Tag Archives: Program Design

August 4, 2014 · 6:22 am

Curb Your “Malthusiasm”—How Evaluation Can Contribute to A Sustainable and Equitable Future

The theme of the upcoming 2014 annual conference of the American Evaluation Association (AEA) challenges participants to consider how evaluation can contribute to a sustainable and equitable future. It’s a fantastic challenge, one that cuts to the core of why evaluation matters—its potential to promote the public good locally and globally, today and in the future.

As I prepare my presentations, I want to share some of my thoughts and encourage others to take up the challenge.

The End is Nigh(ish)

The natural and social environments in which we live have limits. Exceed them, and society puts itself at risk.

It’s a simple idea, but one that did not enter the public’s thinking until Thomas Malthus wrote about it in the late 18^th century. He famously predicted that, unless something changed, the British population would soon grow too large to feed itself. As it turns out, something did change—among other things, merchants imported food—and the crisis never came to pass.

Today, Malthus is strongly—and unjustly—associated with, as Lauren F. Landsburg put it, “a pessimistic prediction of the lock-step demise of a humanity doomed to starvation via overpopulation.” This jolly point of view is sometimes referred to as Malthusianism, and applied to all forms of catastrophic environmental and social decline.

The underlying concept Malthus articulated—there are real environmental and societal limits, and real consequences for exceeding them—is not controversial. There are, however, controversial perspectives related to it, including:

“Malthusiasm”: A passionate belief in—bordering on enthusiasm for—the inevitability of environmental and social collapse, especially in the short term.
Denialism: An equally passionate belief that predictions of environmental and social disaster, like those made by Malthus, never come to pass.
Self-correctionism: A belief that many small, undirected changes in individual and organizational behavior, related primarily to markets and other social structures, will naturally correct for problems in complex ways that may, at first, be difficult to notice.
Intentionalism: A belief that intentional action at the individual, organizational, and social levels—when well planned, executed, and evaluated—can not only help avoid disaster, but produce positive benefits that serve the public good.

I reject the first two. I hope for the third. I’ve spent my life working for the fourth—and this is where evaluation can play a significant role.

From Avoiding Disaster to Promoting Sustainability

I am as much for avoiding disaster as the next guy, but—rightly or wrongly—I expect more from organized human action. Like sustainability. It’s a concept that I and others strongly believe should guide the actions of every organization. It is also a slippery concept that we have not fully defined, making it a rough guide, at best.

So, connecting ideas from various sources (and a few of my own), I’ve developed a preliminary working definition based on a set of underlying principles (in parentheses):

Actions are sustainable when they do not affect future generations adversely (futurity), social groups differentially (equity), larger social and natural systems destructively (globality), or their own objectives negatively (complexity).

I’m not fully satisfied with the definition, but so far it has helped clarify my thinking.

Why Evaluation Matters

Unfortunately, action is only weakly linked to upholding these principles, in part because there is often a lack of information about how well the principles have been (or will be) met.

That is where evaluation comes in. If we use our skills to help design the actions of commercial and social enterprises in ways that uphold these principles, we serve the public good. If we evaluate programs in ways that shed light on these principles—which would require most of us to expand our field of view—we also serve the public good.

This is why evaluation matters—because it has the potential to serve the public good—and why we need to work together to make it matter more. That would truly be evaluation for a sustainable and equitable future.

3 Comments

Filed under AEA Conference, Conference Blog, Evaluation, Program Design, Program Evaluation

Tagged as AEA Conference, American Evaluation Association, complexity, evaluation, Malthus, Program Design, Program Evaluation

October 16, 2013 · 4:06 am

Confessions of a Conference Junkie

It’s true—I am addicted to conferences. While I read about evaluation, write about evaluation, and do evaluations in my day-to-day professional life, it’s not enough. To truly connect to the field and its swelling ranks of practitioners, researchers, and supporters, I need to attend conferences. Compulsively. Enthusiastically. Constantly.

