Tag Archives: Program Evaluation

New European Standard for Social Impact Measurement

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Evaluation has truly become a global movement. The number of evaluators and evaluation associations around the world is growing, and they are becoming more interconnected. What affects evaluation in one part of the world increasingly affects how it is practiced in another.

That is why the European standard for social impact measurement, announced just a few weeks ago, is important for evaluators in the US.

According to the published report and its accompanying press release, the immediate purpose of the standard is to help social enterprises access EU financial support, especially in relation to the European Social Entrepreneurship Funds (EuSEFs) and the Programme for Employment and Social Innovation (EaSI).

But as László Andor, EU Commissioner for Employment, Social Affairs and Inclusion, pointed out, there is a larger purpose:

The new standard…sets the groundwork for social impact measurement in Europe. It also contributes to the work of the Taskforce on Social Impact Investment set up by the G7 to develop a set of general guidelines for impact measurement to be used by social impact investors globally.

That is big, and it has the potential to affect evaluation around the world.

What is impact measurement?

For evaluators in the US, the term impact measurement may be unfamiliar. It has greater currency in Europe and, of late, in Canada. Defining the term precisely is difficult because, as an area of practice, impact measurement is evolving quickly.

Around the world, there is a growing demand for evaluations that incorporate information about impact, values, and value. It is coming from government agencies, philanthropic foundations, and private investors who want to increase their social impact by allocating their public or private funds more efficiently.

Sometimes these funders are called impact investors. In some contexts, the label signals a commitment to grant making that incorporates the tools and techniques of financial investors. In others, it signals a commitment by private investors to a double bottom line—a social return on their investment for others and a financial return for themselves.

These funders want to know if people are better off in ways that they and other stakeholders believe are important. Moreover, they want to know whether those impacts are large enough and important enough to warrant the funds being spent to produce them. In other words, did the program add value?

Impact measurement may engage a wide range of stakeholders to define the outcomes of interest, but the overarching definition of success—that the program adds value—is typically driven by funders. Value may be assessed with quantitative, qualitative, or mixed methods, but almost all of the impact measurement work that I have seen has framed value in quantitative terms.

Is impact measurement the same as evaluation?

I consider impact measurement a specialized practice within evaluation. Others do not. Geographic and disciplinary boundaries have tended to isolate those who identify themselves as evaluators from those who conduct impact measurement—often referred to as impact analysts. These two groups are beginning to connect, like evaluators of every kind around the world.

I like to think of impact analysts and evaluators as twins who were separated at birth and then, as adults, accidentally bump into each other at the local coffee shop. They are delighted and confused, but mostly delighted. They have a great deal to talk about.

How is impact measurement different from impact evaluation?

There is more than one approach to impact evaluation. There is what we might call traditional impact evaluation—randomized control trials and quasi-experiments as described by Shadish, Cook, and Campbell. There are also many recently developed alternatives—contribution analysis, evaluation of collective impact, and others.

Impact measurement differs from traditional and alternative impact evaluation in a number of ways, among them:

  1. how impacts are estimated and
  2. a strong emphasis on valuation.

I discuss both in more detail below. Briefly, impacts are frequently estimated by adjusting outcomes for a pre-established set of potential biases, usually without reference to a comparison or control group. Valuation estimates the importance of impacts to stakeholders—the domain of human values—and expresses it in monetary units.

These two features are woven into the European standard and have the potential to become standard practices elsewhere, including the US. If they were to be incorporated into US practice, it would represent a substantial change in how we conduct evaluations.

What is the new European standard?

The standard creates a common process for conducting impact measurement, not a common set of impacts or indicators. The five-step process presented in the report is surprisingly similar to Tyler’s seven-step evaluation procedure, which he developed in the 1930s as he directed the evaluation of the Eight-Year Study across 30 schools. For its time, Tyler’s work was novel and the scale impressive.

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Tyler’s evaluation procedure developed in the 1930s and the new European standard process: déjà vu all over again?

Tyler’s first two steps were formulating and classifying objectives (what do programs hope to achieve and which objectives can be shared across sites to facilitate comparability and learning). Deeply rooted in the philosophy of progressive education, he and his team identified the most important stakeholders—students, parents, educators, and the larger community—and conducted much of their work collaboratively (most often with teachers and school staff).

