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When COVID-19 pushed many events online, I decided to host a virtual Christmas trivia event for my family.

Participants answered questions over four different rounds in teams of three to six people. The rounds each had five questions and all questions were Christmas- or New Year-themed.

Before: Compiling the Data in a Spreadsheet

To support scorekeeping, teams had an individual score sheet where they wrote and marked their own answers.

I had a master score sheet that would automatically pull their scores together so all teams scores were combined into one page.

I’d then show this master score sheet via screen share at half-time and at the end of the event.

The master score sheet looked like this:

While it brought all the scores into one place, it wasn’t very easy for my participants to quickly pull out the key information (i.e., how well their team was doing).

I decided to apply some of the lessons I learned in the Dashboard Design course to make the sheet more accessible.

After: My Trivia Night Dashboard

First, I had to decide what type of dashboard I wanted to make.

In the course, Ann provided a handy Dashboard Cheat Sheet that helped me see different options.

I decided because I had one time point and wanted to compare categories (i.e., teams) that bars would likely be best.

I also decided to convert the numerical scores into percentages because not all rounds had the same number of possible points. Percentages would be a more consistent indicator.

Now it was time to make the dashboard.

It was easy to follow along step by step with Ann’s stacked bar dashboard video tutorial.

I made the following dashboard using the Data Bars feature in Microsoft Excel:

What I Learned about Dashboards and Excel

Not only were the steps easy to follow, but I also learned about better dashboard and Excel practices.

These tips help make your life easier and your dashboards more editable and readable.

Some of my lessons learned include:

• Always put a title, subtitle, and date on the dashboards.
• If your text is in a colour, make it bold so it is easier to read.
• Add a white border around cells to add white space.
• Use cell styles and Theme Colours to make formatting more consistent and easier to edit (I somehow did not know about this in Excel even though I use it regularly in Word).
• Give yourself a bit of time to do the final editing to make it sure fits on a page

With this dashboard, I found it much easier to see:

• How well teams did in each round (e.g., team 6 struggled with Round 4, but excelled in Round 3).
• How teams compared to each other.
• How hard each round was (e.g., Round 2 was on average harder than Round 3).

Designing a Second Dashboard

Encouraged by my dashboard attempts, I decided to try one more dashboard.

I wanted to know within each round, which questions did teams get right and wrong.

This would help me identify which questions were too easy and which were too hard. It’s a fine balance to get when hosting trivia!

I thought about including it in the same dashboard above, but I then I watched one of Ann’s videos about the four types of dashboards.

This reminded me that it’s okay (and even better) to make different dashboards for different audiences.

I had to do a bit of data cleaning first. I ended up with a table that showed for each question in each round the percent of teams that got that question fully correct:

While it had the information that I needed, I found it hard to identify any patterns or the take home message.

I remembered that in the Dashboard Design course Ann had a video on how to compare categories using heatmaps. (Here’s a blog post tutorial you can read.)

I used the steps to create this:

What I Learned from My Second Dashboard

As with the first dashboard, there were some great tricks.

Essentially, if you’re doing something manually (like changing the text colour to white on the darker cells or individually colouring cells) there is almost always a better way! You can use Excel’s conditional formatting to automatically color-code background fills and/or font colors.

I found it much easier to identify patterns both within round and across rounds.

For example, teams generally had a harder time with questions in Round 2 than they did with Round 3 (there are more lighter cells).

Using this dashboard, I could easily pick out questions which were too hard and too easy.

Questions That Were Too Hard

Round 1 – Question 4: What is the name of this dish and where is it eaten on January 6? (Answer: Rosca de Reyes; Mexico)

Round 2 – Question 2: What is the highest grossing Christmas movie (according to Wikipedia)?

Options: a. The Grinch b. Krampus c. The Polar Express d. Elf

(Answer: The Grinch)

Questions That Were Too Easy

Round 4 – Question 2: What fruit is a traditional stocking stuffer?

(Answer: Citrus fruit like an orange, mandarin, clementine)

Round 3 – Question 4: What performance is this song played in? (Bonus: Who is the composer?)

(Answer: The Nutcracker; Tchaikovsky)

(Sound clip source: Dance of the Sugar Plum Fairy Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0 License, via Wikimedia Commons)

I really enjoyed how approachable and practical the videos in this course were.

I can’t wait to continue to apply the tips and techniques I learned both at work and for fun!