Over the past few months, I was honored to be the keynote speaker at the Canadian Evaluation Society conference in Toronto and the Danish Evaluation Society in Kolding. Over the past two years I have been from Helsinki to Honolulu to speak, present, and give workshops. The figure below shows some of that travel (conferences indicated with darker circles, upcoming travel with dashed lines).

But today is special—it’s the first day of the American Evaluation Association conference in Washington, DC. If conferences were cities, this one would be New York—big, vibrant, and international.

And this year, in addition to my presentations, receptions, and workshops (here and here), I will attempt to do something I have never done before—blog from the conference.

EvalBlog has been quiet this summer. Time to make a little digital noise.

Conference Blog: The American Evaluation Association Conference About to Kickoff

It’s been a busy few months for me. I have been leading workshops, making presentations, attending conferences, and working in Honolulu, Helsinki, London, Tallinn (Estonia), and Claremont. I met some amazing people and learned a great deal about how evaluation is being practiced around the world. More about this in later posts.

This morning, I am in Minneapolis for the Annual Conference of the American Evaluation Association, which begins today. While I am here, I will be reporting on the latest trends, techniques, and opportunities in evaluation.

Today will be interesting. I lead a half-day workshop on program design with Stewart Donaldson. Then I chair a panel discussion on the future of evaluation (a topic that, to my surprise, has mushroomed from a previous EvalBlog post into a number of conference presentations and a website).

Off to the conference–more later.

Conference Blog: Catapult Labs 2012

Did you miss the Catapult Labs conference on May 19? Then you missed something extraordinary.

But don’t worry, you can get the recap here.

The event was sponsored by Catapult Design, a nonprofit firm in San Francisco that uses the process and products of design to alleviate poverty in marginalized communities. Their work spans the worlds of development, mechanical engineering, ethnography, product design, and evaluation.

That is really, really cool.

I find them remarkable and their approach refreshing. Even more so because they are not alone. The conference was very well attended by diverse professionals—from government, the nonprofit sector, the for-profit sector, and design—all doing similar work.

The day was divided into three sets of three concurrent sessions, each presented as hands-on labs. So, sadly, I could attend only one third of what was on offer. My apologies to those who presented and are not included here.

I started the day by attending Democratizing Design: Co-creating With Your Users presented by Catapult’s Heather Fleming. It provided an overview of techniques designers use to include stakeholders in the design process.

Evaluators go to great lengths to include stakeholders. We have broad, well-established approaches such as empowerment evaluation and participatory evaluation. But the techniques designers use are largely unknown to evaluators. I believe there is a great deal we can learn from designers in this area.

An example is games. Heather organized a game in which we used beans as money. Players chose which crops to plant, each with its own associated cost, risk profile, and potential return. The expected payoff varied by gender, which was arbitrarily assigned to players. After a few rounds the problem was clear—higher costs, lower returns, and greater risks for women increased their chances of financial ruin, and this had negative consequences for communities.

I believe that evaluators could put games to good use. Describing a social problem as a game requires stakeholders to express their cause-and-effect assumptions about the problem. Playing with a group allows others to understand those assumptions intimately, comment upon them, and offer suggestions about how to solve the problem within the rules of the game (or perhaps change the rules to make the problem solvable).

I have never met a group of people who were more sincere in their pursuit of positive change. And honest in their struggle to evaluate their impact. I believe that impact evaluation is an area where evaluators have something valuable to share with designers.

That was the purpose of my workshop Measuring Social Impact: How to Integrate Evaluation & Design. I presented a number of techniques and tools we use at Gargani + Company to design and evaluate programs. They are part of a more comprehensive program design approach that Stewart Donaldson and I will be sharing this summer and fall in workshops and publications (details to follow).

The hands-on format of the lab made for a great experience. I was able to watch participants work through the real-world design problems that I posed. And I was encouraged by how quickly they were able to use the tools and techniques I presented to find creative solutions.