Similarly, the first two steps of the European standard process are identifying objectives and stakeholders (what does the program hope to achieve, who benefits, and who pays). They are to be implemented collaboratively with stakeholders (funders and program staff chief among them) with an explicit commitment to serving the interests of society more broadly.

Tyler’s third and fourth steps were defining outcomes in terms of behavior and identifying how and where the behaviors could be observed. The word behavior was trendy in Tyler’s day. What he meant was developing a way to observe or quantify outcomes. This is precisely setting relevant measures, the third step of the new European standard process.

Tyler’s fifth and sixth steps were selecting, trying, proving, and improving measures as they function in the evaluation. Today we would call this piloting, validation, and implementation. The corresponding step in the standard is measure, validate and value, only the last of these falling outside the scope of Tyler’s procedure.

Tyler concluded his procedure with interpreting results, which for him included analysis, reporting, and working with stakeholders to facilitate the effective use of results. The new European standard process concludes in much the same way, with reporting results, learning from them, and using them to improve the program.

How are impacts estimated?

Traditional impact evaluation defines an impact as the difference in potential outcomes—the outcomes participants realized with the program compared to the outcomes they would have realized without the program.

It is impossible to observe both of these mutually exclusive conditions at the same time. Thus, all research designs can be thought of as hacks, some more elegant than others, that allow us to approximate one condition while observing the other.

The European standard takes a similar view of impacts and describes a good research design as one that takes the following into account:

  • attribution,the extent to which the program, as opposed to other programs or factors, caused the outcomes;
  • deadweight, outcomes that, in the absence of the program, would have been realized anyway;
  • drop-off, the tendency of impacts to diminish over time; and
  • displacement, the extent to which outcomes realized by program participants prevent others from realizing those outcomes (for example, when participants of a job training program find employment, it reduces the number of open jobs and as a result may make it more difficult for non-participants to find employment).

For any given evaluation, many research designs may meet the above criteria, some with the potential to provide more credible findings than others.

However, impact analysts may not be free to choose the research design with the potential to provide the most credible results. According to the standard, the cost and complexity of the design must be proportionate to the size, scope, cost, potential risks, and potential benefits of the program being evaluated. In other words, impact analysts must make a difficult tradeoff between credibility and feasibility.

How well are analysts making the tradeoff between credibility and feasibility?

At the recent Canadian Evaluation Society Conference, my colleagues Cristina Tangonan, Anna Fagergren (not pictured), and I addressed this question. We described the potential weaknesses of research designs used in impact measurement generally and Social Return on Investment (SROI) analyses specifically. Our work is based on a review of publicly available SROI reports (to date, 107 of 156 identified reports) and theoretical work on the statistical properties of the estimates produced.

ces_2014_tangonan_gargani_evalblogAt the CES 2014 conference.

What we have found so far leads us to question whether the credibility-feasibility tradeoffs are being made in ways that adequately support the purposes of SROI analyses and other forms of impact measurement.

One design that we discussed starts with measuring the outcome realized by program participants. For example, how many participants of a job training program found employment, or the test scores realized by students who were enrolled in a new education program. Sometimes impact analysts will measure the outcome as a pre-program/post-program difference, often they measure the post-program outcome level on its own.

Once the outcome measure is in hand, impact analysts adjust it for attribution, deadweight, drop-off, and displacement by subtracting some amount or percentage for each potential bias. The adjustments may be based on interviews with past participants, prior academic or policy research, or sensitivity analysis. Rarely are they based on comparison or control groups constructed for the evaluation. The resulting adjusted outcome measure is taken as the impact estimate.

This is an example of a high-feasibility, low-credibility design. Is it good enough for the purposes that impact analysts have in mind? Perhaps, but I’m skeptical. There is a century of systematic research on estimating impacts—why didn’t this method, which is much more feasible than many alternatives, become a standard part of evaluation practice decades before? I believe it is because the credibility of the design (or more accurately, the results it can produce) is considered too low for most purposes.

From what I understand, this design–and others that are similar–would meet the European standard. That leads me to question whether the new standard has set the bar too low, unduly favoring feasibility over credibility.