Maybe at next year’s trivia I’ll have to test some of the dashboard designs for comparing change over time.

Connect with Emily

LinkedIn: https://www.linkedin.com/in/emilysross/

The post Using Dashboards to Make a Family Trivia Event Even Better appeared first on Depict Data Studio.

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Thank you to Ann Emery, Depict Data Studio, and her Simple Spreadsheets class for inviting us to talk to them about the use of statistics in nonprofit program evaluation! If there’s one thing that we never want to miss, it’s an opportunity to tell people their job is easier than they think!

This is why we created ImpactStory Coaching – because it’s actually within reach of small- and medium-sized nonprofits to be clear, confident, and convincing when talking about their impact!

It was also just nice to talk to a group of smart, creative, data-minded professionals who also (yes – that’s right – also) feel overwhelmed by the prospect of having to use statistics.

We used to think our feelings of statistical overwhelm in the nonprofit space was because we were wimps (even though we have literally taken a combined SIX YEARS of graduate-level statistics*).

But then we realized that much of the time, statistics just don’t have much of a role in nonprofit work. And here’s why!

Why Nonprofits Shouldn’t Use Statistics

When working in the nonprofit world sample size is usually very small (i.e., the number of clients served in any given year is usually 40 to 400 people).

Even if you have more people than that (e.g., a school district’s worth of students), it’s still unlikely you need statistics, unless you are trying to answer a scientific-type question (and what scientific-type questions nonprofits with a lot of data might ask is for another blog post on another day).

Simply having a statistically significant group of survey respondents for such a small number of people means you have to get surveys from A LOT of people: 37 of 40 and 196 of 400! This is really hard to do (although we do have tricks for making sure you get surveys from almost 100% of your clients)!

Statistical significance is often mistaken to mean a big difference, but what it really means is a not random difference (e.g., if you looked at a different group of people, you’d find that difference again… it’s a reliable difference). When you have a small number of people, it also means you must have a GREAT BIG DIFFERENCE to get statistical significance.

The math just works out that if you look at 1 million people, just about any finding is statistically significant (e.g., a tiny difference in a big group is almost always not random), but when you’re looking at 100 people, you must have a really big difference to get statistical significance. In the science world, if you have a small group of people and do not find statistical significance, one thing you can do is test a much bigger group!

A Nonprofit’s Mission is to Serve as Many People as Possible to Address an Identified Need

In order to use statistics to identify the impact of a program, you usually need a comparison group (e.g., a random group of people who do not get the program) to which you compare your clients. Ideally, both groups are selected randomly: the people getting the program, and the people not getting the program. (We all know that’s not happening!)

Realistically, the comparison groups available to a nonprofit are either people the nonprofit randomly refused serve – or – a very nonrandom group of people who didn’t want the services the nonprofit was offering. Scientifically, neither of these are good options for comparison groups.

And we’ve never met a nonprofit so flush that they had money to track people they don’t serve. Even if a nonprofit had money to spare, spending this way would not be aligned with its mission to serve as many people as possible.

Nonprofits are Not Set Up to Follow People for a Long Time after Service Provision (e.g., 6 Months to 40 Years)

Most nonprofits provide a service for a specific amount of time, people graduate from that service, and then they go on with their lives (hopefully with more strategies to reach their goals). Staying in touch with people over time is very time consuming and very, very expensive – especially if you want to stay in touch with at least 80% of people (which is a minimum in the scientific world).

If you’re a smaller nonprofit that means you have to track 32 to 320 people over time (to follow up on those 40 to 400 clients you served). Frankly, this is both impossible and still too small to analyze with statistics (see point #1 about statistics with small groups of people).

Using scientific methods to test hypotheses (which are what statistics test) are what scientists do; delivering programming and tracking client progress is what nonprofit practitioners do. 

We have used this example before:  Scientists discover and test medicines to make sure they work. Doctors deliver what’s been shown to work and make sure the people they treat get better.

Two. Separate. Jobs. 

You – our nonprofit friends – are doctors.

Nonprofits Focus on Working with Individual People and Complexity Not Populations and Averages (Which is the Realm of Science)

It’s a cognitive error to assume that statistics (which typically focus on averages) applies to individuals.

For example, the average number of car crashes a person gets into in a lifetime is four (this is scientific knowledge derived from statistics) – but we all know people who get in many more crashes and people who never get in a crash (this is the reality of being an individual in the complexity of life).

