To 3D, or not to 3D, that is the question…

Visualizing data using a 3D chart, graph, or pie doesn’t make it unique or more eye catching. Actually, it has the potential to confuse people by distracting them from the story you’re trying to tell with your visualization. I’ve had to confront several 3D charts over the past couple weeks…my question is how does seeing the side of a bar help me compare it to the bar next to it? Typically the further down the chart list in Excel you go…the more convoluted things get. Kind of conflicting, I think — it would make more sense that the further you go, the more sexy and complex you get (well, the latter is probably true).

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Instead of falling victim to the temptations of Excel (or whatever software you use), think smarter. Easy for me to say, right? Okay, here’s how I recommend you begin to change that:

  1. Don’t always make decisions based on your gut. Remember how you ended up on that 3D pie chart? Yep, thought so. Instead, consider Gestalt Principles…does your visualization make sense based on these principles?
  2. Stepping away helps! Review several days after and determine what your initial interpretation of the chart is.
  3. Critical feedback. Have someone unfamiliar with the project interpret your chart. Even better, get preliminary feedback from your stakeholder. You’ll be surprised what you learn.

The reality is that sometimes the wrong visualization is chosen, but we can do simple things to reduce how often it happens. Similar to a a good report or presentation, good visualizations require stepping away, getting outside feedback, and using research based methods. 

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But why take my word for it?

3D charts: David Sprague (Dashboard Insight) gives some great reasoning behind avoiding “faux 3D charts,” which I think everyone should take a gander at.

Gestalt Principles: I think Steven Bradly (Smashing Magazine) has a nice writeup on design principles.

Find the right chart: I’ve got ideas (go me), and so do a bunch of other people. Trying Googling “What charts should I use for…,” and I bet you will find some ideas.

A new week means tips for less-painful presentations.

Presentations. In addition to delivering findings via charts, graphs, and pictures, the presentation itself is a visualization. Data visualization = taking a lot of info (data) and turning it into something palatable. It isn’t enough to have results or recommendations that people should want to listen to. That alone isn’t going to make your audience pay attention — maybe just keep them awake.

Admit it: you’ve been subjected to a presentation where the PowerPoint (or whatever visual aide used) depreciated the value of what you were supposed to be learning about. Instead of focusing on the person talking, all you could think about was the giant chunk of text, which was nearly illegible from your uncomfortable, squeaky seat (you knew it was squeaky because you had to keep moving around to stay awake).

Although I’m sure we have all delivered one of those presentations, that doesn’t lend much comfort. So instead of letting you accept the status quo, I’m going to share some of the tips that have changed my presentations for the better!

First, let’s look at what I was guilty of doing in my presentations…and still see a lot of.

Presentation Blog.jpg

My tips + sound dataviz + a knowledgable, energetic, and fun presenter = a presentation people might listen to!

  1. Instead of using stupid headers…use that space to introduce your topic. Similar to useless titles on charts and graphs, we have a tendency to state obvious crap (or assume something is obvious). I like throwing in some rhetorical questions for emphasis.
  2. Defaults. Stop using them. Similar to my post on creating better charts, staying away from the crappy canned themes, fonts, and colors in PowerPoint (or whatever software you use) is best.
  3. Use pictures…but only if they mean something. All too often a picture is forced. Connect your content to a meaningful image to capitalize on the picture superiority effect.
  4. Use your PowerPoint as a PROMPT — not a script.
    1. More slides > a book per slide.
  5. Stop using bullet points because PowerPoint tells you to. First, ask yourself if bullet points make sense. If not, you’re wasting space.
  6. Black on white text = easiest to read. I know, I know…PowerPoint has been misleading you for years. But now you know better.

If you try out these tips, you might end up with less slides and more conversation pieces — like the one below. Presentation Blog_2.jpg

What tips do you have for presenters? Feel free to share them!

Is your qualitative dataviz taking a backseat? A few extra minutes = rich data noticed!

Created by Chris Lysy
STOP depreciating your qualitative data by putting it into an appendix, or having six pages worth of themes, definitions, and examples. That’s rich information that you need to bring your stakeholders’ attention to! Like any data visualization, you want to draw readers in and make pile of data more digestible. Qualitative data might be dense but it’s no different.

So what is something easy I’ve started doing? Adding icons. Icons are a super easy way to tell your readers that the qualitative data confirmed something…or it didn’t. Or maybe it did — but only a little bit! Either in Excel (depending on how you build your qualitative tables) or Word, start inserting icons/images/GIFs (okay, maybe that’s a stretch) to indicate if a program outcome was achieved according to qualitative feedback. See my loaded and very fake example below.

First, I choose some icons (Excel or Word: Insert > Symbol or Image). Just like the charts you use to visualize quant, the icons should make sense. A giraffe or poo emoji might not be what you’re looking for (or, if it is, what an awesome evaluation).

