On the recent Pew poll contrasting differences as to the “very big” problems we face today

An online news item appearing on 15 October 2018 proclaims that “Americans don’t just disagree on the issues. They disagree on what the issues are.” The article, by Dylan Scott on the Vox website, reports on a poll conducted by the Pew Research Center, involving registered voters in the U.S., between 24 September and 7 October. Polarizing disagreement is a recurring theme in the world, and keeping the tension up sells ads, so it is not surprising to see the emphasis in both the article and in the Pew report on major differences in people’s perceptions of what counts as a “very big” problem in the U.S. today. But a closer look at the data gives hope for finding ways to communicate across barriers that may look more significant than they actually are.

There’s no mention in the article of the sampling error, uncertainty, or confidence level, but the Pew site indicates that, overall, sampling error is 1.5%. But the Vox article mentions only the total sample size and fails to say that the registered voter portion of the respondents is smaller by a couple of thousand. Further, the sampling error jumps up to 2.6% for respondents indicating support for a Republican candidate, and to 2.3% for respondents supporting a Democrat. Again, the differences being played up are quite large, so there’s little risk of making too much out of a small difference. It’s good to know just how much of a difference makes a difference, though.

That said, neither Pew nor the Vox story mentions the very strong agreement between the different groups supporting opposing party candidates when the focus is on the relative magnitudes of agreement on aligned issues. Survey research typically focuses, of course, on percentages of responses to individual questions. Only measurement geeks like me wonder whether questions addressing a common theme could be related in a way that might convey more information. My curiosity was piqued, even though it is impossible to properly evaluate a model of this kind from the mere summary percentages. I knew if I found any correspondences they might just be accidents or coincidences, but I wanted to see what would happen.

So I typed up the text of the 18 issues concerning the seriousness of the problems being confronted in the US today, along with the percentages of registered voters saying each is a “very big” problem today. I put it all into SPSS and made a few technical checks to see if any major problems of interpretation would emerge from the nonlinear and ordinal percentages. The plots and correlations I did indicated that the same general results could be inferred from both the Pew percentages and their logit transformations.

While I was looking at a scatter plot of the Republican vs Democrat agreement percentages I noticed something interesting. I had been wondering if perhaps the striking differences in the groups’ willingness to say problems were serious might be a matter of relative emphases. Might the Republican supporters be less willing to find anything a big problem, but to nonetheless rank the issues in the same order as the Democrat supporters? This is, after all, exactly the kind of pattern commonly found in data from various surveys, assessments, and tests. No matter whether a respondent scores low overall, or scores high, the relative order of things stays the same.

Now, this is true in the kind of data I work with because considerable care is invested in composing questions that are intended to hang together like that. The idea is to deliberately vary the agreeability or difficulty of the questions so they all tap the same basic construct and demonstrably measure the same thing. When these kind of data are obtained, different questions measuring the same thing can be asked of different people without compromising the unit of measurement. That is, each different examinee or respondent can answer a unique set of questions and still have a measure comparable with anyone else’s. Like I said, this does not just happen by itself, but has to come about through a careful process of design and calibration. But the basic principles are well-established as being of longstanding and proven value across wide areas of research and practice.

So I was wondering if there might be one or more subsets of questions in the Pew data that would define the same problem magnitude dimension for supporters of both Republican and Democratic candidates. And as soon as I looked at the scatterplot of the percentages from the two groups, I saw that yes, indeed, there appeared to be four groups of issues that lined up along shared slopes. A color-coded version of that plot is in Figure 1.

The one statistical inference problem that emerged in examining these ordinal data concerns the yellow dot that is lowest and furthest to the left. At 8% agreement from the Republican supporters it was pulled away from the linear relation further than the other correspondences. When transformed into a log-odds unit, that single problematic difference lines up well with the other yellow dots further to the right.

The identity line in the figure shows where exact agreement between the two groups would be. That line marks out the connection between the same percentages of respondents agreeing an issue is a “very big” problem. We can see that the three green dots very nearly fall on that identity line. Just below them is a row of blue dots almost parallel with the identity line. Then there’s a third row of yellow dots further down, indicating more absolute disagreement between the two groups on these issues, but also showing a quite strong agreement as to their relative magnitudes within that group. Finally, there is another, red, line of dots in the lower right corner of the figure that marks out a more extreme range of absolute disagreement, but is also quite parallel to the identity line.

Figure 1 Initial plot of Republican vs Democrat Percentages agreement as to “Very Big” problems

Figures 2-5 below illustrate each of these groups of issues separately, giving further information on the problems and showing the regression lines and correlations for each contrast. The same colors have been retained to aid in seeing which groups of issues in Figure 1 are being shown.

