This “tons of separate columns” format doesn’t play well with formulas, pivot tables, or charts.

In this tutorial, you’ll learn 3 workarounds:

https://www.youtube.com/watch?v=7VMlRPyra1g

What’s Inside

• 0:00 The challenge: Survey response options are exported into tons of separate columns. ​
• 1:10 Solution 1: Manually type, copy/paste, or use CTRL +F+replace
• 1:55 Solution 2: Create a new column and =sum them
• 3:10 Solution 3: Just use a pivot table

Download the Spreadsheet

And try it yourself. It’s here.

The post Survey Response Scales in Different Columns?! 3 Excel Workarounds appeared first on Depict Data Studio.

But we can use the “brackety compass rose” to customize our pivot tables… which lets us customize our graphs.

For example, with this technique, we can re-sort Agree, Disagree, Neutral, Strongly Agree, Strongly Disagree…. into Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree.

Learn how in this lesson:

What’s Inside

• 0:00 Intro
• 1:18 Re-Sorting Months in Pivot Tables
• 6:26 Re-Sorting Survey Response Options in Pivot Tables

Related Resources

• What are Helper Cells?
• How to Sort Your Bar Charts

The post Re-Sorting Categories in Pivot Tables 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.

While planning for the session, I asked conference attendees to submit examples from their reports, dashboards, and slideshows that I could makeover as part of the talk.

Later, during the live keynote, I shared a few data visualization principles. Then, as a group, we practiced applying those principles to their real projects.

Here’s one of my favorite submissions:

This conference attendee worked at an organization that placed children into foster care homes. Each year, the organization surveyed their foster care parents to gather their feedback about the experience.

Virtually every organization conducts satisfaction surveys of one kind or another, so even if you don’t work for a foster agency, keep reading!

I’m going to show you the before version followed by five makeovers.

The first makeover didn’t work. The second makeover didn’t work. The third makeover didn’t work. The fourth makeover didn’t work. Just as I was about to give up, I found a winning design with my fifth attempt!

Let’s make fun of my first few attempts together. Then, we can celebrate the fifth attempt together.

I’m going to provide a behind-the-scenes peek into my thought process so that you can apply my thinking to your own projects.

What’s Already Working Well: Length and Context

A couple things were already working well in the before version.

First, I was pleasantly surprised to see that they fit all 22 survey questions and the responses on a single page. They wanted an at-a-glance handout, not a full report. All too often, I witness organizations drone on and on about simple survey results. It’s just a survey. Keep simple things simple, please.

Second, I was pleasantly surprised to see two years’ worth of data included: fiscal year 2016 and fiscal year 2017. All too often, I see annual survey results that only provide the current year’s data. Without historical context, we don’t know whether the current year’s data is any better or worse than previous years. Providing patterns over time is always a good thing.

What’s Not Working: Clutter, Order, Clustered Bars, and Analysis Approach

We’ll declutter the one-pager, obviously. We need to remove the gray background shading. How are the foster agency’s leaders supposed to make decisions based on this data if they can’t even see it?

We’ll also re-order the survey questions. Right now, questions are listed in the order they were asked on the survey: 1, 2, 3, and so on. Presenting survey results in the same order as the survey is rarely the best approach. Instead, we’ll group the individual questions into categories.

Next, I wanted to find an alternative to the clustered bars. Clustered bars are my least favorite chart of all time—even more so than 3D exploding pie charts! Clustered bars aren’t inherently evil. They’re just overused.

Finally, the analysis approach was a bit off. The agency asked foster parents whether they were completely satisfied, very satisfied, satisfied, not very satisfied, or not at all satisfied. They coded a completely satisfied as a 5, a very satisfied as a 4, and so on. Then, they calculated the average score. For example, Staff are courteous and respectful got an average score of 4.5 in 2017.

Although this numeric coding approach is common, it’s not correct. Variables can be nominal (favorite ice cream flavors), ordinal (this satisfaction scale), interval (the scale’s points are equidistant), or ratio (the scale has a true zero, e.g., a height of 0 feet means zero height). If this is the first time you’re learning about nominal, ordinal, interval, and ratio scales, check out this article to learn more.