After you’ve chosen icons, create a legend…because assumptions are dangerous.

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Now, incorporate the icons into your qualitative table. In ones I’ve done, I add it on the left most side — the FIRST place my stakeholders are looking. They can quickly see that the hypothesis was accepted…or not. This makes it easy for them to dive into what they need to read first. For example, your stakeholder might be most concerned that their program did not achieve the desired outcome (and if your survey questions answer your evaluation questions, this will be no problem to connect, right?!).

Here’s a super simple example…that took me all of a few seconds. Something sensical that compliments the dense text will help get qualitative data noticed.

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This doesn’t replace all the other important stuff (e.g., definition, frequency, etc.), but your stakeholders can get a snapshot of the results! 
  1. This is one very simple idea, and I bet you’ve seen some of the awesome resources put forth by Ann K. Emery, and Stephanie Evergreen on visualizing qualitative data. They are great ideas! But even with these awesome ideas, most of the reports I’ve seen in the past few months are still full of indigestible qual…NOT a great compliment to the awesome charts and graphs you’re probably making, right? So, my challenge to you is to start using the great resources available to you — and come up with your own!

Got an applied project? You can build the capacity for data-driven decision making.

During graduate school, students are usually offered applied opportunities. What I love about applied psychology (e.g., evaluation, I-O psychology…) is that graduate students have the chance to bring their knowledge to a variety of industries — and built the value of data driven decision making. To me, that is priceless. Exposing the field of applied psych is great…and so is making others aware of all the great things can be done when something other than anecdotes are the decision making tool of choice.

So what’s my experience with this? I also had these great applied opportunities, and I started to realize that I was an advocate for my field. I had a new perspective about the entire experience — if my client walked away feeling like they wasted their time, I didn’t do a very good job.

My second to last semester I completed a needs assessment and process evaluation for a company in another state. This company is phenomenal — great idea that’s meeting a need, lean bottom line, and an office full of great people. Where’s the but? Well, it’s that data wasn’t driving their strategic planning. Needs assessment? What’s that? There I had it — an opportunity to BUILD the capacity of evaluation in this organization.

In short, the project went well — everyone learned a lot, services were revised, future planning was focused on data. But that’s all while you’re still there, right? In the back of your obsessive applied psych mind…you know this was one project, and the long lived method of luck and “educated” guessing (oxymoron’s make for good blog topics) could be revived and become the preferred decision making tool — again.

Data driven decision making. What does the data tell us? How do the statistics relate to what we are seeing financially? Your clients said they wanted this service…but was it a representative sample?? I felt like a broken record…because I hoped that between demonstration, dialogue, and bringing my client along for the experience would lead to an appreciation and PREFERENCE for data to inform their decisions.

Created by Chris Lysy

Well, two months later I was in a follow up meeting…sitting in on an unrelated project…and I heard it:

“We need to make decisions based on what the data tells us. It can’t be what I like, or what makes sense to me. Let’s use the tools we have to make changes using data.”

…you know that moment when someone says their idea of an awesome day is binge watching Frasier and eating pizza rolls, and you’re like “…that’s hot.” Bam. There it was. The sexy side of being an advocate for our field.

You see, change is hard. Pushing for a better method (that isn’t always easier) can be a challenge. And hey, being a grad student is a special level of hell at times. Sometimes you want to drop the results and peace. You don’t always want to screw with Excel for hours to get something other than a canned report (but seriously, talk to me if it’s taking you hours to craft good viz). But at the end of it, you have an opportunity to see the results put into action. Your very presence is a disruption — a potential catalyst for change. The credibility of our field? It’s on ALL of our shoulders. So, the next time you’re burnt out, remember the potential to impact decision makers and the responsibility to your colleagues (oh, and call on them when you need help!).

Created by Chris Lysy

Perfect datasets don’t exist but cool tools do. Here are two I use.

One component of my day-to-day is retrieving reports and turning them into something useful. Simple, right? Ha! I bet you laughed, too. Data comes in all shapes and sizes…in fact, it usually comes in every form besides the one we need. As data scientists, our job isn’t only to interpret the data and provide someone with the results to make an informed decisions – although that’s great. Visualizations? Those are great, too. But, if you don’t start with the right data, you might as well not analyze or visualize it. …so what?

Well, canned reports suck (unless they’re super customized by your IT team, but who has time for that?!?). When I ask for a data pull, I hate being told “no” or “yes, I’ll get that to you in a few weeks.” Usually when I need a report, it’s because someone important needs to make a decision. So instead of waiting around for someone to do it for me, I put on my data manipulation cap and get ready to finesse some Excel files.