That made my task of providing feedback on their designs a joy. We shared a common conceptual framework and were able to speak a common language. Given the abstract nature of social impact, I was very impressed with that—and their designs—after less than 90 minutes of interaction.

I wrapped up the conference by attending Three Cups, Rosa Parks, and the Polar Bear: Telling Stories that Work presented by Melanie Moore Kubo and Michaela Leslie-Rule from See Change. They use stories as a vehicle for conducting (primarily) qualitative evaluations. They call it story science. A nifty idea.

I liked this session for two reasons. First, Melanie and Michaela are expressive storytellers, so it was great fun listening to them speak. Second, they posed a simple question—Is this story true?—that turns out to be amazingly complex.

We summarize, simplify, and translate meaning all the time. Those of us who undertake (primarily) quantitative evaluations agonize over this because our standards for interpreting evidence are relatively clear but our standards for judging the quality of evidence are not.

For example, imagine that we perform a t-test to estimate a program’s impact. The t-test indicates that the impact is positive, meaningfully large, and statistically significant. We know how to interpret this result and what story we should tell—there is strong evidence that the program is effective.

But what if the outcome measure was not well aligned with the program’s activities? Or there were many cases with missing data? Would our story still be true? There is little consensus on where to draw the line between truth and fiction when quantitative evidence is flawed.

As Melanie and Michaela pointed out, it is critical that we strive to tell stories that are true, but equally important to understand and communicate our standards for truth. Amen to that.

The icing on the cake was the conference evaluation. Perhaps the best conference evaluation I have come across.

Everyone received four post-it notes, each a different color. As a group, we were given a question to answer on a post-it of a particular color, and only a minute to answer the question. Immediately afterward, the post-its were collected and displayed for all to view, as one would view art in a gallery.

Evaluation as art—I like that. Immediate. Intimate. Transparent.

Gosh, I like designers.

4 Comments

Filed under Conference Blog, Design, Evaluation, Program Design, Program Evaluation

Tagged as Catapult Design, complexity, Design, effective programs, empowerment, evaluation, evaluations, policy development, Program Design, program designers, Program Evaluation

May 10, 2012 · 6:35 am

Measuring Impact: Integrating Evaluation & Design (Workshop May 19 in SF)

Interested in design for social change? Curious about how to measure the social impact of your designs? Check out my upcoming San Francisco workshop—Measuring Impact: Integrating Evaluation & Design–taking place on May 19 as part of CatapultLabs: Design Tools to Spark Social Change.

Come join in a day of hands-on labs with leading designers and organizations promoting social change.

Learn more about it at http://catapultlabs-2012.eventbrite.com/–space is limited.

Conference Blog: The Wharton “Creating Lasting Change” Conference

How can corporations promote the greater good? Can they do good and be profitable? How well can we measure the good they are doing?

These were some of the questions explored at a recent Wharton School Conference entitled Creating Lasting Change: From Social Entrepreneurship to Sustainability in Retail. I provide a brief recap of the event. Then I discuss why I believe program evaluators, program designers, and corporations have a great deal to learn from each other.

The Location

The conference took place at Wharton’s stunning new San Francisco campus. By stunning I mean drop-dead gorgeous. Here is one of its many views.

An Unusual and Effective Conference

The conference was jointly organized by three entities within the Wharton School—the Jay H. Baker Retailing Center, the Initiative for Global Environmental Leadership, and the Wharton Program for Social Impact.

When I first read this I scratched my head. A conference that combined the interests of any two made sense to me. Combining the interests of all three seemed like a stretch. I found—much to my delight—that the conference worked very well because of its two-panel structure.

Panel 1 addressed the social and environmental impact of new ventures; Panel 2 addressed the impact of large, established corporations. This offered an opportunity to compare and contrast new with old, small with large, and risk takers with the risk averse.

Fascinating and enlightening. I explain why after I describe the panels.

Panel 1: Social Entrepreneurship/Innovation

The first panel considered how entrepreneurs and venture capitalists can promote positive environmental and social change.