What is valuation?

In the US, I believe we do far less valuation than is currently being done in Europe and Canada. Valuation expresses the value (importance) of impacts in monetary units (a measure of importance).

If the outcome, for example, were earned income, then valuation would entail estimating an impact as we usually would. If the outcome were health, happiness, or well-being, valuation would be more complicated. In this case, we would need to translate non-monetary units to monetary units in a way that accurately reflects the relative value of impacts to stakeholders. No easy feat.

In some cases, valuation may help us gauge whether the monetized value of a program’s impact is large enough to matter. It is difficult to defend spending $2,000 per participant of a job training program that, on average, results in additional earned income of $1,000 per participant. Participants would be better off if we gave $2,000 to each.

At other times, valuation may not be useful. For example, if one health program saves more lives than another, I don’t believe we need to value lives in dollars to judge their relative effectiveness.

Another concern is that valuation reduces the certainty of the final estimate (in monetary units) as compared to an impact estimate on its own (in its original units). That is a topic that I discussed at the CES conference, and will again at the conferences of the European Evaluation Society, Social Impact Analysts Association, and the American Evaluation Association .

There is more to this than I can hope to address here. In brief—the credibility of a valuation can never be greater than the credibility of the impact estimate upon which it is based. Call that Gargani’s Law.

If ensuring the feasibility of an evaluation results in impact estimates with low credibility (see above), we should think carefully before reducing credibility further by expressing the impact in monetary units.

Where do we go from here?

The European standard sets out to solve a problem that is intrinsic to our profession–stakeholders with different perspectives are constantly struggling to come to agreement about what makes an evaluation good enough for the purposes they have in mind. In the case of the new standard, I fear the bar may be set too low, tipping the balance in favor of feasibility over credibility.

That is, of course, speculation. But so too is believing the balance is right or that it is tipped in the other direction. What is needed is a program of research—research on evaluation—that helps us understand whether the tradeoffs we make bear the fruit we expect.

The lack of research on evaluation is a weak link in the chain of reasoning that makes our work matter in Europe, the US, and around the world. My colleagues and I are hoping to strengthen that link a little, but we need others to join us. I hope you will.

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AfrEA Conference 2014 #2: Commitment, Community, and Change

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The 2014 Conference of the African Evaluation Association (AfrEA) was just opened. Organizers delayed the start of the opening ceremony, however, as they waited for the arrival of officials from the government of Cameroon. Fifteen minutes. Thirty minutes. An hour. More.

This may sound like a problem, but it wasn’t—the unofficial conference had already begun. Participants from around the world were mixing, laughing, and learning. I met evaluators from Kenya, South Africa, Sri Lanka, Europe, and America. I learned about health programs, education systems, evaluation use in government, and the development of evaluation as a profession across the continent. It was a truly delightful delay.

And it reflects the mindset I am finding here—a strong belief that commitment and community can overcome circumstance.

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During the opening ceremony, the Former President of AGRA, Dr. Namanga Ngongi, stated that one of the greatest challenges facing development programs is finding enough qualified evaluators—those who not only  have technical skills, but also the ability to help organizations increase their impact.

Where will these much-needed evaluators come from?

Historically, many evaluators have come from outside of Africa. The current push for made-in-Africa evaluations promises to change that by training more African evaluators.

Evaluators are trained in many ways, chief among them university programs, professional mentoring, practical experience, and ongoing professional development. The CLEAR initiative—Centers for Learning on Evaluation and Results—is a new approach. With centers in Anglophone and Francophone Africa, CLEAR has set out to strengthen monitoring, evaluation, performance management, and evaluation use at the country level.

While much of CLEAR’s work is face-to-face, a great many organizations have made training material available on the web. One can now piece together free resources online—webinars, documents, videos, correspondence, and even one-on-one meetings with experts—that can result in highly contextualized learning. This is what many of the African evaluators I have met are telling me they are doing.

The US, Canada, Australia, and New Zealand appear to be leading exporters of evaluation content to Africa. Claremont Graduate University, Western Michigan University, the American Evaluation Association, the Canadian Government, and BetterEvaluation are some of the better-known sources.