And, if you – as an individual – get into four crashes that does not mean that you are now immune to getting in crashes (this is the cognitive error of applying statistics to individuals)!

So, drive safe and buckle up!

Also, if you are concerned about diversity and equity then you need to have more people from marginalized groups from whom you gather data so you can really hear what they have to say. Period.

You do not want to just have a representative number (e.g., a number equal to the proportion in the larger population) because their voices get washed out in the average.

When doing nonprofit work: Each. Individual. Voice. Matters.

What Can Nonprofits Do?

Nonprofits Should Think of Themselves as Conducting Qualitative Analysis with Numbers and Stories

Qualitative analysis basically means you are looking for patterns and changes in patterns in both your numbers data (what people report on surveys) and your stories data (what people tell you in words).

You’re examining how the data look – the shape, the themes, the patterns that emerge, and when the patterns change.

Your Data Team is the litmus test for determining which things are important and meaningful and which things are not.  Data Teams are for answering questions in real life; experimental design and statistics are for answering scientific questions. (Ask us more about Data Teams!  We love to talk about them!)

You HAVE TO GRAPH Your Data to See How it Looks

If you do use math at all it’s probably only to calculate the percent difference, the percent change, and maybe a risk ratio. This means you count how many people say something and how many people didn’t say that.

Graph all the answers in both groups. Then break the groups up differently to better understand the patterns of responses (e.g., males and females, comparisons based on race or income or zip code or classroom or age. . . you get the picture). If you don’t graph your data, you’re sunk. 

You simply have to graph it to see what it’s doing. Mostly bar charts (to compare groups) and line graphs (to look at stuff over time).

Here are some examples:

• Count how many people said “Strongly Agree” and “Agree” compared to “Disagree” and “Strongly Disagree.” What’s the percent difference between the two groups?
• Decide how different these counts are: meaningfully different or slightly different? The best way to make the most valid assessments of how meaningful the differences are is to use a Data Team. (We love a good Data Team!)
• Based on what you know about the people you serve, as well as changes in the community and at your organization, what do you think might be the reasons for those differences? (These are follow-up questions your Data Team can ask during their data review meetings.)
• Think about how you might divide the groups into different groups, or subgroups, to explore deeper questions (e.g., males and females, wealthy and financially struggling, graduated and not graduated, etc.). If you are looking at disparities, what’s the risk ratio of one group having a poor outcome compared to the other?
• If you have data over time (i.e., surveys from the same people at different times) – you may want to look at percent change happened over time?

What Software Should Nonprofits Use?

99% of the time. . . Excel.

If you have data with lots of complex relationships (e.g., data from parents and children, over time, in different programs) you probably want to be using a database like Apricot.

Then you can create and run reports that graph your data with the touch of a button!  And you can still can create downloads of the data behind those reports and create your own graphs in Excel.

If you have many hundreds or thousands of people you are serving, then it’s just easier to clean and sort that data in a statistical package like SAS, SPSS, or R. 

In these cases, we choose to hire someone who’s very good at these programs (like a graduate student taking a stats class) and pay them like $30-$40/hour to clean the data, maybe do some descriptive statistics and show us some averages. Then we have them download the clean dataset (a delimited CSV) and we pull that into Excel for graphing!

Tableau is great for being able to create dashboards you can manipulate and post on the web. But you actually have to know what graphs you want before creating them.

So, we’ll create the graphs we want (in Excel!), and then hire someone to transform the data and recreate those graphs in Tableau so nonprofit leadership can manipulate them or post them on the web.

We hope all this is some weight off your shoulders!  Sign up here to stay connected with us and follow us on all the social media! We have lots more to share!

Connect with MaryFrances Porter & Alison Nagel

Partnerships for Strategic Impact: https://impactstorycoaching.com/

Maryfrances Porter, Ph.D.  – LinkedIn: @maryfrances-porter-psi/

Alison Nagel, Ph.D. – LinkedIn: @alison-nagel-41493a125

The post Why Nonprofits Shouldn’t Use Statistics appeared first on Depict Data Studio.

The default: The data just sits there inside a Dusty Shelf Report.  

But what if your data could actually inform real-life decisions?  

I recently sat down with Vivian Jefferson from Loudoun County Public Schools, a growing district in the Washington, D.C. metro area.   