Andrew D’Souza, Chief Revenue Officer at Top Hat Monocle, discussed how his company developed web-based clickers for classrooms and online homework tools that are designed to promote learning—a social benefit that can be directly monetized.
Mike Young, Director of Technology Development at Innova Dynamics, described how his company’s social mission drives their development and commercialization of “disruptive advanced materials technologies for a sustainable future.”
Amy Errett, Partner at the venture capital firm Maveron, emphasized the firm’s belief that businesses focusing on a social mission tend to achieve financial success.
Susie Lee, Principal at TBL Capital, outlined her firm’s patient capital approach, which favors companies that balance their pursuit of social, environmental, and financial objectives.
Raghavan Anand, Chief Financial Officer at One Million Lights, moderated the panel.

Panel 2: Sustainability/CSR in the Retail Industry

The second panel discussed how large, established companies impact society and the natural world, and what it means for a corporation to act responsibly.

Christy Consler, Vice President of Sustainability at Safeway Inc., made the case that the large grocer (roughly 1,700 stores and 180,000 employees) needs to focus on sustainable, socially responsible operations to ensure that it has dependable sources for its product—food—as the world population swells by 2 billion over the next 35 years.

Lori Duvall, Director of Operational Sustainability at eBay Inc., summarized eBay’s sustainability efforts, which include solar power installations, reusable packaging, and community engagement.

Paul Dillinger, Senior Director-Global Design at Levi Strauss & Co., made an excellent presentation on the social and environmental consequences—positive and negative—of the fashion industry, and how the company is working to make a positive impact.

Shauna Sadowski, Director of Sustainability at Annie’s (you know, the company that makes the cute organic, bunny-shaped mac and cheese), discussed how bringing natural foods to the marketplace motivates sustainable, community-centered operations.

Barbara Kahn moderated. She wins the prize for having the longest title—the Patty & Jay H. Baker Professor, Professor of Marketing; Director, Jay H. Baker Retailing Center—and from what I could tell, she deserves every bit of the title.

Measuring Social Impact

I was thrilled to find corporations, new and old, concerned with making the world a better place. Business in general, and Wharton in particular, have certainly changed in the 20 years since I earned my MBA.

The unifying theme of the panels was impact. Inevitably, that discussion turned from how corporations were working to make social and environmental impacts to how they were measuring impacts. When it did, the word evaluation was largely absent, being replaced by metrics, measures, assessments, and indicators. Evaluation, as a field and a discipline, appears to be largely unknown to the corporate world.

Echoing what I heard at the Harvard Social Enterprise Conference (day 1 and day 2), impact measurement was characterized as nascent, difficult, and elusive. Everyone wants to do it; no one knows how.

I find this perplexing. Is the innovation, operational efficiency, and entrepreneurial spirit of American corporations insufficient to crack the nut of impact measurement?

Without a doubt, measuring impact is difficult—but not for the reasons one might expect. Perhaps the greatest challenge is defining what one means by impact. This venerable concept has become a buzzword, signifying both more an less than it should for different people in different settings. Clarifying what we mean simplifies the task of measurement considerably. In this setting, two meanings dominated the discussion.

One was the intended benefit of a product or service. Top Hat Monocle’s products are intended to increase learning. Annie’s foods are intended to promote health. Evaluators are familiar with this type of impact and how to measure it. Difficult? Yes. It poses practical and technical challenges, to be sure. Nascent and elusive? No. Evaluators have a wide range of tools and techniques that we use regularly to estimate impacts of this type.

The other dominant meaning was the consequences of operations. Evaluators are probably less familiar with this type of impact.

Consider Levi’s. In the past, 42 liters of fresh water were required to produce one pair of Levi’s jeans. According to Paul Dillinger, the company has since produced about 13 million pairs using a more water-efficient process, reducing the total water required for these jeans from roughly 546 million liters to 374 million liters—an estimated savings of 172 million liters.