What’s next? Perhaps consolidators who organize online and in-person content into high-quality curricula that are convenient, coherent, and comprehensive.

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Although the supply of evaluators may be limited in many parts of Africa, the demand for evaluation continues to increase. The history of evaluation in the US, Canada, and Europe suggests that demand grows when evaluation is required as a condition of funding or by law. From what I have seen, it appears that history is repeating itself in Africa. In large part this is due to the tremendous influence that funders from outside of Africa have.

An important exception is South Africa, where there government and evaluators work cooperatively to produce and use evaluations. I hope to learn more about this in the days to come.

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AfrEA Conference 2014 #1: What a Difference 32 Hours Makes

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“Tell me again why you are going to Cameroon?” my wife asked. I paused, searching for an answer. New business? Not really, although that is always welcome. Old connections? I have very few among those currently working in Africa. What should I say? How could I explain?

I decided to confess.

“Because I am curious. There is something exciting going on across Africa. The African Evaluation Association—AfrEA—is playing a critical role. I want to learn more about it. Support it. Maybe be a part of it.”

She found that perfectly reasonable. I suppose that is why I married her.

Then she asked more questions about the conference and how my work might be useful to practitioners in that part of the world. As it turns out, she was curious, too. I believe many are, especially evaluation practitioners.

It takes a certain irrational obsessiveness, however, to fly 32 hours because you are curious.

For those not yet prepared to follow their curiosity to such lengths, I will be blogging about the AfrEA Conference over the next week.

You can find guest posts about the previous AfrEA conference in Ghana two years ago here, here, here, and here.

Check back here for the latest conference news from Youndé, Cameroon.

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On the Ground at AEA #2: What Participants Had to Say

Are you suffering from “post-parting depression” now that the conference of the American Evaluation Association has ended? Maybe this will help–a sampling of the professionals who attended the conference, along with their thoughts on the experience.  Special thanks to Anna Fagergren who collected most of these photos and quotes.

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Stefany Tobel Ramos, City Year

This is my first time here and I really enjoyed the professional development workshop Evaluation-Specific Methodology. I learned a lot and have new ideas about how to get a sense of students as a whole.

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Jonathan Karanja, Independent Consultant with Nielsen, Kenya

This is my first time here and Nielsen is trying to get into the evaluation space, because that is what our clients want. The conference is a little overwhelming but I have a strategy – go to the not technically demanding, easy-to-digest sessions. Baby steps. I want to ensure that our company learns to not just apply market research techniques but to actually do evaluation.

george_julnes_aea_2013_evalblogGeorge Julnes, University of Baltimore

When I attend AEA, I get to present to enthusiastic groups of evaluation professionals. It makes me feel like a rock star for a week. Then I go home and do the dishes.

linda_pursley_aea_2013_evalblogLinda Pursley, Lesley University

I’m returning to the conference after some years away—it’s great to renew contact with acquaintances and colleagues. I am struck by the conference’s growth and the huge diversity of TIGs (topical interest groups), and I’m finding a lot of sessions of interest.

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Pieta Blakely, Commonwealth Corporation

It’s my first time here and it’s a little overwhelming. I’m getting to know what I don’t know. But it’s also really exciting to see people working on youth engagement because I’m really interested in that.

linda_stern_aea_2103_evalblogLinda Stern, National Democratic Institute

I’ve been coming for many years, and I really like the two professional development workshops I took—Sampling and Empowerment Evaluation Strategies—and how they helped guide my way through the greater conference program.

DSC02841Carsten Strømbæk Pedersen, National Board of Social Services, Denmark

John, I really like your blog. You have…how do you say it in English?…a twisted mind. I really like that.

Aske Graulund, National Board of Social Services, Denmark

Nina Middelboe, Oxford Research AS, Denmark

[nods of agreement]

No greater compliment, Carsten!  And my compliments to all 3,500 professionals who participated in the conference.

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On the Recursive Nature of Recursion: Reflections on the AEA 2013 Conference

John Gargani Not Blogging

Recursion is when your local bookstore opens a café inside the store in order to attract more readers, and then the café opens a bookstore inside itself to attract more coffee drinkers.