Enough with the back story, right? You need tools! Admittedly, my most common problem is having all the data I need…spread across a bunch of out-of-the-box reports. Easy you say? Sure, if I was only dealing with a few rows of data. We’re talking thousands of rows. So, here are a few tools I’ve come across that have turned my data nightmare into something usable:

  1. Consolidate
  2. Merge Table Wizard for Microsoft Excel (the app with the world’s longest name) – Mac
    1. If you’re a PC user, use ActiveData

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So let’s talk about these tools…

Consolidate is great when you have several entries for one company (or participant) that you need summed, averaged, or a number of other functions. It’s as easy as five steps:

  1. Select your data.
  2. Go to the Data tab.
  3. Choose the Consolidate function.Screen Shot 2017-04-17 at 9.07.41 PM.png
  4. Select your data (be sure to note where the label that you’re consolidating is at).Screen Shot 2017-04-17 at 8.37.35 PM
  5. Choose your function.

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Done! Now you’ve got your data in one place. Screen Shot 2017-04-17 at 9.14.02 PM.pngIn my case, I needed each company’s entry to be consolidated into one entry, and I wanted the sum of all of their orders.
Next, I wanted to tie e-mail addresses to the company. That’s where my fancy application came into play. Here’s a quick walk-through on merging two reports to make one useful report.

  1. Open both reports.
  2. Open Merge Tables Wizard (Mac)
    1. ActiveData (PC)Screen Shot 2017-04-17 at 8.47.35 PM
  3. Select the data you want to merge on (pick the dataset with the majority of the data). This is your primary dataset.
  4. From the other worksheet (or, in my case, a workbook), select the data you want to merge. Screen Shot 2017-04-17 at 8.48.36 PM
  5. Your tool will likely verify that it identified the variable you want to merge on (i.e. company).Screen Shot 2017-04-17 at 8.48.46 PM
  6. The default is to add a new column for any data that does not exist in the primary.
  7. After that, the remaining steps are fluff. Select what you want and run it!

Boom. Screen Shot 2017-04-17 at 8.49.34 PM.pngIn several minutes, you just created a data file with all the data you needed. Of course, this is an oversimplification. But, you can apply it to anything – especially those nasty datasets.

So, what about you? What are your favorite tools for forcing Excel to comply?Screen Shot 2017-04-17 at 9.14.02 PM.png

I stopped blaming Excel for my basic data visualizations. Here’s when and why.

Early last year I realized my research findings only meant something if I involved the right people. And, to do so required me to be a creative and adaptive evaluator. Around the same time, I realized that I could have all the answers, but without a good way to communicate them, no one would actually notice.

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Look how easy it is to be basic!
I started searching for solutions. I found blogs by industry celebs (Ann K. Emery, and Stephanie Evergreen, to name a few) and began to pour over their archives. I found a lot of cool stuff. My world was quickly shaken when I realized that meant APA formatting might not be the best way for my voice to be heard (my condolences to the die hards, there is a time and a place). But, the big takeaway was that I figured out that, “Yes, it sucks because Excel made me do it,” was a crappy excuse for not visualizing my data in a better way. I learned to stop accepting the norm. You see, the purpose of Excel is to be a tool – not to do all my work for me because guess what? I’m the expert! I know those gridlines are garbage (and unnecessary), and that I should really figure out the RGB numbers for my brand.

So when did I stop blaming Excel? When I started valuing my data visualizations as more than entering data, selecting insert, choosing between a column or bar chart, and dragging it into my equally as boring report. When I realized that sweet visualizations in Excel just meant more thought and a few extra steps.

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I stopped accepting default settings!
Need an example? Check out Basic to Bomb Column Charts! Now go…find your inspiration for better visuals!

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Not too bad for a few extra clicks…

Basic to bomb column charts.

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The basic Excel column chart.
Let’s take a quick look at how a basic column chart in Excel can be turned into something you might want to look at.

  1. Enter your data into Excel
  2. Insert a column chart (if you’re basic, save and close here…)

Oh, good! You’re ready for more. Okay, here it goes:

  1. Get rid of that white space by decreasing the gap width.
  2. Remove the gridlines. They add unnecessary clutter for readers
  3. Trash the x-axis.
  4. …the y-axis, too!
  5. Add data labels (and for the love of all things, don’t leave them hanging in space – move them onto their respective bar).
  6. The title. I’m a fan of Stephanie Evergreen’s piece on this. Instead of a simple word or two, write a sentence that describes the dataset and LEFT justify it.
  7. Format! 10pt labels, 12pt title, non-default font and colors.

Bonus: Make everything a table. Sort them — highest to lowest (i.e. descending first), so readers don’t have to do the ordering themselves. 

Questions? Shoot me a message or tweet. 

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Our column chart gone bomb!

Can we talk about logic models?

Over a year ago I had the pleasure of co-presenting on logic models to a group of individuals from non-profit organizations. The presentation included time to discuss the pros and cons of the logic model. This really gave my team and I the ability to work through the problems with the attendees. The most voiced problem?

Continue reading “Can we talk about logic models?”