Is that a lot? The Institute of Medicine estimates that one person requires about 1,000 liters of drinking water per year (2.2 to 3 liters per day making a variety of assumptions)—so Levi’s saved enough drinking water for about 172,000 people for one year. Not bad.

But operational impact is more complex than that. Levi’s still used the equivalent yearly drinking water for 374,000 people in places where potable water may be in short supply. The water that was saved cannot be easily moved where it may be needed more for drinking, irrigation, or sanitation. If the water that is used for the production of jeans is not handled properly, it may contaminate larger supplies of fresh water, resulting in a net loss of potable water. The availability of more fresh water in a region can change behavior in ways that negate the savings, such as attracting new industries that depend on water or inducing wasteful water consumption practices.

Is it difficult to measure operational impact? Yes. Even estimating something as tangible as water use is challenging. Elusive? No. We can produce impact estimates, although they may be rough. Nascent? Yes and no. Measuring operational impact depends on modeling systems, testing assumptions, and gauging human behavior. Evaluators have a long history of doing these things, although not in combination for the purpose of measuring operational impact.

It seems to me that evaluators and corporations could learn a great deal from each other. It is a shame these two worlds are so widely separated.

Designing Corporate Social Responsibility Programs

With all the attention given to estimating the value of corporate social responsibility programs, the values underlying them were not fully explored. Yet the varied and often conflicting values of shareholders and stakeholders pose the most significant challenge facing those designing these programs.

Why do I say that? Because it has been that way for over 100 years.

The concept of corporate social responsibility has deep roots. In 1909, William Tolman wrote about a trend he observed in manufacturing. Many industrialists, by his estimation, were taking steps to improve the working conditions, pay, health, and communities of their employees. He noted that these unprompted actions had various motives—a feeling that workers were owed the improvements, unqualified altruism, or the belief that the efforts would lead to greater profits.

Tolman placed a great deal of faith in the last motive. Too much faith. Twentieth-century industrial development was not characterized by rational, profit-maximizing companies competing to improve the lot of stakeholders in order to increase the wealth of shareholders. On the contrary, making the world a better place typically entailed tradeoffs that shareholders found unacceptable.

So these early efforts failed. The primary reason was that their designs did not align the values of shareholders and stakeholders.

Can the values of shareholders and stakeholders be more closely aligned today? I believe they can be. The founders of many new ventures, like Top Hat Monocle and Innova Dynamics, bring different values to their enterprises. For them, Tolman’s nobler motives—believing that people deserve a better life and a desire to do something decent in the world—are the cornerstones of their company cultures. Even in more established organizations—Safeway and Levi’s—there appears to be a cultural shift taking place. And many venture capital firms are willing to take a patient capital approach, waiting longer and accepting lower returns, if it means they can promote a greater social good.

This is change for the better. But I wonder if we, like Tolman, are putting too much faith in win-win scenarios in which we imagine shareholders profit and stakeholders benefit.

It is tempting to conclude that corporate social responsibility programs are win-win. The most visible examples, like those presented at this conference, are. What lies outside of our field of view, however, are the majority of rational, profit-seeking corporations that are not adopting similar programs. Are we to conclude that these enterprises are not as rational as they should be? Or have we yet to design corporate responsibility programs that resolve the shareholder-stakeholder tradeoffs that most companies face?

Again, there seems to be a great deal that program designers, who are experienced at balancing competing values, and corporations can learn from each other…if only the two worlds met.

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Filed under Commentary, Conference Blog, Design, Evaluation, Program Design, Program Evaluation

Tagged as Annie's, complexity, Creating Lasting Change Conference, eBay, environment, evaluation, evaluations, Innova Dynamics, Levi Strauss & Co., Maveron, measurement, One Million Lights, Program Design, program designers, Program Evaluation, Safeway, social enterprise, TBL Capital, The Wharton School, Top Hat Monocle, venture capitalists, wharton school

February 22, 2012 · 8:42 am

Toward a Taxonomy of Wicked Problems

Program designers and evaluators have become keenly interested in wicked problems. More precisely, we are witnessing a second wave of interest—one that holds new promise for the design of social, educational, environmental, and cultural programs.

