
  • 80% of Americans attend a barbecue, picnic or cookout on the 4th of July

Thinking about the food for your picnic (if you’re one of the 80% having one)?

From the perspective of work (remember work?) here is an infographic from Unmetric on how and why you should create your own infographics for the 4th of July: How to Create Engaging 4th of July Content.

Have a great 4th of July weekend!












What chart should I use?

Friday, April 1st, 2016

It’s time to put your carefully collected data into a chart, but which chart should you use?  And then how do you set it up from scratch in your Excel spreadsheet or Power Point presentation if you aren’t experienced with charts?

Here’s one way to start: go to the Chart Chooser at Juice Analytics.  They allow you to pick your chart and then download it into Excel or Power Point. Then you can simply put in your own data and modify the chart the way you want to.

They also have a way to narrow down the options.  As a hypothetical example, let’s say a fictional health science librarian, Susan, is in charge of the social media campaign for her library.  She wants to compare user engagement for her Twitter, Facebook and Blog posts to see if there is any patterns in their trends. Here are some fictional stats showing how difficult it is to find trends in the data.

Monthly stats of blog, Twitter and Facebook engagement

Susan goes to the Juice Analytics Chart Chooser and selects from the options given (Comparison, Distribution, Composition, Trend, Relationship, and Table).  She selects Comparison and Trend, and then also selects Excel, because she is comfortable working in Excel.  The Chart Chooser selects two options: a column chart and a line chart.  Susan thinks the line chart would work best for her, so she downloads it (by the way, you can download both and see which one you like better).  After substituting their data with hers, and making a couple of other small design changes, here is Susan’s resulting chart in Excel, showing that user engagement with both blog posts and Facebook posts shows a pattern of increasing and decreasing at the same time, but that Twitter engagement does not show the same pattern.

Line chart of Blog Twitter and Facebook engagment

By the way, the total time spent selecting the chart, downloading it, putting in the fictional data, and making chart adjustments was less than 15 minutes.  Is it a perfect chart?  Given more time, I would suggest adjusting some more of the chart features (see our January 29, 2016 post The Zen Trend in Data Visualization). But it was a very easy way to pick out a chart that allowed Susan to learn what she needed to from the data.

One thing I want to point out is that this is not a complete list of charts.  This is a good starting place, and depending on your needs, this might be enough. But if you get more involved in data, you might want to take a look at small multiples, lollipop charts, dot plots, and other ways to visualize data.  Check out Stephanie Evergreen’s EvergreenData Blog  for more chart types.


The Zen Trend in Data Visualization

Friday, January 29th, 2016

You may have noticed there is a data visualization bandwagon out there and the OERC bloggers are on it. We like to write about tools and resources that can help you communicate visually.  There’s a bit of self-interest here. We want to fuel this bandwagon because we personally prefer to review a one-page visual synopsis of data over a table-dense written report.

Unless, that is, we have to grapple with a poorly designed data visualization. These are displays with too many details; unnecessary icons; many variables piled into one chart. It can make your head spin.

That isn’t to say you have to be an artist to do good visual displays. Quite the contrary.  Most information designers adamantly state that data visualization is about communication, not art. However, you have to know how to design with a purpose.

To understand the basics of good design, you need to understand why humans respond so well to visual displays of data.  That is the topic of an excellent blog article by Stephen Few, a thought leader in the data visualization field. First, he advocates that data visualizations aid users in performing three primary functions: exploring, making sense of, and communicating data.  Evaluators would add that our ultimate goal is to help our users apply data in planning and decision-making. To that end, Few argues that data visualizations should be designed to support readers’ ability to perform these four cognitive tasks:

  1. See the big picture in the data
  2. Compare values
  3. See patterns among values
  4. Compare patterns

To demonstrate Few’s point, I am sharing a sample of the OERC Blog’s monthly site statistics.  The blog dashboard uses a table format to display total views per month. The table shows monthly view counts starting in June 2014, when we started tracking site statistics.

Table showing total page views per month for the OERC blog from June 2016 to Jan 2016.  The numbers generally get higher over time, but there is fluctuation month to month. It is difficult to see trendts


Line graph of data shown in previous table (June 2014 to January 2015) It shows total page-view-per-months. The general upward trend is much more apparent than in the table. 2015 has more page views than 2014 for all months. There are some times during the year where there is a zigzag pattern, with high viewership one month followed by lower readership the next. This would suggests a need to look at topics or schedules of the blog readership.

Now here’s the same information presented in a line graph created in Excel.  You quickly see the big picture: Our page views are trending upward.  You can easily compare the direction of month-to-month traffic.  The chart allows comparison of monthly patterns in 2014 and 2015.  It’s the same data, but its almost impossible to see any of these findings in tabled data.

Dataviz design experts like Stephen Few are avowed minimalists. They hate chart junk, such as gridlines and data labels.  They have an affinity for small multiples, which are series of graphs displaying different slices of data.  (If you have never seen small multiples, here’s a post from Juice Analytics with good examples) In general, they do not include any element that will hinder users’ ability to make comparisons, find patterns, and identify pattern abnormalities that may be indicators of important events.  Needless to say, most data visualization luminaries are not big on decorative features like gas gauges and paper doll icons. These, too, are viewed as unnecessary distraction.

Yet, there is a lot of chart art out there and you may wonder why. There is a distinction between data visualizations and infographics.  Alberto Cairo, Knight Chair in Visual Journalism at the University of Miami’s School of Communication,  wrote that data visualizations are tools for interactive data exploration while infographics are visual displays that make a specific point.  I tend to think of data visualizations as having users, while infographics have readers. Chart art may be more legitimate in infographics because it supports the primary message or story.