You can only calculate averages on interval or ratio scales, but the survey has an ordinal scale. In other words, the agency should’ve displayed how many foster parents selected completely satisfied, very satisfied, and so on for each of the questions instead of calculating an average score.

I didn’t have access to the raw dataset while designing this makeover, so I’m going to have to display the average scores here. It’s not the end of the world. But I did cringe during the makeover process. And I’m definitely cringing again during the blogging process.

Makeover 1: Slope Graphs Didn’t Slope…

I experimented with a few makeovers before I settled on a winning design. Here’s the first makeover. Wait! Before you start frowning and rolling your eyes, hear me out. Let’s look at what’s working, and then I’ll be honest with you about what’s not working.

I kept all of the makeovers to a single page, which was a fun challenge. I kept two years’ worth of data. I dramatically decluttered the page, removing the background shading, vertical lines, and horizontal lines from the original. And I grouped the survey questions into categories and then color-coded by category (Staff in turquoise, Clients in purple, Caseworkers in red, and so on). You can learn more about color-coding by category here.

I love most aspects of this redesign.

The major shortcoming of this data visualization makeover is the visualization itself—darn!

We have a few options for comparing two points in time, like these two fiscal years. The most obvious choice is a line chart, which was born with the sole purpose of displaying patterns over time. A slope chart is just the line chart’s cousin; it displays exactly two points in time.

The problem is that the slopes didn’t slope. The visuals were too short to show much of a difference.

Most designs require compromise. I decided that it was more important to limit the makeover to a single page than to increase the height of each slope chart (and, therefore, spill the survey results onto a second page).

Makeover 2: Columns Were Too Short…

Here’s my second attempt.

In this iteration, I visualized the data with column charts.

You can already see the problem, right? The columns were too short to see any differences.

If I didn’t tell you that these were supposed to be column charts, then you might’ve assumed they were just funny-looking squares.

I’m not upset that this makeover didn’t work. I wasn’t rooting for the clustered column approach anyway!

Onwards. I’ve still got a few more ideas up my sleeve…

Makeover 3: The Heat Table Was Too Colorful…

Heat tables are helpful when you’re working with limited space, like my self-imposed rule of limiting myself to a single page, just for fun, ha! The colors live on top of the numbers, not beside them, so they take up less space.

I can generally see that the FY17 column is darker than the FY16 column (ratings were higher in FY17 than in FY16). But I have to work to see the darker colors because I’m distracted by the rainbow in front of me.

If I wasn’t stubbornly devoted to a one-page design, then I could’ve added an empty row between the categories. For example, you’d see the turquoise section for staff. Then, there would be a centimeter of white space. Then, you’d see the purple section for clients. But again, every design is a compromise. I was committed to a one-page design. I couldn’t turn back now!

Makeover 4: Check Boxes Provided Overly Positive News…

As I was critiquing the heat tables, I finally realized that the FY17 results were better than the FY16 results. I hadn’t actually noticed that pattern while looking at the original, at my slopes, or at my columns! Spotting this pattern was a game-changing aha moment.

In this redesign, I opted to focus on big-picture results: that foster parents scored the agency higher in FY17 than in FY16 on every survey item except one. The filled-in squares and empty-squares are easy to scan at a glance. I’ve used square icons in dozens of real-life projects, and I’ve talked about them a few times on the blog before, too. You can read this post to learn how I created them. The filled-in square is a lowercase g in the Webdings font and the empty square is a lowercase c in the Webdings font.

The downside was that the check boxes provided overly positive news.

Can you have too much good news? I think so. Pretend that you’re a leader at the foster care agency. You see this handout at a meeting. You’ve improved from one year to the next on nearly everything! This is great news! There’s nothing to fix! Everything’s working! Except for that one survey question, but that’s just one thing, so who cares! No need to try any harder next year! You’re already doing everything perfectly… or are you?

I didn’t want to encourage complacency. I wanted to provide actionable ideas for improvement.

