Make an impact with your research data! Focusing on the guiding principles of presenting data in evidence-based ways so that audiences are effectively engaged and researchers are better understood, Presenting Data Effectively offers the best communication strategies available to those working with data. With this accessible step-by-step guide, anyone--from students develMake an impact with your research data! Focusing on the guiding principles of presenting data in evidence-based ways so that audiences are effectively engaged and researchers are better understood, Presenting Data Effectively offers the best communication strategies available to those working with data. With this accessible step-by-step guide, anyone--from students developing a research poster for a school project to faculty and researchers presenting data at a conference--can learn how to present and communicate their research findings in more interesting and effective ways.Author Stephanie Evergreen draws on her extensive experience in the study of research reporting, interdisciplinary evaluation, and data visualization, as well as from diverse interdisciplinary fields, including cognitive psychology, communications, and graphic design, to extract tangible and practical data-reporting communication lessons and insights. She then demonstrates how to apply those principles to the design of data presentations to make it easier for the audience to understand, remember, and use the data....
|Title||:||Presenting Data Effectively: Communicating Your Findings for Maximum Impact|
|Number of Pages||:||183 Pages|
|Status||:||Available For Download|
|Last checked||:||21 Minutes ago!|
Presenting Data Effectively: Communicating Your Findings for Maximum Impact Reviews
This book is an extremely useful tool for learning how to present data visually. It is easy to read with lots of great examples -- not surprising for a book dedicated to effective communication!
I was expecting a book about dataviz specifically, but this isn't it.Here "presenting data" is meant in the sense of document design: putting together reports, slideshows, and posters about your work. How to make the title page look professional? Where do you find images licensed for free use? What combinations of fonts work well for headings vs body text? and so on.There is some dataviz advice, but it's haphazard, and I disagree with plenty of it.Evergreen seems to have plenty of solid advice on putting together reports. But since her dataviz advice is often wrong, I can't really trust her on the other matters either.So I'm glad I skimmed this for the useful tips & resources I did find... But I cannot recommend it as a general resource to someone learning about dataviz (not sure about document design).Good bits:* p.12: "Although working memory has limits on its cognitive load, graphic elements can reduce the overload by doing some of the thinking for the reader. By visually organizing and emphasizing information, graphic design makes it more accessible for the reader, increasing the capacity to engage with the words and data. By virtue of this process, richer chunks of information are actually created, which in turn enables the viewer to essentially handle a larger cognitive load at one time."* p.18: Apparently the International Institute for Information Design site has helpful white papers about info design.* p.44: If you use Advanced Search in Google Images, you can filter down by copyright status too, to help find images that are OK to reuse.* The APA Publication Manual apparently has helpful dataviz standards?* p.66: I didn't know how to find your fonts in Windows. Go to C:\Windows\Fonts and you can see the intended usage category for each font (text, display, decorative, etc.)* p.88: Free font websites Fontpark and Font Squirrel; and font pairing advice here and here; and an experiment on font trustworthiness* p.89: Great book title: Type & Layout: Are You Communicating or Just Making Pretty Shapes? :)* p.98: I didn't know about Adobe Kuler, a free color picker website* p.116: Not all her dataviz advice is bad: this is a good example of using diverging color schemes for a Likert scale.Tips for next time I teach dataviz:* I like her terms "unintentional" and "sloppy." Better than my own vague explanations to students of why alignment should be perfect ("it looks almost aligned but not quite"), just say "You don't want it to look sloppy."* Show students examples of font substitution in different formats and on different machines. This is why we use PDFs and embed fonts when possible, rather than writing Word docs whose layout can get completely thrown off by using a font unavailable on the reader's machine.* Talk with students about line length: how many words or characters to fit in a block of text before making a line break? (Apparently 8-12 words, or 50-80 characters.) Useful when deciding where to put text boxes, how to shape them, how many columns to use, etc.* "Squish and separate" is graphic designers' catchier slang for the Gestalt proximity principle.