Vivian and I are both members of the same Facebook group (a community for everyone taking data visualization training with me).  

A couple weeks ago, Vivian mentioned that her graphs had been featured on the news (!!!).  

The topic was school reopening plans for the 2020-2021 academic year. Vivian and her colleagues had collected surveys from parents and teachers to gauge their opinions.  

Watch the Conversation Below 

Vivian and I talked about the 52,000 surveys that her office designed, administered, cleaned, and visualized within a two-week timeframe.  

She’ll teach you how they visualized the data, making sure to have detailed reports for technical audiences and a storytelling slideshow for a school board meeting with hundreds of attendees.  

And, she’ll tell you how her graphs ended up being featured inside a news story for an even broader audience. 

About Vivian Jefferson 

Vivian works in the research office of Loudoun County Public Schools in Virginia. The district is one of the largest in the state, with 83,000 students and 94 school facilities (and counting–they open a new school almost every year!). They average about 2,500 new students each year.  

The research office is a four-person team consisting of a program analyst, data analyst, office supervisor, and Vivian, who manages surveys.  

How Vivian’s Role Has Shifted Due to COVID-19 

Vivian noted that the data requests have been more urgent and bigger in size as the leadership tries to make decisions quickly. Loudoun County Public Schools closed in early March but was able to implement some online learning using existing tools.  

“The leadership wanted to monitor how that was going: Are students logging in, are they engaged? What we found was that the tools that we had couldn’t necessarily collect all of that data. We could tell how many students were logging in, but not if that were completing the activities or for how long they were logged in,” said Vivian.  

Vivian and her colleagues have been doing more surveys to try and find where people stand and what their concerns are.   

Vivian also said that the biggest impact she feels has been on what they haven’t been able to do.  

In the spring, they usually conduct assessments to see how students have progressed. They haven’t been able to do that, so they don’t know if the interventions they had in place worked. They also don’t know what the student needs and strengths are going into the next school year.  

“We won’t have the whole last quarter of data to be able to compare with previous years. Anytime we see trend data for 2020, it’s going to have an asterisk that it’s showing only three-quarters of the data. And I think that’s probably happening all over with school districts across the country,” Vivian said.  

Designing the School Reopening Surveys 

Let’s dive into the survey that was featured on the news. 

School leaders requested a “survey of families and staff to see where their comfort level is with these three models that we’ve developed, what they’re concerned about and their needs are.” The school system was considering three models: 100% in-person, 100% virtual, or a hybrid. 

Vivian’s office designed and administered two surveys: one for parents and one for all school-based staff, such as administrators, office staff, and other professionals in addition to teachers. 

They reviewed similar surveys from other school districts, and then added questions specific to their own county. 

Parents were asked about their spring 2020 online learning experience; which of the three reopening models they preferred; whether they had computer access for distance learning; and more. 

Staff were asked whether they received the support and resources they needed in spring 2020; whether they would be comfortable being inside a classroom with physical distancing measures in place; and whether they were comfortable taking their temperatures and wearing face coverings. 

Then, the surveys were translated into Spanish, and links were emailed to parents and staff, and further promoted on social media.  

Collecting the Survey Responses 

Vivian said, “We knew we were going to get a lot interest in it because it’s such a hot topic. We do a school climate survey every spring for staff and parents. The parent survey usually gets 11,000 to 12,000 responses. This survey had 46,000 parents respond. And then about 6,000 staff responded (usually only a couple thousand respond). It was huge.”  

Vivian’s office designed the surveys, collected 52,000 responses, and compiled the data into reports and a slideshow within just two weeks. 

Visualizing the Data 

Vivian color-coded the data to make the categories easier to navigate. For example, they consistently used teal for elementary schools, orange for middle schools, gold for high schools, and blue for the county. 

Vivian also drew attention to key findings by making pieces of the visuals darker or lighter: 

Vivian also said that, “On the titles of the slides, I tried to pull out what the main finding was, to highlight what they should be looking for.” 

The Reporting Model 

I personally love the reporting model that Vivian’s office followed. 

They developed two detailed reports plus a slideshow with key findings. And, the news story provided a high-level overview. There’s something available for every type of audience. 