Chris Lysy at Freshspectrum.com noticed, laughed at, and illustrated (above) the same phenomenon as it relates to my blogging (or rather lack of it) during the American Evaluation Association Conference last week.

I intended to harness the power of recursion by blogging about blogging at the conference. I reckoned that would nudge a few others to blog at the conference, which in turn would nudge me to do the same.

I ended up blogging very little during those hectic days, and none of it was about blogging at the conference. Giving up on that idea, I landed on blogging about not blogging, then not blogging about not blogging, then blogging about not blogging about not blogging, and so on.

Once Chris opened my eyes to the recursive nature of recursion, I noticed it all around me at the conference.

roe_aea_2013_evalblogFor example, the Research on Evaluation TIG (Topical Interest Group) discussed using evaluation methods to evaluate how we evaluate. Is that merely academic navel gazing? It isn’t. I would argue that it may be the most important area of evaluation today.

As practitioners, we conduct evaluations because we believe they can make a positive impact in the world, and we choose how to evaluate in ways we believe produce the greatest impact. Ironically, we have little evidence upon which to base our choices. We rarely measure our own impact or study how we can best achieve it.

ROE (research on evaluation, for those in the know) is setting that right. And the growing community of ROE researchers and practitioners is attempting to do so in an organized fashion. I find it quite inspiring.

A great example of ROE and the power of recursion is the work of Tom Cook and his colleagues (chief among them Will Shadish).tom_cook_aea_2103_evalblogI must confess that Tom is a hero of mine. A wonderful person who finds tremendous joy in his work and shares that joy with others. So I can’t help but smile every time I think of him using experimental and quasi-experimental methods to evaluate experimental and quasi-experimental methods.

Experiments and quasi-experiments follow the same general logic. Create two (or more) comparable groups of people (or whatever may be of interest). Provide one experience to one group and a different experience to the other. Measure outcomes of interest for the two groups at the end of their experiences. Given that, differences in outcomes between the groups are attributable to differences in the experiences of the groups.

If on group received a program and the other did not, you have a very strong method for estimating program impacts. If on group received a program designed one way and the other a program designed another way, you have a strong basis for choosing between program designs.

Experiments and quasi-experiments differ principally in how they create comparable groups. Experiments assign people to groups at random. In essence, names are pulled from a hat (in reality, computers select names at random from a list). This yields two highly comparable but artificially constructed groups.

Quasi-experiments typically operate by allowing people to choose experiences as they do in everyday life. This yields naturally constructed groups that are less comparable. Why are they less comparable? The groups are comprised of people who made difference choices, and these choice may be associated with other factors that affect outcomes. The good news is that the groups can be made more comparable–to some degree–by using a variety of statistical methods.

four_arm_study_aea_2013_evalblogIs one approach better than another? At the AEA Conference, Tom described his involvement with efforts to answer that question. One way that is done is by randomly assigning people to two groups–one group that will be part of an experiment or another group that will be part of a quasi-experiment (referred to as an observational study in the picture above). Within the experimental group, participants are randomly assigned to either a treatment group (e.g., math training) or control group (vocabulary training). Within the quasi-experimental group, participants choose between the same two experiences, forming treatment and comparison groups according to their preference.

Program impact estimates are compared for the experimental and quasi-experimental groups. Differences at this level are attributable to the evaluation method and can indicate whether one method is biased with respect to the other. So far, there seems to be pretty good agreement between the methods (when implemented well–no small achievement), but much work remains to be done.

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Perhaps the most important form of recursion at the AEA Conference is membership. AEA is comprised of members who manage themselves by forming groups of members who manage themselves by forming groups of members who manage themselves. The board of AEA, TIGs, local affiliates, task forces, working groups, volunteer committees, and conference sessions are all organized by and comprised of groups of members who manage themselves. That is power of recursion–3,500 strangers coming together to create a community dedicated to making the world a better place. And what a joy to watch them pull it off.

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On the Ground at AEA #1: Tina and Rodney

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Rodney Hopson, Professor, George Mason University (Past President of AEA)

I’m plotting.  I’m always plotting. That’s how you make change in the world. You find the opportunities, great people to work with, and make things happen.