The concept of wicked problems was first introduced in the late 1960s by Horst Rittel, then at UC Berkeley. It became a popular subject for authors in many disciplines, and writing on the subject grew through the 1970s and into the early 1980s (the first wave). At that point, writing on the subject slowed until the late 1990s when the popularity of the subject again grew (the second wave).

Here are the results of a Google ngram analysis that illustrates the two waves of interest (click the image to enlarge).

Rittel contrasted wicked problems with tame problems. Various authors, including Rittel, have described the tame-wicked dichotomy in different ways. Most are based on the 10 characteristics of wicked problems that Rittel introduced in the early 1970s. Briefly…

Tame problems can be solved in isolation by an expert—the problems are relatively easy to define, the range of possible solutions can be fully enumerated in advance, stakeholders hold shared values related to the problems and possible solutions, and techniques exist to solve the problems as well as measure the success of implemented solutions.

Wicked problems are better addressed collectively by diverse groups—the problems are difficult to define, few if any possible solutions are known in advance, stakeholders disagree about underlying values, and we can neither solve the problems (in the sense that they can be eliminated) nor measure the success of implemented solutions.

In much of the writing that emerged during the first wave of interest, the tame-wicked dichotomy was the central theme. It was argued that most problems of interest to policymakers are wicked, which limited the utility of the rational, quantitative, stepwise thinking that dominated policy planning, operations research, and management science at the time. A new sort of thinking was needed.

In the writing that has emerged in the second wave, that new sort of thinking has been given many names—systems thinking, design thinking, complexity thinking, and developmental thinking, to name a few. Each, supposedly, can tame what would otherwise be wicked.

Perhaps.

The arguments for “better ways of thinking” are weakened by the assumption that wicked and tame represent a dichotomy. If most social problems met all 10 of Rittel’s criteria, we would be doomed. We aren’t.

Social problems are more or less wicked, each in its own way. Understanding how a problem is wicked, I believe, is what will enable us to think more effectively about social problems and to tame them more completely.

Consider two superficially similar examples that are wicked in different ways.

Contagious disease: We understand the biological mechanisms that would allow us to put an end to many contagious diseases. In this sense, these diseases are tame problems. However, we have not been able to eradicate all contagious diseases that we understand well. The reason, in part, is that many people hold values that conflict with solutions that are, on a biological level, known to be effective. For example, popular fear of vaccines may undermine the effectiveness of mass vaccination, or the behavioral changes needed to reduce infection rates may clash with local cultures. In cases such as this, contagious diseases pose wicked problems because of conflicting values. The design of programs to eradicate these diseases would need to take this source of wickedness into account, perhaps by including strong stakeholder engagement efforts or public education campaigns.

Cancer: We do not fully understand the biological mechanisms that would allow us to prevent and cure many forms of cancer. At the same time, the behaviors that might reduce the risk of these cancers (such as healthy diet, regular exercise, not smoking, and avoiding exposure to certain chemicals) conflict with values that many people hold (such as the importance of personal freedom, desire for comfort and convenience, and the need to earn a living in certain industrial settings). In these cases, cancer poses wicked problems for two reasons—our lack of understanding and conflicting values. This may or may not make it “more” wicked than eradicating well-understood contagious diseases; that is difficult to assess. But it certainly makes it wicked in a different way, and the design of programs to end cancer would need to take that difference into account and address both sources of wickedness.

The two examples above are wicked problems, but they are wicked for different reasons. Those reasons have important implications for program designers. My interest over the next few months is to flesh out a more comprehensive taxonomy of wickedness and to unpack its design implications. Stay tuned.