The four areas of problems seem to me to correspond to issues of perceived major threats (Figure 2), accountability and access issues (Figure 3), equal opportunity issues (Figure 4), and systemic problems (Figure 5). Each of these content areas could be explored conceptually and qualitatively to assess whether some initial sense of a measured construct can be formed. If the by-person individual response data could be analyzed for fit to a proper measurement model, a much better job of determining the presence of invariant structure could be done.

But even without undertaking that work, these results already suggest a basis for productive conversations between the supposedly polarized groups. To start from the low-hanging fruit, the three problems the two groups agree on to within a couple of sampling errors (Figure 2) present topics of common agreement. Both Democrats and Republicans identify violent crime, the federal budget deficit, and drug addiction as matters of equally shared concern. The point is not that these are the highest rated problems for either group, but, rather, that they agree within the limits of statistical precision as to the extent that these are “very big” problems. It may be that setting shared priorities for addressing these problems could ground new relationships in that experience of having accomplished something productive together.

This new approach to building social capital might then proceed by taking up progressively more difficult areas of disagreement as to what “very big” problems are. Even though Republicans rate each area as less likely to be a “very big” problem, within each of the four groups of issues, they agree with Democrats as to their relative magnitudes. News like this might not sell a lot of ads, but it does offer hope for finding new ways of approaching relationships and crossing divides.

Figure 2.Republican vs Democrat areas of agreement as to “Very Big” problems

Figure 3 Republican vs Democrat areas of some disagreement as to “Very Big” problems

Figure 4 Republican vs Democrat areas of marked disagreement as to “Very Big” problems

Figure 5 Republican vs Democrat areas of fundamental disagreement as to “Very Big” problems

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

With Reich in spirit, but with a different sense of the problem and its solution

In today’s editorial in the San Francisco Chronicle, Robert Reich seeks some way of defining a solution to the pressing problems of how globalization and technological changes have made American workers less competitive. He rightly says that “reversing the scourge of widening inequality requires reversing the upward distributions [of income] within the rules of the market, and giving average people the bargaining power they need to get a larger share of the gains from growth.”

But Reich then says that the answer to this problem lies in politics, not economics. As I’ve pointed out before in this blog, focusing on marshaling political will is part of the problem, not part of the solution. Historically, politicians do not lead, they follow. As is demonstrated across events as diverse as the Arab Spring and the Preemption Act of 1841, mass movements of people have repeatedly demanded ways of cutting through the Gordian knots of injustice. And just as the political “leadership” across the Middle East and in the early U.S. dragged its feet, obstructed, and violently opposed change until it was already well underway, so, too, will that pattern repeat itself again in the current situation of inequitable income distribution.

The crux of the problem is that no one can give average people anything, not freedom (contra Dylan’s line in Blowin’ in the Wind about “allowing” people to be free) and certainly not a larger share of the gains from growth. As the old saying goes, you can lead a horse to water, but you can’t make it drink. People have to take what’s theirs. They have to want it, they have to struggle for it, and they have to pay for it, or they cannot own it and it will never be worth anything to them.

It is well known that a lack of individual property rights doomed communism and socialism because when everything is owned collectively by everyone, no one takes responsibility for it. The profit motive has the capacity to drive people to change things. The problem is not in profit itself. If birds and bees and trees and grasses did not profit from the sun, soil, and rain, there would be no life. The problem is in finding how to get a functional, self-sustaining economic ecology off the ground, not in unrealistically trying to manipulate and micromanage every detail.

The fundamental relevant characteristic of the profits being made today from intellectual property rights is that our individual rights to our own human and social capital are counter-productively restricted and undeveloped. How can it be that no one has any idea how much literacy or health capital they have, or what it is worth?! We have a metric system that tells us how much real estate and manufactured capital we own, and we can price it. But despite the well-established scientific facts of decades of measurement science research and practice, none of us can say, “I own x number of shares of stock in intellectual, literacy, or community capital, that have a value of x dollars in today’s market.” We desperately need an Intangible Assets Metric System, and the market rules, roles, and responsibilities that will make it impossible to make a profit while destroying human, social, and natural capital.

In this vein, what Reich gets absolutely correct is hidden inside his phrase, “within the rules of the market.” As I’ve so often repeated in this blog, capitalism is not inherently evil; it is, rather, unfinished. The real evil is in prolonging the time it takes to complete it. As was so eloquently stated by Miller and O’Leary (2007, p. 710):

“Markets are not spontaneously generated by the exchange activity of buyers and sellers. Rather, skilled actors produce institutional arrangements, the rules, roles and relationships that make market exchange possible. The institutions define the market, rather than the reverse.”