Tina Christie, Professor, UCLA

I’ve just finished three years on the AEA board with Rodney. The chance to connect with colleagues like Rodney–work with them, debate with them, laugh with them–is something I look forward to each year. It quickly starts to feel like family.

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Confessions of a Conference Junkie

evalblog_conference_junkieIt’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).

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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.

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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.

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

Measurement Is Not the Answer

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Bill Gates recently summarized his yearly letter in an article for the Wall Street Journal entitled My Plan to Fix the World’s Biggest Problems…Measure Them!

As an evaluator, I was thrilled. I thought, “Someone with clout is making the case for high-quality evaluation!” I was ready to love the article.

To my great surprise, I didn’t.

The premise of the piece was simple. Organizations working to change the world should set clear goals, choose an approach, measure results, and use those measures to continually refine the approach.

At this level of generality, who could disagree? Certainly not evaluators—we make arguments like this all the time.

Yet, I must—with great disappointment—conclude that Gates failed to make the case that measurement matters. In fact, I believe he undermined it by the way he used measurements.

Gates is not unique in this respect. His Wall Street Journal article is just one instance of a widespread problem in the social sector—confusing good measures for good inference.

Measures versus Inference

The difference between measures and inferences can be subtle. Measures quantify something that is observable. The number of students who graduate from high school or estimates of the calories people consume are measures. In order to draw conclusions from measures, we make inferences.  Two types of inference are of particular interest to evaluators.

(1) Inferences from measures to constructs. Constructs—unobservable aspects of humans or the world that we seek to understand—and the measures that shed light on them are not interchangeable. For example, what construct does the high school graduation rate measure? That depends. Possibly education quality, student motivation, workforce readiness, or something else that we cannot directly observe. To make an inference from measure to construct, the construct of interest must be well defined and its measure selected on the basis of evidence.

Evidence is important because, among other things, it can suggest whether many, few, or only one measure is required to understand a construct well. By using the sole measure of calories consumed, for example, we gain a poor understanding of a broad construct like health. However, we can use that single measure to gain a critical understanding of a narrower construct like risk of obesity.

(2) Inferences from measures to impacts. If high school graduation rates go up, was it the result of new policies, parental support, another reason left unconsidered, or a combination of several reasons? This sort of inference represents one of the fundamental challenges of program evaluation, and we have developed a number of strategies to address it. None is perfect, but more often than not we can identify a strategy that is good enough for a specific context and purpose.

Why do I think Gates made weak inferences from good measures? Let’s look at the three examples he offered in support of his premise that measurement is the key to solving the world’s biggest problems.

Example 1: Ethiopia

Gates described how Ethiopia became more committed to providing healthcare services in 2000 as part of the Millennium Development Goals. After that time, the country began tracking the health services it provided in new ways. As evidence that the new measurement strategy had an impact, Gates reported that child mortality decreased 60% in Ethiopia since 1990.

In this example, the inference from measure to impact is not warranted. Based on the article, the sole reason to believe that the new health measurement strategy decreased child mortality is that the former happened before the latter. Inferring causality from the sequential timing of events alone has been recognized as an inferential misstep for so long that it is best known by its Latin name, post hoc ergo propter hoc.

Even if we were willing to make causal inferences based on sequential timing alone, it would not be possible in this case—the tracking system began sometime after 2000 while the reported decrease in child mortality was measured from 1990.

Example 2: Polio

The global effort to eradicate polio has come down to three countries—Nigeria, Pakistan, and Afghanistan—where immunizing children has proven especially difficult. Gates described how new measurement strategies, such as using technology to map villages and track health workers, are making it possible to reach remote, undocumented communities in these countries.

It makes sense that these measurement strategies should be a part of the solution. But do they represent, “Another story of success driven by better measurement,” as Gates suggests?

Maybe yes, maybe no—the inference from measure to impact is again not warranted, but for different reasons.

In the prior example, Gates was looking back, claiming that actions (in the past) made an impact (in the past) because the actions preceded the impact. In this example, he made that claim that ongoing actions will lead to a future impact because the actions precede the intended impact of eradicating polio. The former was a weak inference, the latter weaker still because it incorporates speculation about the future.