Makeover 5, the Winning Makeover: Deviation Bars

Finally! The winning makeover! It only took four failed attempts to get this one right…

I loved the simplicity of the check boxes. But I was afraid that they only provided good news. I needed to strike a balance: Keep the makeover simple while providing details about where the agency could do even better.

I created deviation bars to show the size of the difference from one fiscal year to the next. I intentionally re-ordered the survey questions yet again. Within each category, the survey questions are ordered by the magnitude of their improvements.

At a glance, you can still see that all but one survey question improved. But now, you can also see how much or little improvement took place.

It’s good to provide leaders with good news, but it’s better to provide leaders with balanced news.

Now, they can still celebrate all the areas where they improved. Then, it’s time to roll up their sleeves and get to work on improving even more.W

Bonus: Download the Materials

Want to see how I created these five makeovers? They all live inside Excel!

That’s right, I typed the survey questions into Excel, created all of the visuals within Excel, and then PDF’d my screen so that I could share the handouts with leaders.

Purchase the files to learn more and to adapt the templates for your own work.

Learn More

You’ll learn all the step-by-step skills inside Dashboard Design.

The post Visualizing Your Annual Survey Results: Four Makeovers That Didn’t Work, and the Fifth That Did appeared first on Depict Data Studio.

The Project: Need to Display Demographic Data about Survey Respondents

A little context: I recently spoke to graduate students about visualizing their survey data. A group of students had surveyed an art museum’s visitors to gather feedback about their experiences at the museum.

I’m pretending that I’m going to meet with the museum’s higher-ups and share data in a slideshow.

I’d begin with a single slide about the survey respondents. Same thing for a report: Limit your background information to just a page.

In the vast majority of cases, I suggest that you only present one graph per slide. Otherwise, the slide gets cramped and the audience is reading Graphs B, C, and D while you’re still droning on about Graph A. But, nobody really cares about demographic data. I mean, they want to know how many people responded. And they want to know whether the small sample was generally a good representation of everyone else as a whole. Reassure your audience that you gathered a nice big number of surveys and that you had a balanced mix of people, and then move on. Don’t drone on for four separate slides.

Your speaking points would be brief, too: “We surveyed 60 people. 50 percent were first-time visitors and 30% were Harvard affiliates. Most of the visitors were from Massachusetts, but we did have a bunch of other states and countries represented, like Canada and even China! And the people were surveyed tended to be on the younger side; 39 percent were in that 18 to 29 year old bracket.”

The content is fine as-is. We just need to tweak the visuals.

Step 1. Instead of a List, Experiment with a Two-Column or Three-Column Layout

First, break out of the regular ol’ one-column layout and experiment with two-column layouts. As you’re rearranging your key phrases on the slide, don’t forget to delete the bullet points altogether. Those little circles are chartjunk.

Step 2. Add One Visual Per Key Point

Then, add one visual per key point. I used two-slice donut charts to show that 50 percent of survey respondents were first-time visitors to a museum and 30 percent were connected with the nearby university. I used a map to emphasize how most of the survey respondents were from Massachusetts. I used a histogram to show the distribution of age ranges of survey respondents. All of the visuals have been decluttered. In other words, there aren’t any unnecessary lines, borders, tick marks, or legends on the graphs.

Step 3. Apply a Text Hierarchy

Size your text intentionally to guide viewers through the content. The most important information should be large, bold, and dark. The least important information should be small, but not too small. I suggest using at least size 18 for the less-important info on slides, like the “18-29” age bracket labels.

You’ll use your audience’s font to enhance their branding (not my font).

Step 4. Re-Write Your Slide’s Title

Do something, anything, I’m begging you, to translate that jargony slide title for non-technical audiences.

Step 5. Apply Intentional Color-Coding

Finally, color-code by category. Intentional color-coding breaks up your dense visuals and text into a few simple categories.

You’ll use your audience’s color palette to enhance their branding (not my colors).

Bonus! Watch a Video Tutorial

And better yet, watch a sample lesson from my online course, Great Graphs: Transform Spreadsheets into Stories with Better Data Visualization so that you can see the entire step-by-step transformation. Most lessons, like this one, are just a few minutes long. I know you’ve got other things going on. Learn a new technique while you eat lunch, and build your new skills steadily. Enjoy!