* Style sheets are useful. I should review the ones I got from various newspapers when I took Alberto Cairo's dataviz MOOC.Huh?* p.67: Do serif fonts really look that bad when projected in slideshows? Check for myself: do my Beamer slides use serifs or sans fonts?* p.72: She suggests choosing one word in the title to stand out in a decorative font (like an oldey-timey font for the word History in "KCC History Department"). I don't see the point, and it looks unprofessional to my eyes.* p.85: What exactly is wrong with bullets?* p.131: You seem to tell me to avoid putting things in the lower-left corner, because of this Gutenberg Diagram thing. But then your next example claims that putting things in that corner is a great example of the Gutenberg advice. What?Major gripes:* p.51: No, starting bars above 0 is not "cheating a little bit"---it defeats the whole point of using bars, which is that their lengths are comparable. If you want to zoom in (and not show 0), just use dots instead of bars. Easy. And if you don't understand this, you shouldn't be in the business of doling out dataviz advice.* p.53: Weird conflicting advice: Don't use 3D because you can't read it easily against the gridlines... Instead, switch to 2D---but also remove the gridlines so that they can't be read at all...? Well, I agree 3D is bad, but this is not a coherent way to argue your point.* p.85: Oh, now you *do* include 0s, in a scatterplot where all points are far from 0? Why? And why not include a legend for the colors & shapes of these points?* p.105: Color-blindness is important. But it doesn't help readers to "illustrate" it with awful fuzzy greyscale versions of your images when your book is printed in black-and-white.* p.110 and many other places: "Go online to this book's website to see this image in color and then keep reading." Nope! That's not how reading a book works. Oh man, and also the text description of colors in her images doesn't match the images at all. Why am I still reading this?* p.112: AHA, now I get it! She does not care about effective data visualization. She is an infographic designer at heart: look at my giant, colorful "8%" without any context for whether that number higher/lower than average, or the past, or our targets, or anything. This is not about communicating data, just purely about making things "pretty."
A clear, easy-to-follow guide with practical advice on the presentation of information. Data-based reports and presentations can be useful and engaging!
I won this as a giveaway. What a disappointment. I was looking forward to enhancing my data presentation skills and I gained very little. My biggest complaint was that there were more "I"s in this book than in some autobiographies I have read. The other major issue with this book is that the author seems to think that charts that show nothing but pictures and very little data are useful. Most of her examples are incomprehensible with just a jumble of unlabled numbers and a huge picture which, in my experience, won't help anyone. One bright spot in this was that she does provide many useful resources that will be of practical help.
For a long time, I wrote off data reporting/charts as an annoying part of my work. I'd rather spend my time analyzing data and finding things that could move my organization forward. This book has convinced me that quality presentation is incredibly important for making your work stand out, especially to the lay audience. What convinced me? Evergreen backs up her rationale for design with cognitive science and research into graphic design. Whether you're putting together a report, a presentation, or a poster, you can make deliberate choices in the design that can help make your work easier for your audience to understand because that's the way their brain works. I think my main reason for hating formatting/presentation is that I didn't have a system. Evergreen lowers the bar for entry into proper design by explaining why you should make certain choices around font, color, alignment, and more. If you flip your viewpoint from, "this report will look pretty" to "the information will be presented in a way that focuses reader cognition on my message rather than my formatting", then this work feels pretty compelling. If you hate presenting data or simply need a guide on how to do it well, this is a book for you.
A great small easy to read and visual book which differentiates very helpfully between Reports, Slideshows, Posters, Data Displays, and Online data. The visuals are always relevant and the chapter summaries are invaluable; the further links are brilliant and the actual suggestions of trying it out are very relevant. Brilliant book for beginners who need an overview of design principles focussed on presenting data
Easy to understand, relevant to every undergraduate, and with direct applications to the software most of us use: Microsoft Office
Great data visualization resource. Goes beyond just charts and graphs to overall report formats, etc. A great addition to my data geek bookshelf.
Not really about presenting data, but a good document and deck design book anyway.