We have failed to set up the institutional arrangements needed to define human, social, and natural capital markets. The problem is that we cannot properly manage three of the four major forms of capital (human, social, and natural, with the fourth being manufactured/property) because we do not measure them in a common language built into scientifically, economically, legally and financially accountable titles, deeds, and other instruments.

And so, to repeat another one of my ad nauseum broken record nostrums, the problem is the problem. As long as we keep defining problems in the way we always have, as matters of marshalling political will, we will inadvertently find ourselves contributing more to prolonging tragic and needless human suffering, social discontent, and environmental degradation.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-734.

Comments on the New ANSI Human Capital Investor Metrics Standard

The full text of the proposed standard is available here.

It’s good to see a document emerge in this area, especially one with such a broad base of support from a diverse range of stakeholders. As is stated in the standard, the metrics defined in it are a good place to start and in many instances will likely improve the quality and quantity of the information made available to investors.

There are several issues to keep in mind as the value of standards for human capital metrics becomes more widely appreciated. First, in the context of a comprehensively defined investment framework, human capital is just one of the four major forms of capital, the other three being social, natural, and manufactured (Ekins, 1992; Ekins, Dresden, and Dahlstrom, 2008). To ensure as far as possible the long term stability and sustainability of their profits, and of the economic system as a whole, investors will certainly want to expand the range of the available standards to include social and natural capital along with human capital.

Second, though we manage what we measure, investment management is seriously compromised by having high quality scientific measurement standards only for manufactured capital (length, weight, volume, temperature, energy, time, kilowatts, etc.). Over 80 years of research on ability tests, surveys, rating scales, and assessments has reached a place from which it is prepared to revolutionize the management of intangible forms of capital (Fisher, 2007, 2009a, 2009b, 2010, 2011a, 2011b; Fisher & Stenner, 2011a, 2011b; Wilson, 2011; Wright, 1999). The very large reductions in transaction costs effected by standardized metrics in the economy at large (Barzel, 1982; Benham and Benham, 2000) are likely to have a similarly profound effect on the economics of human, social, and natural capital (Fisher, 2011a, 2012a, 2012b).

The potential for dramatic change in the conceptualization of metrics is most evident in the proposed standard in the sections on leadership quality and employee engagement. For instance, in the section on leadership quality, it is stated that “Investors will be able to directly compare all organizations that are using the same vendor’s methodology.” This kind of dependency should not be allowed to stand as a significant factor in a measurement standard. Properly constructed and validated scientific measures, such as those that have been in wide use in education, psychology and health care for several decades (Andrich, 2010; Bezruzcko, 2005; Bond and Fox, 2007; Fisher and Wright, 1994; Rasch, 1960; Salzberger, 2009; Wright, 1999), are equated to a common unit. Comparability should never depend on which vendor is used. Rather, any instrument that actually measures the construct of interest (leadership quality or employee engagement) should do so in a common unit and within an acceptable range of error. “Normalizing” measures for comparability, as is suggested in the standard, means employing psychometric methods that are 50 years out of date and that are far less rigorous and practical than need be. Transparency in measurement means looking through the instrument to the thing itself. If particular instruments color or reshape what is measured, or merely change the meaning of the numbers reported, then the integrity of the standard as a standard should be re-examined.

Third, for investments in human capital to be effectively managed, each distinct aspect of it (motivations, skills and abilities, health) needs to be measured separately, just as height, weight, and temperature are. New technologies have already transformed measurement practices in ways that make the necessary processes precise and inexpensive. Of special interest are adaptively administered precalibrated instruments supporting mass customized—but globally comparable—measures (for instance, see the examples at http://blog.lexile.com/tag/oasis/ and that were presented at the recent Pearson Global Research Conference in Fremantle, Australia http://www.pearson.com.au/marketing/corporate/pearson_global/default.html; also see Wright and Bell 1984, Lunz, Bergstrom, and Gershon, 1994, Bejar, et al., 2003).

Fourth, the ownership of human capital needs clarification and legal status. If we consider each individual to own their abilities, health, and motivations, and to be solely responsible for decisions made concerning the disposition of those properties, then, in accord with their proven measured amounts of each type of human capital, everyone ought to have legal title to a specific number of shares or credits of each type. This may transform employment away from wage-based job classification compensation to an individualized investment-based continuous quality improvement platform. The same kind of legal titling system will, of course, need to be worked out for social and natural capital, as well.