5 Comments

Filed under Design, Program Design

Tagged as complexity, design thinking, Horst Rittel, policy development, possible solutions, Program Design, program designers, rittel, social messes, systems thinking, wicked problems

February 13, 2012 · 10:58 am

Should the Pie Chart Be Retired?

The ability to create and interpret visual representations has been an important part of the human experience since we began drawing on cave walls at Chauvet.

Today, that ability—what I call visualcy—has even greater importance. We use visuals to discover how the world works, communicate our discoveries, plan efforts to improve the world, and document the success of our efforts.

In short, visualcy affects every aspect of program design and evaluation.

The evolution of our common visual language, sadly, has been shaped by the default settings of popular software, the norms of the conference room, and the desire to attract attention. It is not a language constructed to advance our greater purposes. In fact, much of our common language works against our greater purposes.

An example of a counterproductive element of our visual language is the pie chart.

Consider this curious example from the New York Times Magazine (1/15/2012).

This pie chart has a humble purpose—summarize reader responses to an article on obesity in the US. It failed that purpose stunningly. Here are some reasons why.

(1) Three-dimensionality reduces accuracy: Not only are 3-D graphs harder to read accurately, but popular software can construct them inaccurately. The problem—for eye and machine—arises from the translation of values in 1-D or 2-D space into values in 3-D space. This is a substantial problem with pie charts (imagine computing the area of a pie slice while taking its 3-D perspective into account) as well as other types of graph. Read Stephanie Evergreen’s blog post on the perils the 3-D to see a good example.

(2) Pie charts impede comparisons: People have trouble comparing pie slices by eye. Think you can? Here is a simple pie chart I constructed from the data in the NYT Magazine graph. Which slice is larger—orange or the blue?

This is much clearer.

Note that the the the Y axis ranges from 0% to 100%. That is what makes the bar chart a substitute for the pie chart. Sometimes the Y axis is truncated innocently to save column inches or intentionally to create a false impression, like this:

Differences are exaggerated and large values seem to be closer to 100% than they really are. Don’t do this.

(3) The visual theme is distracting: I suspect the NYT Magazine graph is intended to look like some sort of food. Pieces of a pie? Cake? Cheese? It doesn’t work. This does.

Unless you are evaluating the Pillsbury Bake-Off, however, it is probably not an appropriate theme.

(4) Visual differentiators add noise: Graphs must often differentiate elements. A classic example is differentiating treatment and control group averages using bars of different colors. In the NYT Magazine pie chart, the poor choice of busy patterns makes it very difficult to differentiate one piece of the pie from another. The visual chaos is reminiscent of the results of a “poll” of Iraqi voters presented by the Daily Show in which a very large number of parties purportedly held almost equal levels of support.

(5) Data labels add more noise: Data labels can increase clarity. In this case, however, the swarm of curved arrows connecting labels to pieces of the pie adds to the visual chaos. Even this tangle of labels is better because readers instantly understand that Iraq received a disproportionate amount of the aid provided to many countries.

Do you think I made up these reasons? Then read this report by RAND that investigated graph comprehension using experimental methods. Here is a snippet from the abstract:

We investigated whether the type of data display (bar chart, pie chart, or table) or adding a gratuitous third dimension (shading to give the illusion of depth) affects the accuracy of answers of questions about the data. We conducted a randomized experiment with 897 members of the American Life Panel, a nationally representative US web survey panel. We found that displaying data in a table lead [sic] to more accurate answers than the choice of bar charts or pie charts. Adding a gratuitous third dimension had no effect on the accuracy of the answers for the bar chart and a small but significant negative effect for the pie chart.

There you have it—empirical evidence that it is time to retire the pie chart.

Alas, I doubt that the NYT Magazine, infographic designers, data viz junkies, or anyone with a reporting deadline will do that. As every evaluator knows, it is far easier to present empirical evidence than respond to it.

5 Comments

Filed under Design, Evaluation, Visualcy

Tagged as axis ranges, data viz, evaluation, How to Lie with Statistics, Inaccurate Graphs, nyt magazine, pie charts, Program Design, Program Evaluation, types of graph, visual representations

January 23, 2012 · 12:00 am

Tragic Graphic: The Wall Street Journal Lies with Statistics?