The post How to Visualize Demographic Data: From Boring Bullet Points into Great Graphs appeared first on Depict Data Studio.

After my sessions, the clients who sponsored the workshop ask their participants to rate my session. Sometimes the satisfaction surveys are electronic. Other times, the satisfaction surveys are conducted with good ol’ paper and pencil. The paper surveys get scanned and a software program automatically compiles the results.

Survey software tools have come a long way. During college, I spent ten soul-crushing hours each week entering data from paper surveys into SPSS in exchange for course credit. I paid the university for the privilege of entering data! (And yes, I learned a ton along the way, and still use those statistics and research methods skills daily.) When I was a research assistant, the survey scanning tools hadn’t been invented yet. Nowadays, research assistants probably have more meaningful tasks than data entry. It’s a different world.

I’m grateful for this automated reporting technology. But, at the same time, survey scanning tools drive me crazy. Their designs still live in the dark ages—it’s 2018 software with a 1998-era knowledge of graphic design and brain science.

Before

Here’s the most recent survey report that I received. The four-page report comes from a Dashboard Design workshop that I led at a conference last fall.

This software program’s design drives me crazy because:

• The title—Integrated Item Analysis Report—is a mouthful. Yes, I know what an item analysis is. I took an entire graduate course about psychometrics. There were dozens of workshop instructors at this conference. We came from a variety of academic backgrounds. Let’s not assume that everyone knows what an item analysis is.
• It’s dense. There are borders and outlines around everything. There are even double outlines around the open-ended comments. There’s almost no white space. The information is straightforward—here’s how people responded to each survey question—but the dense design makes it feel more complicated than it is.
• The colors and fonts are lacking a soul. The conference had its own logo, fonts, and colors. The organization that sponsored the conference had its own logo, fonts, and colors. Let’s breathe some life and identity into this report.
• So many decimal places. I would never change my workshop approach because a tool told me that 88.89% of people answered a certain way. That’s 89%. I’m the audience for this report. I know exactly what’s useful for me and what isn’t. Decimals won’t change my life.

Let’s revamp this report. Tiny edits will make a big difference. This is the step-by-step process that I teach in all of my Dashboard Design workshops, and today I’m sharing the process with you, too.

Step 1: Start with a Table

Tables are the heart of every quantitative report. We tabulate our numbers and build visuals from those tables. I re-typed the survey questions and the responses into my spreadsheet.

Make sure that you declutter your table by removing unnecessary ink. Tables rarely need all the borders, horizontal lines, and vertical lines that we’re accustomed to seeing. Sometimes I add a few horizontal lines back at the very end of the design process.

I ignored the mean values for each of the survey questions. Agree/disagree scales are ordinal. We can only calculate means for interval or ratio variables.

I also ignored the Neither Agree Or Disagree category. I’m the audience for this report. I won’t change my workshop content based on a middle category. Here’s how I read this report: “Did anyone hate the workshop? Are there disagree responses? Darn, one person disagreed with every statement. Curmudgeon Effect? A handful of people will be dissatisfied no matter what I do. Maybe that person was having a bad day. Or, maybe they accidentally checked the strongly disagree boxes instead of the strongly agree boxes? I’ll never know for sure. Okay, let’s move on. Did anyone love the workshop? Are there any strongly agree responses? Phew, that’s almost everyone. So now I need to compare the agree and the strongly agree responses. What was holding someone back from checking the strongly agree box? How can I go from good to great?” In most projects, participants tend to be satisfied, so we’re usually comparing the top two choices. The middle categories rarely matter.

Step 2: Add Visuals

There are several ways to visualize agree/disagree scales, like stacked bar charts, diverging stacked bar charts, or even waffle charts. The existing bar charts would be easiest to automate across dozens of workshop evaluation surveys, so we’ll keep them.

This example contains miniature within-cell bar charts called data bars. You can make data bars in good ol’ Excel with just a few clicks.