Fifth, given scientific standards for each major form of capital, practical measurement technologies, and legal title to our shares of capital, we will need expanded financial accounting standards and tools for managing our individual and collective investments. Ongoing research and debates concerning these standards and tools (Siegel and Borgia, 2006; Young and Williams, 2010) have yet to connect with the larger scientific, economic, and legal issues raised here, but developments in this direction should be emerging in due course.

Sixth, a number of lingering moral, ethical and political questions are cast in a new light in this context. The significance of individual behaviors and decisions is informed and largely determined by the context of the culture and institutions in which those behaviors and decisions are executed. Many of the morally despicable but not illegal investment decisions leading to the recent economic downturn put individuals in the position of either setting themselves apart and threatening their careers or doing what was best for their portfolios within the limits of the law. Current efforts intended to devise new regulatory constraints are misguided in focusing on ever more microscopically defined particulars. What is needed is instead a system in which profits are contingent on the growth of human, social, and natural capital. In that framework, legal but ultimately unfair practices would drive down social capital stock values, counterbalancing ill-gotten gains and making them unprofitable.

Seventh, the International Vocabulary of Measurement, now in its third edition (VIM3), is a standard recognized by all eight international standards accrediting bodies (BIPM, etc.). The VIM3 (http://www.bipm.org/en/publications/guides/vim.html) and forthcoming VIM4 are intended to provide a uniform set of concepts and terms for all fields that employ measures across the natural and social sciences. A new dialogue on these issues has commenced in the context of the International Measurement Confederation (IMEKO), whose member organizations are the weights and standards measurement institutes from countries around the world (Conference note, 2011). The 2012 President of the Psychometric Society, Mark Wilson, gave an invited address at the September 2011 IMEKO meeting (Wilson, 2011), and a member of the VIM3 editorial board, Luca Mari, is invited to speak at the July, 2012 International Meeting of the Psychometric Society. I encourage all interested parties to become involved in efforts of these kinds in their own fields.

References

Andrich, D. (2010). Sufficiency and conditional estimation of person parameters in the polytomous Rasch model. Psychometrika, 75(2), 292-308.

Barzel, Y. (1982). Measurement costs and the organization of markets. Journal of Law and Economics, 25, 27-48.

Bejar, I., Lawless, R. R., Morley, M. E., Wagner, M. E., Bennett, R. E., & Revuelta, J. (2003, November). A feasibility study of on-the-fly item generation in adaptive testing. The Journal of Technology, Learning, and Assessment, 2(3), 1-29; http://ejournals.bc.edu/ojs/index.php/jtla/article/view/1663.

Benham, A., & Benham, L. (2000). Measuring the costs of exchange. In C. Ménard (Ed.), Institutions, contracts and organizations: Perspectives from new institutional economics (pp. 367-375). Cheltenham, UK: Edward Elgar.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Conference note. (2011). IMEKO Symposium: August 31- September 2, 2011, Jena, Germany. Rasch Measurement Transactions, 25(1), 1318.

Ekins, P. (1992). A four-capital model of wealth creation. In P. Ekins & M. Max-Neef (Eds.), Real-life economics: Understanding wealth creation (pp. 147-155). London: Routledge.

Ekins, P., Dresner, S., & Dahlstrom, K. (2008). The four-capital method of sustainable development evaluation. European Environment, 18(2), 63-80.

Fisher, W. P., Jr. (2007). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009a). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Fisher, W. P.. Jr. (2009b). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P.. Jr. (2010). Rasch, Maxwell’s method of analogy, and the Chicago tradition. In G. Cooper (Chair), https://conference.cbs.dk/index.php/rasch/Rasch2010/paper/view/824. Probabilistic models for measurement in education, psychology, social science and health: Celebrating 50 years since the publication of Rasch’s Probabilistic Models.., University of Copenhagen School of Business, FUHU Conference Centre, Copenhagen, Denmark.

Fisher, W. P., Jr. (2011a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011b). Measurement, metrology and the coordination of sociotechnical networks. In  S. Bercea (Chair), New Education and Training Methods. International Measurement Confederation (IMEKO), http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24491/ilm1-2011imeko-017.pdf, Jena, Germany.

Fisher, W. P., Jr. (2012a). Measure local, manage global: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. in press). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012b). What the world needs now: A bold plan for new standards. Standards Engineering, 64, in press.

Fisher, W. P., Jr., & Stenner, A. J. (2011a). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Retrieved 25 October 2011, from National Science Foundation: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Stenner, A. J. (2011b). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

Fisher, W. P., Jr., & Wright, B. D. (Eds.). (1994). Applications of probabilistic conjoint measurement. International Journal of Educational Research, 21(6), 557-664.