Believe it or not, the Wall Street Journal provides another example of an inaccurate circular graph. This time the error so closely parallels an example from Darrell Huff’s classic How to Lie with Statistics that I find myself wondering—intentional deception or innocent blunder?

The image above comes from Huff’s book. The moneybag on the left represents the average weekly salary of carpenters in the fictional country of Rotundia. The bag on the right, the average weekly salary of carpenters in the US.

Based on the graph, how much more do carpenters in the US earn? Twice? Three times? Four times? More?

The correct answer is that they earn twice as much, but the graph gives the impression that the difference is greater than that. The heights of the bags are proportionally correct but their areas are not. Because we tend to focus on the areas of shapes, graphics like this can easily mislead readers.

Misleading the reader, of course, was Huff’s intention. As he put it:

…I want you to infer something, to come away with an exaggerated impression, but I don’t want to be caught at my tricks.

What were the intentions of the Wall Street Journal this Saturday (1/21/2012) when it previewed Charles Murray’s new book Coming Apart?

In the published preview, Murray made a highly qualified claim—the median family income across 14 of the most elite places to live in 1960 rose from $84,000 in 1960 to $163,000 in 2000, after adjusting incomes to reflect today’s purchasing power.

Those cumbersome qualifications take the oomph right out of the claim. Too long to be a provocative sound bite, the Journal refashioned it into a provocative sight bite. Wow, those incomes really grew!

But not as much as the graph suggests. The text states that the median salary just about doubled. The picture indicates that it quadrupled. It’s Huff’s moneybag trick—even down to the relative proportion of salaries!

Here is a comparison of the inaccurate graph with an accurate version I constructed. The accurate graph is far less provocative.

As a rule, the areas of circles are difficult for people to compare by eye. In fact, using the area of any two-dimensional shape to represent one-dimensional data is probably a bad idea. Not only do interpretations vary depending on the shape that is used, but they vary depending on the relative placement of the shapes.

To illustrate these points, here are six alternative representations of Murray’s data. Which, if any, are lies?

3 Comments

Filed under Design, Visualcy

Tagged as Charles Murray, Coming Apart, How to Lie with Statistics, Inaccurate Graphs, Program Design, Wall Street Journal

November 2, 2010 · 6:08 pm

Good versus Eval

After another blogging hiatus, the battle between good and eval continues. Or at least my blog is coming back online as the American Evaluation Association’s Annual Conference in San Antonio (November 10-14) quickly approaches.

I remember that twenty years ago evaluation was widely considered the enemy of good because it took resources away from service delivery. Now evaluation is widely considered an essential part of service delivery, but the debate over what constitutes a good program and a good evaluation continues. I will be joining the fray when I make a presentation as part of a session entitled Improving Evaluation Quality by Improving Program Quality: A Theory-Based/Theory-Driven Perspective (Saturday, November 13, 10:00 AM, Session Number 742). My presentation is entitled The Expanding Profession: Program Evaluators as Program Designers, and I will discuss how program evaluators are increasingly being called upon to help design the programs they evaluate, and why that benefits program staff, stakeholders, and evaluators. Stewart Donaldson is my co presenter (The Relationship between Program Design and Evaluation), and our discussants are Michael Scriven, David Fetterman, and Charles Gasper. If you know these names, you know to expect a “lively” (OK, heated) discussion.

If you are an evaluator in California, Oregon, Washington, New Mexico, Hawaii, any other place west of the Mississippi, or anywhere that is west of anything, be sure to attend the West Coast Evaluators Reception Thursday, November 11, 9:00 pm at the Zuni Grill (223 Losoya Street, San Antonio, TX 78205) co-sponsored by San Francisco Bay Area Evaluators and Claremont Graduate University. It is a conference tradition and a great way to network with colleagues.

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