Step 3: Write a Title and Subtitle

One of the most common mistakes that I see among aspiring data visualizers is a lack of text. People get really excited about data visualization. They start churning out more (and better!) visuals than ever before. Sometimes they forget that text still plays an important role. At the top of your report, add plain language that introduces your viewers to what you’re about to show them.

For the title, I changed “Integrated Item Analysis Report” to “Dashboard Design with Ann K. Emery.” At this particular conference, there were dozens of workshops and dozens of evaluation survey reports. The title’s job is to distinguish one report from another. The contents of the report are an item analysis (a question-by-question analysis). But the title of the report needs to contain the workshop’s name and the instructor’s name.

For the subtitle, I wrote, “This report shows the results from the Dashboard Design workshop evaluation survey. If you have questions about this report, please contact So-in-So.” I typically keep my dashboard subtitles to two sentences. The first sentence tells you what you’re about to learn. The second sentence tells you who to contact if you want to learn more.

Step 4: Apply a Text Hierarchy

A text hierarchy tells your viewers which text is at the top of the food chain. The title should be large, dark, and bold so that it instantly grabs your viewers’ attention. You could also apply ALL CAPS to the title or section headers. Use ALL CAPS sparingly, please. It takes longer for our brains to read ALL CAPS than Sentence case or Title Case. We like having a mix of tall and short letters.

This is especially true for people with learning disabilities.

I also made each of the survey questions bold. Later on, I tweaked the font sizes and colors again. The idea is the same. The important information needs to stand out in large, dark, and bold text.

Step 5: Brand with Customs Colors and Fonts

Another common mistake that I see among aspiring data visualizers is when people think that adding their logo will sufficiently brand their document. Sure, you can add your logo to your report.

Just make sure to place your logo in the lower corner—not in the top—so that it doesn’t distract from the report’s contents. In addition to using logos, your fonts and can reinforce your brand.

For fonts, I’m using a combination of Lato Heavy and Lato Light. You would use your own fonts.

You would also use your own colors, not mine. Learn how to read your organization’s style guide, locate your color codes with an eyedropper, or locate your color codes with Microsoft Paint. Then, enter your color codes in Excel or in Tableau.

Step 6: Re-Arrange Until Everything Fits on the Page

This step is more of an art than a science…

I opted for a landscape layout instead of portrait layout. I reserve portrait layout for materials that are going to be printed. Are workshop facilitators printing out documents like this? I doubt it. I won’t. Landscape layout is best for documents that are going to be read on-screen because our computer monitors are already landscape-shaped. You open the document and it just fits so nicely.

No wasted space around the margins.

I adjusted row heights and column widths to get the page breaks just right.

I merged a few cells. When you’re working in spreadsheets, don’t merge cells together too early!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! You’ll kick yourself. Unmerging cells is drudgery.

I added page numbers. My report was designed entirely within Excel, but it would get shared with others as a PDF. Page numbers make my “spreadsheet” feel more like a regular document.

I added horizontal lines so that readers could across the rows more easily. The lines are light gray, not black. They don’t distract from the more important information.

And speaking of lines… I removed the double outlines (!!!) around the open-ended responses. This is one of my favorite parts of the makeover. Now, we can actually see the comments because they aren’t competing with the outlines for attention.

I color-coded the report by category. The introductory section is purple, the closed-ended questions are blue, and the open-ended questions are turquoise. I wanted readers to know when a new topic was starting.

Finally, for bonus points, I added icons. Michelle Borkin and her team found that icons make graphs more memorable, so I add icons whenever I can. I’ll teach you how to add icons in a future post.

Here’s the full makeover. I hope you like it.

Within minutes, we’ve provided workshop facilitators with useful information without burning their eyeballs.

Learn More

Want to learn the technical how-to’s in Microsoft Excel?

Want to see additional before/after data visualization makeovers?

All these skills are covered inside Dashboard Design.

Purchase the Template

Want to explore my survey report makeover in more detail? See how I arranged the text, graphs, and icons. Or, get ideas from the template to use in your own project. You can purchase the template below.

Purchase the Dashboard Template

The post How to Visualize Workshop Evaluation Results appeared first on Depict Data Studio.