Even if we are willing to trust an inference about an unrealized future in which polio has been eradicated, there is another problem. The measures Gates described are implementation measures. Inferring impact from implementation may be warranted if we have strong faith in a causal mechanism, in this case that contact with remote communities leads to immunization which in turn leads to reduction in the transmission of the disease.

We should have strong faith in second step of this causal mechanism—vaccines work. Unfortunately, we should have doubts about the first step because many who are contacted by health workers refuse immunization. The Bulletin of the World Health Organization reported that parental refusal in some areas around Karachi has been widespread, accounting for 74% of missed immunizations there. It is believed that the reasons for the refusals were fear related to safety and the religious implications of the vaccines. New strategies for mapping and tracking cannot, on the face of it, address these concerns.

So I find it difficult to accept that polio immunization is a story of success driven by measurement. It seems more like a story in which new measures are being used in a strategic manner. That’s laudable—but quite different from what was claimed.

Example 3: Education

The final example Gates provided came from the foundation’s $45 million Measures of Effective Teaching (MET) study. As described in the article, the MET study concluded that multiple measures of teacher effectiveness can be used to improve the way administrators manage school systems and teachers provide instruction. The three measures considered in the study were standardized test scores (transformed into controversial units called value-added scores), student surveys of teacher quality, and scores provided by trained observers of classroom instruction.

The first problem with this example is the inference from measures to construct. Everyone wants more effective teachers, but not everyone defines effectiveness the same way. There are many who disagree with how the construct of teacher effectiveness was defined in the MET study—that a more effective teacher is one who promotes student learning in ways that are reflected by standardized test scores.

Even if we accept the MET study’s narrow construct of teacher effectiveness, we should question whether multiple measures are required to understand it well. As reported by the foundation, all three measures in combination explain about 52% of the variation in teacher effectiveness in math and 26% in English-language arts. Test scores alone (transformed into value-added scores) explain about 48% and 20% of the variation in the math and English-language arts, respectively. The difference is trivial, making the cost of gathering additional survey and observation data difficult to justify.

The second problem is inference from measures to impact. Gates presented Eagle County’s experience as evidence that teacher evaluations improve education. He stated that Eagle County’s teacher evaluation system is “likely one reason why student test scores improved in Eagle County over the past five years.” Why does he believe this is likely? He doesn’t say. I can only respond post hoc ergo propter hoc.

So What?

The old chestnut that lack of evidence is not evidence of lacking applies here. Although Gates made inferences that were not well supported by logic and evidence, it doesn’t mean he arrived at the wrong conclusions. Or the right conclusions. All we can do is shrug our shoulders.

And it doesn’t mean we should not be measuring the performance and impact of social enterprises. I believe we should.

It does mean that Gates believes in the effectiveness of potential solutions for which there is little evidence. For someone who is arguing that measurement matters, he is setting a poor example. For someone who has the power to implement solutions on an unprecedented scale, it can also be dangerous.

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Evaluation in the Post-Data Age: What Evaluators Can Learn from the 2012 Presidential Election

Stop me if you’ve heard this one before.  An evaluator uses data to assess the effectiveness of a program, arrives at a well-reasoned but disappointing conclusion, and finds that the conclusion is not embraced—perhaps ignored or even rejected—by those with a stake in the program.

People—even evaluators—have difficulty accepting new information if it contradicts their beliefs, desires, or interests.  It’s unavoidable.  When faced with empirical evidence, however, most people will open their minds.  At least that has been my experience.

During the presidential election, reluctance to embrace empirical evidence was virtually universal.  I began to wonder—had we entered the post-data age?

The human race creates an astonishing amount of data—2.5 quintillion bytes of data per day.  In the last two years, we created 90% of all data created throughout human history.

In that time, I suspect that we also engaged in more denial and distortion of data than in all human history.

The election was a particularly bad time for data and the people who love them—but there was a bright spot.

On election day I boarded a plane for London (after voting, of course).  Although I had no access to news reports during the flight, I already knew the result—President Obama had about an 84% chance of winning reelection.  When I stepped off the plane, I learned he had indeed won.  No surprise.