Yet, Cairo admits that the boundary between infographic and data visualizations is fuzzy. He noted a trend toward infographics with two layers: a presentation layer, and an exploration one.  The infographics have an obvious primary message, but readers are also presented with opportunities to explore their own questions. One of my favorites from the Washington Post is an example of an infographic with both layers.

That said, Cairo still argues that data design principles hold true for both data visualizations and infographics.  There is no excuse to drown your readers in images or to distract them with bling.

Zen is in; bells and whistles are out. The good news is that simple data visualizations do not require sophisticated software or design skills.  That’s not to say that simple is the same as easy. Good data visualizations and infographics take a lot of thought. For the more interactive data visualizations, you must identify how your users will use your data and design accordingly. For infographics, you need first to clearly identify your central message and then be sure that every element has a supporting role.

Want to start developing good visual design habits? I recommend Presenting Data Effectively by Stephanie Evergreen (Sage, 2013).



Fun for Data Lovers: Two Interactive Data Visualizations

Wednesday, December 23rd, 2015

‘Tis the season of gift-giving and who doesn’t love getting toys during the holidays? So we want to give our readers links to two fun data visualizations to play with over the holidays. Both were designed by David McCandless at Information is BeautifulSnake Oil Superfoods summarizes the evidence (or lack thereof) for health claims about foods popularly believed to have healing properties. Snake Oil Supplements gives the same scrutiny to dietary supplements.

You can readily check the science behind the infographics. Both link to abstracts of scientific studies indexed in reputable sources such as PubMed or Cochran Library. The pretty-colored bubbles and the filters give you an enjoyable way to check some of those food-related miracles being proclaimed in popular magazines and your Facebook feed.

Meanwhile,  Happy Holidays to you and yours from OERC bloggers Cindy Olney and Karen Vargas.

Christmas 2015
Cindy (left) and Karen at the American Evaluation Association’s 2015 Conference




Dashboards for Your Library? Here Are Some Examples

Friday, December 18th, 2015

illustration of different business graphs on white backgroundLast week’s blog post was about using Excel to make data dashboards. As Cindy pointed out a dashboard is “a reporting format that allows stakeholders to view and interact with program or organizational data, exploring their own questions and interests.”

What can that mean for your library? What does a library data dashboard look like?

In the OERC Tools and Resources for Evaluation, we have a Libguide for Reporting and Visualizing, which includes a section on data dashboards.  In it are some examples of libraries using data dashboards.  In their dashboards, libraries are sharing data on some of the following things:

  • How much time is spent helping students and faculty with research
  • What databases are used most often
  • How e-books are changing the library picture
  • What librarians have been learning at their professional development conferences
  • What is the use of study rooms over time
  • What month is the busiest for library instruction
  • What department does the most inter-library loan

Can you create a dashboard to tell a story? While libraries can keep (and post) statistics on all kinds of things, consider who the dashboard is for, and what story you want to tell them about your library.  Maybe it’s the story of how the library is using its resources wisely.  Or maybe it’s the story of why the library decided it needed more study rooms.  Or the story of whether or not the library should eliminate it’s book collection and increase e-books and databases.

Consider what data you want to share and what people are interesting in knowing.  Happy dashboarding!

Excel Dashboards at

Friday, December 11th, 2015 is a website that excels at Excel.  More specifically, it provides an extensive collection of resources to help the rest of us use Excel effectively.  There’s something for everyone at this website, whether you’re a basic or advanced user. Today, however. I want to specifically talk about’s resources on building data dashboards with Excel.

Data dashboards are THE cool new data tools. A dashboard is a reporting format that allows stakeholders to view and interact with program or organizational data, exploring their own questions and interests. When the OERC offered a basic data dashboard webinar several years ago, we hit our class limit within hours of opening registration. If you are unfamiliar with data dashboards, here are slides from a presentation by Buhler, Lewellen, and Murphy that describe and provide samples of data dashboards. .

Tableau seems to have grabbed the limelight as the go-to software for data dashboard development. Yet it may not be accessible to many of our blog readers.  It’s expensive and, unless you are a data analyst savant, Tableau may require a fair amount of training.

The good news is that Excel software is a perfectly fine tool for creating data dashboards. Some of the best known data visualization folks in the American Evaluation Association (AEA) are primarily Excel users. Stephanie Evergreen of Evergreen Data  and Ann Emery write popular blogs about data visualizations built from Excel. At the AEA’s annual conference in November, I attended a presentation by Miranda Lee of EvaluATE on creating dashboards with Excel.  She has some how-to dashboarding videos in the works that will be available to the public in the near future. (We’ll let our blog readers know when they become available.)

Hand of a business man checking data on a handheld device

There are free resources all over the Internet if you are good at do-it-yourself training.  However, for a modest fee, offers a more systematic class on how to design a data dashboard with Excel. Depending on how many resources you want to take away from the class, the cost is between $97 (online viewing only) and $247 (downloads and extra modules). I have not taken the class yet, but I have heard positive feedback about’s other courses and have plans to take this class in the near future.

If you are an Excel user but don’t see dashboard-building in your future, you still may find a wealth of useful tips and resources about Excel at My favorite is this list of 100+ Excel tips. I attended several data dashboard sessions at the AEA conference last month. The word on the street is that Microsoft is rising to the challenge to develop its data visualization capabilities.  Apparently, each new release is better than the last.  It may be getting easier to work dashboard magic with Excel.

Last updated on Monday, June 27, 2016

Funded by the National Library of Medicine under Contract No. UG4LM012343 with the University of Washington.