- Paperback: 264 pages
- Publisher: SAGE Publications, Inc; 1 edition (May 18, 2016)
- Language: English
- ISBN-10: 1506303056
- ISBN-13: 978-1506303055
- Product Dimensions: 7.2 x 0.5 x 9.2 inches
- Shipping Weight: 15.5 ounces
- Customer Reviews: 68 customer ratings
- Amazon Best Sellers Rank: #520,166 in Books (See Top 100 in Books)
Effective Data Visualization: The Right Chart for the Right Data 1st Edition
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-- David Boyns
Very approachable writing style, clear examples and instructions make this a "must-have" for anyone who has to present data. -- Thomas Cappaert
I love this book. It opened me up to so many possibilities that I didn’t know existed in Excel. The author really helps you build these skills though thoughtful exercises. She uses her real-world experience to open the "black-box" behind graphing techniques. I can’t wait to use these skills for my next batch of research projects. The competition at the professional conferences will be amazed by our ninja skills. -- John O. Elliott
This run-to-read and easy-to-use book can boost your visual presentation by making it right to the point! -- Shun-Yung Kevin Wang
Effective Data Visualization sets a new standard for the practical presentation data using Excel. It provides impressive graphics and hands on details on when and how to present them to various audiences. Any instructor who works with students seeking an advanced professional degree should consider adopting this text.-- Brian Frederick
This book is an excellent guide for creating innovative and intentional graphics that can frequently speak for themselves. Stephanie not only shows you how to create visually appealing charts and graphics, but she also explains why it matters.-- Mindy Hightower King
About the Author
Dr. Stephanie D. H. Evergreen is a sought-after speaker, designer, and researcher. She is best known for bringing a research-based approach to helping others better communicate their work through more effective graphs, slides, and reports. She holds a PhD from Western Michigan University in interdisciplinary research, which included a dissertation on the extent of graphic design use in written data reporting. Dr. Evergreen has trained audiences worldwide through keynote presentations and workshops for clients, such as Verizon, Head Start, American Institutes for Research, Brookings Institute, the Ad Council, Boys and Girls Club of America, and the United Nations. She led the first known attempt to revamp the quality of presentations for an entire association: the Potent Presentations Initiative for the American Evaluation Association (AEA). She is the 2015 recipient of the AEA’s Marcia Guttentag Promising New Evaluator Award, which recognizes early notable and substantial accomplishments in the field. Dr. Evergreen is coeditor and coauthor of two issues of New Directions for Evaluation on data visualization. She writes a popular blog on data presentation at StephanieEvergreen.com. Her book, Presenting Data Effectively: Communicating Your Findings for Maximum Impact, was published by Sage in Fall 2013 and was listed as number one in Social Science Research on Amazon in the United States and United Kingdom for several weeks.
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her own consulting firm. This book on data presentation, her second, is
focused on elementary displays of data using MS Office. It joins an
increasingly crowded market and does not stand up well given some
If you have access to a copy and skim through, you may find examples of
unfamiliar graph types that could be useful. That's the most likely
positive use of this book I can imagine. In particular, I agree that
what the author calls 'dot plots' (Cleveland dot charts is another name)
are often very helpful and should be used more frequently. Similarly,
'slope graphs', an old idea under Tufte's new name, can be very good for
showing paired changes as labeled line segments.
The book mentions 11 technical reviewers, all from universities. I have
to wonder whether they read the book, or perhaps more likely, how they
feel about being named if their advice was ignored. Technically, this
book is unreliable, and shows much evidence of the author's inexperience
Evergreen is negative about scatter plots and lays that on repeatedly:
they are a 'complicated visual' (p.161), can be 'confusing to interpret'
(p.165), and are suited for '[m]ore sophisticated groups' who have taken
an 'advanced statistics course' (p.172). I come from a different
educational system, evidently, in which scatter plots appear early in
children's education and are a staple of every introductory statistics
course. The author's main scatter plot example shows % of people of
color in various areas of New York City (y axis) versus number of
military recruits per 100,000 residents (x axis). She repeatedly brushes
off the standard point that the axes would be better reversed (a point
also poorly handled in her first book). This isn't just a cosmetic
choice: which variable is outcome (response) is crucial to thinking
about such problems. Evergreen also fails to comment that the pattern of
scatter is strongly nonlinear, so the straight line fitted is absurd for
that reason, and indeed on other grounds too. Evidently, the author
needed simpler examples that she could handle confidently.