How could I be so certain of the result when the election was hailed as too close to call?  I read the FiveThiryEight blog, that’s how.  By using data—every available, well-implemented poll—and a strong statistical model, Nate Silver was able to produce a highly credible estimate of the likelihood that one or the other candidate would win.

Most importantly, the estimate did not depend on the analysts’—or anyone’s—desires regarding the outcome of the election.

Although this first-rate work was available to all, television and print news was dominated by unsophisticated analysis of poll data.  How often were the results of an individual poll—one data point—presented in a provocative way and its implications debated for as long as breath and column inches could sustain?

Isn’t this the way that we interpret evaluations?

News agencies were looking for the story.  The advocates for each candidate were telling their stories.  Nothing wrong with that.  But when stories shape the particular bits of data that are presented to the public, rather than all of the data being used to shape the story, I fear that the post-data age is already upon us.

Are evaluators expected to do the same when they are asked to tell a program’s story?

It has become acceptable to use data poorly or opportunistically while asserting that our conclusions are data driven.  All the while, much stronger conclusions based on better data and data analysis are all around us.

Do evaluators promote similar behavior when we insist that all forms of evaluation can improve data-driven decision making?

The New York Times reported that on election night one commentator, with a sizable stake in the outcome, was unable to accept that actual voting data were valid because they contradicted the story he wanted to tell.

He was already living in the post-data age.  Are we?

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Conference Blog: Evaluation 2012 (Part 1)—Complexity

I have a great fondness for the American Evaluation Association and its Annual Conference.  At this year’s conference—Evaluation 2012—roughly 3,000 evaluators from around the world came together to share their work, rekindle old friendships, and establish new ones.  I was pleased and honored to be a part of it.

As I moved from session to session, I would ask those I met my favorite question—What have you learned that you will use in your practice?

Their answers—lists, connections, reflections—were filled with insights and surprises.  They helped me understand the wide range of ideas being discussed at the conference and how those ideas are likely to emerge in practice.

In the spirit of that question, I would like to share some thoughts about a few ideas that were thick in the air, starting with this post on complexity.

Complexity: The Undefined Elephant in the Room

The theme of the conference was Evaluation in Complex Ecologies: Relationships, Responsibilities, Relevance.  Not surprisingly, the concept of complexity received a great deal of attention.

Like many bits of evaluation jargon, it has a variety of legitimate formal and informal definitions.  Consequently, evaluators use the term in different ways at different times, which led a number of presenters to make statements that I found difficult to parse.

Here are a few that I jotted down:

“That’s not complex, it’s complicated.”

“A few simple rules can give rise to tremendous complexity.”

“Complexity can lead to startling simplicity.”

“A system can be simple and complicated at the same time.”

“Complexity can lead to highly stable systems or highly unstable systems.”

“Much of time people use the term complexity wrong.”

We are, indeed, a profession divided by a common language.

Why can’t we agree on a definition for complexity?

First, no other discipline has.  Perhaps that is too strong a statement—small sub-disciplines have developed common understandings of the term, but across those small groups there is little agreement.

Second, we cannot decide if complexity, simplicity, and complicatedness, however defined, are:

(A) Mutually exclusive

(B) Distinct but associated

(C) Inclusive and dependent

(D) All of the above

From what I can tell, the answer is (D).  That doesn’t help much, does it?

Third, we conflate the entities that we label as complex, complicated, or simple.  Over the past week, I heard the term complexity used to describe:

  • real-world structures such as social, environmental, and physical systems;
  • cognitive structures that we use to reason about real-world structures;
  • representations that we use to describe and communicate our cognitive structures;
  • computer models that we use to reveal the behavior of a system that is governed by a mathematically formal interpretation of our representations;
  • behaviors exhibited by real-world structures, cognitive structures, and computer models;
  • strategies that we develop to change the real world in a positive way;
  • human actions undertaken to implement change strategies; and
  • evaluations of our actions and strategies.

When we neglect to specify which entities we are discussing, or treat these entities as interchangeable, clarity is lost.

Where does this get us?

I hope it encourages us to do the following when we invoke the concept of complexity: define what we mean and identify what we are describing.  If we do that, we don’t need to agree—and we will be better understood.

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