Vivian and her colleagues have evolved their communications strategy. “When I first started there 14 years ago, we were doing the full Dusty Shelf Reports.  Over the past few years, we’ve realized that our decision makers need data to make policy and decisions within a few weeks. They don’t have time to wait for a year long, in-depth program evaluation. We’ve been kind of gearing up for a fast response model of reporting anyway, but this was really fast.” 

Two 13-Page Technical Reports 

Vivian’s office shared detailed results within two 13-page reports, one for the parent survey and one for the staff survey. 

These reports contained tables of both quantitative and qualitative survey results. 

The Slideshow 

Vivian and her colleagues also developed a slideshow, which would be presented at a school board meeting. The slideshow was viewed by school board members, administrators, staff, and parents. 

The News Story 

Finally, the news article and 90-second video provided a high-level overview of the survey results. 

Vivian said she was very surprised to see that someone on a Facebook group she’s a member of linked to the new story and said, “LCPS was on the news today!”  

“I thought, ‘I wonder what they said?’ And I clicked on it, and watched it, and I almost fell out of my chair, literally. They had used the graphs from my presentation!” she said.  

The news station used several of Vivian’s graphs, even enhancing one by circling one set of columns that they wanted to draw attention to.  

The news story combined the survey’s quantitative data with audio clips from the public comment portion of the school meeting: 

“I knew that people would be looking at the report, but I thought mainly like the school board, people who tuned in to watch the school board meeting,” Vivian mentioned. “But I didn’t realize that people would take anything from it and use it in a different way.” 

Reactions from the School Board and Parents 

And, a couple days after the school board meeting, Vivian was out shopping in a store and overheard a couple parents discussing statistics from the report. 

The school board also gave Vivian’s office good feedback on the data. 

Loudoun County Public Schools had considered three models for the 2020-2021 academic year: 

1. 100% in-person 

2. 100% virtual 

3. A hybrid model 

The school system opted for the hybrid model, in which half the students would be in school at a time. Parents will also have the option to opt-out and follow 100% virtual learning.  

Note: Vivian reached out to let us know that “as typical of the times we are in, this week the school board and superintendent changed the reopening plan to be all distance learning at first, with a phased approach to the hybrid model. You can see their revised plan here: Revised Plan for 2020-21.  

Learn More about Vivian’s Survey 

The survey results were shared publicly on the school board’s site.  

• The slides that were presented at the school board meeting in June 2020.
• The tables from the parent survey.
• The tables from the staff survey.

Read the WUSA 9 story, Loudoun County School Board votes on reopening plan, and watch the 1.5-minute video where Vivian’s work was featured.

Connect with Vivian Jefferson on LinkedIn.

Your Turn 

Comment below. Let us know which part of the conversation resonated with you the most.  

The post Can Your Dataviz Have an Influence on School Reopening Plans? appeared first on Depict Data Studio.

Amanda is the Data Visualization Capability Lead at Excella; a co-organizer for Dataviz DC; and the Operations Director for the Data Visualization Society (DVS).

She’s also one of the most knowledgeable people around when it comes to visualizing COVID-19 data.

Listen to Our Conversation Here

What’s Inside: Amanda’s Career Path

“Ten years ago, when I finished graduate school, I couldn’t have guessed that this would be my full-time job and I could wear as many hats as I do in the data viz world. Now I think it’s really exciting because there are a lot of paths into data viz,” Amanda told me.

Excella

Amanda is currently the Data Visualization Capability Lead at Excella, a technology consulting firm. She works with organizations from the CDC to Fortune 500 companies.

“It’s been a really transformational learning experience for me to see not just different ways data and tech get used, but the different ways projects get managed. I’ve learned a lot more about Agile processes and software development and thinking about how some of those same practices actually apply when we’re building different analytical applications like dashboards,” Amanda said.

Previously, she worked in global public health.

DataViz DC

Amanda is also involved with DataViz DC. They focus on bringing people together from various disciplines, from graphic designers to software developers. They host monthly meet-ups, which have included hands-on workshops, guest speakers, and career panels. It’s a great way to connect in the DC area- there are over 8,000 members!

Data Visualization Society

Amanda is the Operations Director for the Data Visualization Society.

They are focused on bringing people together across the world and serve as a global, professional organization for dataviz professionals at any level. They have over 13,000 members from more than 130 countries around the world. They communicate through Slack, email and Fireside Chats with panels of dataviz experts.