Similarly, Evergreen dislikes histograms which are 'not the sexiest'
(p.140) and 'feel clunky to me' (p.145). These aren't serious comments,
for all that she does continue with examples.
The author is evidently no programmer (that's a comment, not a
criticism) and focuses on how to get there step by step in MS Office
(meaning MS Excel, mostly) using your mouse. Those sceptical of the
merit of doing graphics at all within Excel will find their opinions
confirmed by the dodges and fudges needed even to do some very simple
things. There are some unsystematic comments on using macros instead,
which are mostly a distraction. There is little mention of the many
internet resources available from more expert Excel users. There is
some token R code on the author's website, which looks like a waste of
effort given the mass of outstanding code already available.
There are other technical mistakes that should have been caught by
reviewers. There is reference to debate on whether bubble charts should
be sized by their diameter, radius or circumference (p.17), but there is
no such debate. Any issue is over using areas or lengths, as any length
basis is equivalent to any other. 'Upper confidence interval' (p.25) is
a slip for 'upper confidence limit'. Cluster analysis is not defined
(even vaguely) by combining quantitative and qualitative data (p.183),
although that is one of its applications.
Evergreen broaches the difficulty of paraphrasing technical terms for
non-technical audiences, but some of her own suggestions fall far short
of acceptable. 'Statistically, there's a chance the actual score falls
in this range' is offered (p.26) as an explanation of confidence
interval: that explanation is not just vague, it's completely vacuous.
As other reviews note, no colors are used here except blue and grey. A
side-effect of that, which is not quite inevitable, is that many graphs
here depend heavily on minor differences between shades of those colors.
It is hard to like this book if, as I did, you find its written style
slangy and immature. Sample vocabulary includes amazing, awesome, baby,
cool, cute, freak, geek (often), heaps, hush-hush, incredibly, kid,
kinda, loads, nerd (often), ninja (oh so often), pow, rad, rock star
(ditto), sorta, stellar, Super Academic, super bad, super cool, super
critical, super famous, super hero, super honest, super interested,
super weird, um, whew, wimpy, wow, yay, yeah, yep, yikes, and yowza.
(Also, if it's news: using MS Excel for presentation graphics doesn't
make you a geek.)
Combined with that style come careless editing and proof-reading. There
is repeated confusion between it's and its and bizarre typos on the
graphs themselves, such as camradarie, kindney, ful (for 'flu') and
individulized. There is also confusion between i.e. and e.g., affect and
effect, axis and axes, rectangle and oblong, and disbursed and
dispersed. Names of key figures in the field (Jorge Cameos, Mike Bostok)
are mangled. No editor should have let through `the bottom of Indiana'
and `the top of Kentucky' (p.229): the words being reached for might be
southern and northern, or something more precise.
In total, writing and editing were poorly done. I evidently don't know
how blame is to be shared between the author and the publisher.
I do find Sage's technical books a very mixed bunch.
Berinato: Good Charts: The HBR Guide to Making Smarter, More Persuasive
Cairo: The Truthful Art: Data, Charts, and Maps for Communication.
Camões: Data at Work: Best Practices for Creating Effective Charts and
Information Graphics in Microsoft Excel.
Knaflic: Storytelling with Data: A Data Visualization Guide for Business
Robbins: Creating More Effective Graphs.
Camões is best of these if you need an Excel-based book. Robbins has the
greatest depth of analysis.
I could barely work Excel well enough to do basic default charts back then, now Stephanie Evergreen has helped me develop a niche in my research group and she can help you too. Skeptical? Check out the photos I've attached on how we used to show data in charts and how we do it now.
Bottom line: Buy it!
Actually, I think that the principal thing that it does is to get YOUR head / mind / thinking RIGHT -- & to SEE / FIGURE OUT what SHOULD (REALLY) be shown in the situation -- with the data available...
(Only) THEN, do the (amazing) ninja techniques apply -- because, at the CORE, Excel (graphs) are just a 'tool' -- which, ONLY follow the 'commands' of the 'interpreter'... Excel is both 'smart' -- & 'DUMB' -- at once -- but ONLY come to LIFE in the hands / head of a SKILLED 'interpreter' / user... Can't replace (or create) INTELLIGENCE -- & a WELL-THOUGHT -- THROUGH -- message / point...