Category Archives: Commentary

Measurement Is Not the Answer

gates_impact_evalblog

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: 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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Running Hot and Cold for Mixed Methods: Jargon, Jongar, and Code

Jargon is the name we give to big labels placed on little ideas. What should we call little labels placed on big ideas? Jongar, of course.

A good example of jongar in evaluation is the term mixed methods. I run hot and cold for mixed methods. I praise them in one breath and question them in the next, confusing those around me.

Why? Because mixed methods is jongar.

Recently, I received a number of comments through LinkedIn about my last post. A bewildered reader asked how I could write that almost every evaluation can claim to use a mixed-methods approach. It’s true, I believe that almost every evaluation can claim to be a mixed-methods evaluation, but I don’t believe that many—perhaps most—should.

Why? Because mixed methods is also jargon.

Confused? So were Abbas Tashakkori and John Creswell. In 2007, they put together a very nice editorial for the first issue of the Journal of Mixed Methods Research. In it, they discussed the difficulty they faced as editors who needed to define the term mixed methods. They wrote:

…we found it necessary to distinguish between mixed methods as a collection and analysis of two types of data (qualitative and quantitative) and mixed methods as the integration of two approaches to research (quantitative and qualitative).

By the first definition, mixed methods is jargon—almost every evaluation uses more than one type of data, so the definition attaches a special label to a trivial idea. This is the view that I expressed in my previous post.

By the second definition, which is closer to my own perspective, mixed methods is jongar—two simple words struggling to convey a complex concept.

My interpretation of the second definition is as follows:

A mixed-methods evaluation is one that establishes in advance a design that explicitly lays out a thoughtful, strategic integration of qualitative and quantitative methods to accomplish a critical purpose that either qualitative or quantitative methods alone could not.

Although I like this interpretation, it places a burden on the adjective mixed that it cannot support. In doing so, my interpretation trades one old problem—being able to distinguish mixed methods evaluations from other types of evaluation—for a number of new problems. Here are three of them:

  • Evaluators often amend their evaluation designs in response to unanticipated or dynamic circumstances—so what does it mean to establish a design in advance?
  • Integration is more than having quantitative and qualitative components in a study design—how much more and in what ways?
  • A mixed-methods design should be introduced when it provides a benefit that would not be realized otherwise—how do we establish the counterfactual?

These complex ideas are lurking behind simple words. That’s why the words are jongar and why the ideas they represent may be ignored.

Technical terms—especially jargon and jongar—can also be code. Code is the use of technical terms in real-world settings to convey a subtle, non-technical message, especially a controversial message.

For example, I have found that in practice funders and clients often propose mixed methods evaluations to signal—in code—that they seek an ideological compromise between qualitative and quantitative perspectives. This is common when program insiders put greater faith in qualitative methods and outsiders put greater faith in quantitative methods.

When this is the case, I believe that mixed methods provide an illusory compromise between imagined perspectives.

The compromise is illusory because mixed methods are not a middle ground between qualitative and quantitative methods, but a new method that emerges from the integration of the two. At least by the second definition of mixed methods that I prefer.

The perspectives are imagined because they concern how results based on particular methods may be incorrectly perceived or improperly used by others in the future. Rather than leap to a mixed-methods design, evaluators should discuss these imagined concerns with stakeholders in advance to determine how to best accommodate them—with or without mixed methods. In many funder-grantee-evaluator relationships, however, this sort of open dialogue may not be possible.

This is why I run hot and cold for mixed methods. I value them. I use them. Yet, I remain wary of labeling my work as such because the label can be…

  • jargon, in which case it communicates nothing;
  • jongar, in which case it cannot communicate enough; or
  • code, in which case it attempts to communicate through subtlety what should be communicated through open dialogue.

Too bad—the ideas underlying mixed methods are incredibly useful.

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Santa Cause

I’ve been reflecting on the past year.  What sticks in my mind is how fortunate I am to spend my days working with people who have a cause.  Some promote their causes narrowly, for example, by ensuring that education better serves a group of children or that healthcare is available to the poorest families in a region.  Others pursue causes more broadly, advocating for human rights and social justice.  In the past, both might have been labeled impractical dreamers, utopian malcontents, or, worse, risks to national security.  Yet today they are respected professionals, envied even by those who have achieved great success in more traditional, profit-motivated endeavors.  That’s truly progress.

I also spend a great deal of time buried in the technical details of evaluation—designing research, developing tests and surveys, collecting data, and performing statistical analysis—so I sometimes lose sight of the spirit that animates the causes I serve.  However, it isn’t long before I’m led back to the professionals who, even after almost 20 years, continue to inspire me.  I can’t wait to spend another year working with them.

The next year promises to be more inspiring than ever, and I look forward to sharing my work, my thoughts, and the occasional laugh with all of you in the new year.

Best wishes to all.

John

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Fruitility (or Why Evaluations Showing “No Effects” Are a Good Thing)

sisyphus

The mythical character Sisyphus was punished by the gods for his cleverness.   As mythological crimes go, cleverness hardly rates and his punishment was lenient — all he had to do was place a large boulder on top of a hill and then he could be on his way.