Amanda told me that the Data Visualization Society is “a great space for actually bringing together different disciplines. Instead of focusing on one tool or tech stat, we instead said, ‘How do we bring together the people that are individually engaged in Tablueau groups? Power VI groups? R groups? Graphic designers? How do we bring all those people together and create a space for people early in their career or looking to change careers and do data visualization as their full-time job and create a space for them to grow and learn and share best practices?’”

She continued, “While we use different tools and technologies in different data viz disciplines and roles, I think there are so many cross cutting best practices that once you learn and master them in one tool, it’s really easy to think about how they are used and applied in other spaces. DVS tries to create that central space to bring people together and really advance the data viz discipline as a practice profession.”

Visualizing COVID-19 Data Responsibly

Next, I asked Amanda to share tips for visualizing COVID-19 data responsibly.

She wrote “Ten Considerations Before You Create Another Chart About COVID-19” on the Data Visualization Society’s blog in March 2020, and I also wanted to know whether her guidance had evolved or shifted since writing the article.

Amanda said, “There are different points in which we make decisions about how and what we visualize, and then how we publish and share. What are we creating and doing more for our own exploration and understanding? And what are doing so that we can share it with the public to help others make sense of information?”

Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices.

COVID-19 Data Quality Issues

Amanda said, “Consider the fact that even though the dataset are very accessible right now, does not mean it is high quality data.”

“There are so many issues and challenges with the different ways that COVID-19 cases are counted in different states or countries. Are we including only cases that have been lab confirmed with a swab test that came back positive? Or are we also including probable cases or diagnostically confirmed cases?

How those cases are counted are very different in some states and countries. It’s really hard to make these apples to apples comparisons, as easy as it might seem since the data is so accessible.”

Understanding COVID-19 Data Collection

Amanda said, “If you’re going to dabble in COVID-19 data in some way, try to really understand how the data gets collected so that you have a firm understanding of what that process looks like and why there might be issues with the accuracy, timeliness or completeness of those datasets.”

She continued, “Make sure you understand how the data got collected. Just because it’s there as a nice shiny, analyzable table doesn’t mean it’s not something you should try to understand the underbelly of.

In your choices around how you analyze and design certain visualizations, be mindful that the data that we have is really incomplete. The case data is really a function of how many tests are being done.  As a result, where we have more certainty on the death counts, deaths are also a function of cases.

So we have to think about the fact that we have a lot of cases not represented in that data. We’re seeing that come out more and more in some of the retroactive reporting being done in countries that are farther along in their epidemic curves.

Remember that there’s a lot of uncertainty in the data and it’s not uncertainty that we can represent visually well. We can’t always quantify that uncertainty.

Make sure that you’re considering the ways in which your visualizations could be misinterpreted or misused.”

Be Careful with Comparisons and Reference Points

Amanda said, “One of the common comparisons I’ve seen is comparing COVID-19 to the flu.

We’ve seen that in the media and early on even public health folks were trying to make sense of this disease by comparing it to the flu.

When we look at how we collect data on the flu in the US, we have routine, structured reporting systems for that data with better quality data. We have a disease that comes in seasonally and we understand what that seasonality looks like. We don’t know that about COVID-19.

So comparing cases in March for COVID-19 and the flu [doesn’t make sense]. We’re in a very different point of that epidemic curve for COVID-19. It really isn’t an apples to apples comparison. We’d need a whole year of COVID-19 data to start to make that comparison.

So be cautious in how you start to try to create those reference points which can help us enable understanding, but also can mislead.”

Remembering the People Inside the COVID-19 Datasets

Amanda said, “Remember that every single case and every single death represents a person. As we visualize and think about health-related data, thinking about that fact that each of those cases and each of those deaths represents a person and their story, makes it really important to be thoughtful and mindful about how we’re presenting that information.”

COVID-19 Data Visualization Color Choices

Amanda said, “Those red, big bubble maps can be hard to interpret, but the color choice also creates such a visceral, angry, sad response. I hope we can be thoughtful in the ways our visualizations can create emotional responses especially when visualizing such sensitive data.”

Connect with Amanda Makulec

• Data Visualization Society: DataVisualizationSociety.com/Join
• Slack Workspace: DataVizSociety.Slack.com
• Twitter: @ABMakulec

The post Visualizing COVID-19 Data Responsibly: An Interview with Amanda Makulec appeared first on Depict Data Studio.