The first time Sisyphus rolled the boulder to the hilltop I imagine he was intrigued as he watched it roll back down on its own.  Clever Sisyphus confidently tried again, but the gods, intent on condemning him to an eternity of mindless labor, had used their magic to ensure that the rock always rolled back down.

Could there be a better way to punish the clever?

Perhaps not. Nonetheless, my money is on Sisyphus because sometimes the only way to get it right is to get it wrong. A lot.

This is the principle of fruitful futility, or as I call it fruitility. Continue reading

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It’s a Gift to Be Simple

 simple_logic

Theory-based evaluation acknowledges that, intentionally or not, all programs depend on the beliefs influential stakeholders have about the causes and consequences of effective social action. These beliefs are what we call theories, and they guide us when we design, implement, and evaluate programs.

Theories live (imperfectly) in our minds. When we want to clarify them for ourselves or communicate them to others, we represent them as some combination of words and pictures. A popular representation is the ubiquitous logic model, which typically takes the form of box-and-arrow diagrams or relational matrices.

The common wisdom is that developing a logic model helps program staff and evaluators develop a better understanding of a program, which in turn leads to more effective action.

Not to put too fine a point on it, this last statement is a representation of a theory of logic models. I represented the theory with words, which have their limits, yet another form of representation might reveal, hide, or distort different aspects of the theory. In this case, my theory is simple and my representation is simple, so you quickly get the gist of my meaning. Simplicity has its virtues.

It also has its perils. A chief criticism of logic models is that they fail to promote effective action because they are vastly too simple to represent the complexity inherent in a program, its participants, or its social value. This criticism has become more vigorous over time and deserves attention. In considering it, however, I find myself drawn to the other side of the argument, not because I am especially wedded to logic models, but rather to defend the virtues of simplicity. Continue reading

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Data-Free Evaluation

 curves

George Bernard Shaw quipped, “If all economists were laid end to end, they would not reach a conclusion.”  However, economists should not be singled out on this account — there is an equal share of controversy awaiting anyone who uses theories to solve social problems.  While there is a great deal of theory-based research in the social sciences, it tends to be more theory than research, and with the universe of ideas dwarfing the available body of empirical evidence, there tends to be little if any agreement on how to achieve practical results.  This was summed up well by another master of the quip, Mark Twain, who observed that the fascinating thing about science is how “one gets such wholesale returns of conjecture out of such a trifling investment of fact.”

Recently, economists have been in the hot seat because of the stimulus package.  However, it is the policymakers who depended on economic advice who are sweating because they were the ones who engaged in what I like to call data-free evaluation.  This is the awkward art of judging the merit of untried or untested programs. Whether it takes the form of a president staunching an unprecedented financial crisis, funding agencies reviewing proposals for new initiatives, or individuals deciding whether to avail themselves of unfamiliar services, data-free evaluation is more the rule than the exception in the world of policies and programs. Continue reading

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The Most Difficult Part of Science

tesla

I recently participated in a panel discussion at the annual meeting of the California Postsecondary Education Commission (CPEC) for recipients of Improving Teacher Quality Grants.  We were discussing the practical challenges of conducting what has been dubbed scientifically-based research (SBR).  While there is some debate over what types of research should fall under this heading, SBR almost always includes randomized trials (experiments) and quasi-experiments (close approximations to experiments) that are used to establish whether a program made a difference. 

SBR is a hot topic because it has found favor with a number of influential funding organizations.  Perhaps the most famous example is the US Department of Education, which vigorously advocates SBR and at times has made it a requirement for funding.  The push for SBR is part of a larger, longer-term trend in which funders have been seeking greater certainty about the social utility of programs they fund.

However, SBR is not the only way to evaluate whether a program made a difference, and not all evaluations set out to do so (as is the case with needs assessment and formative evaluation).  At the same time, not all evaluators want to or can conduct randomized trials.  Consequently, the push for SBR has sparked considerable debate in the evaluation community. Continue reading

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Conflicts as Conflicting Theories of the World

nyt_2009_01_25

Theories are like bellybuttons-everybody has one and all are surprisingly different.  Last Sunday Scott Atran and Jeremy Ginges wrote an opinion piece for the New York Times in which they described their research on beliefs about conflict and peace in the Middle East.  In brief, they argued that what many outsiders consider rational and logical solutions to the Israeli-Palestinian conflict, insiders consider irrational and illogical.  The reason has largely to do with sacred beliefs.  In spite of the name, these are not religious beliefs, per se, but rather any deeply held beliefs that sit at the core of our world views and are highly resistant to change.

In an earlier post I described beliefs in general as a pile of pick-up sticks, with the most resistant to change-the sacred beliefs-at the bottom of the pile.  Accordingly, altering sacred beliefs in any significant way will disturb all the rest.  At best this is exhausting, at worst traumatic.

Given the variety of beliefs that abound regarding social problems and solutions, it seems that program designers and policymakers are always treading upon someone’s sacred beliefs.  One of the practical questions we have been wrestling with is how to help groups of people with disparate world views reach consensus about programs and policies.  With the approach that we have been developing, we engage a broad range of stakeholders in a simple, iterative process in which they reveal what they believe and why.

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