Lunz, M. E., Bergstrom, B. A., & Gershon, R. C. (1994). Computer adaptive testing. International Journal of Educational Research, 21(6), 623-634.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Salzberger, T. (2009). Measurement in marketing research: An alternative framework. Northampton, MA: Edward Elgar.

Siegel, P., & Borgia, C. (2006). The measurement and recognition of intangible assets. Journal of Business and Public Affairs, 1(1).

Wilson, M. (2011). The role of mathematical models in measurement: A perspective from psychometrics. In L. Mari (Chair), Plenary lecture. International Measurement Confederation (IMEKO), http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24178/ilm1-2011imeko-005.pdf, Jena, Germany.

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D., & Bell, S. R. (1984, Winter). Item banks: What, why, how. Journal of Educational Measurement, 21(4), 331-345 [http://www.rasch.org/memo43.htm].

Young, J. J., & Williams, P. F. (2010, August). Sorting and comparing: Standard-setting and “ethical” categories. Critical Perspectives on Accounting, 21(6), 509-521.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Proposed U.S. Presidential Candidate Stump Speech

Over the course of our history, we have gotten a lot of things right in this country. Our political and economic principles and practices, not to speak of our technological innovations, have been models for countries the world over. The rest of the world has looked to us for leadership for a long time, and continues to do so.

Though some say our day in the sun might be over, I say we’ve hardly begun. We have new things to show the world. The problems we are facing as a nation right now have not come about because of flaws or failings in our basic principles. Those problems have come about because we have not yet creatively applied those principles in new ways, in new areas of our lives.

We have built our democracy and our economy on the ideas of equal rights and fair play, so that everyone has a chance to get into the game and make a place for themselves. Because of the way we have invested in these ideas over the last 235 years, this country has made big gains in bringing higher standards of living to more and more of our citizens, and to the citizens of countries on every continent. Along the way, there have been times when we’ve stumbled, but we’ve always picked ourselves back up and moved on to reach even higher standards than before.

We’ve been stumbling again here over these last few years. Though we continue to succeed with creative and innovative ideas in some areas, the world is changing. It isn’t enough for us to just react to the changes going on around us, or to resist those changes. We need to initiate changes of our own. Creating the future lets us predict it, lets us own it. Let me tell you about my vision of how we can create a new future together, a future that we can all own a piece of.

We have known for a long time that the richness of our lives depends on far more than the mere accumulation of material things. But despite that, the ongoing economic crisis has come about in large part precisely because we systematically put too much weight on material things in gauging our quality of life. But real wealth–and we all know this–the things that really make life worth living are not measured by any of the numbers that appear in the financial pages’ stock and economic indexes.

So efforts have been made to come up with numbers that will rise and fall with changes in our overall quality of life. New measures of real wealth, genuine progress, or happiness have been proposed. Many of us invest our retirement funds in stock indexes tied to socially responsible or environmentally sustainable corporate behaviors.

These are all steps in the right direction. But they fall short of what we need. More importantly, they fall short of what’s possible, and what’s already proven. Advances made in the social sciences over the last 50 years and more are setting the stage for a whole new array of exciting opportunities. It’s time to move these developments out of the lab and bring them to market. For instance, instead of relying on traditional statistics summarizing what’s going on at a high level, we need new measures that help us individually manage our investments in our own resources.

We say we manage what we measure, but, as I’ve already noted, we don’t have systems for measuring what’s really important in life. Are our skills, health, trustworthiness, and environmental quality really as important to us as we say they are? It would be natural to think, if they are that important, we would know how much of each of them we have and what they are worth. We ought to have ways of measuring these things, showing how much we each own, and knowing what it’s all worth. But we don’t.

Without those measures, we can’t effectively manage our own stocks of the resources most valuable to the quality of our lives. If we don’t know where we stand relative to one another or relative to where we were last week or last year, then we lack information vital to knowing how to move forward. And if we don’t know as individuals how to move forward, then we don’t know as a nation. If we do know as individuals where we stand and how to move forward, then we will also know as communities, and as managers in firms, classrooms, clinics, and hospitals.

The role of government in our lives is supposed to be to make things easier. And so to make it easier for everyone to manage the full range of the resources they have available to them, I now propose a new array of initiatives to be undertaken by the National Science Foundation, the National Institute for Standards and Technology, and the National Institutes of Health. These initiatives will focus on the research and education programs we need to create a new set of measurement standards, a kind of metric system that will give us the meaningful and precise numbers we need to manage the sources of our real wealth.

I will furthermore propose new legislation establishing an Intangible Assets Metric System as the legally binding terms for expressing the sources of real wealth in our lives. This law, when passed, as I’m sure it will be, will also establish each individual’s right to the free and clear ownership of their shares of human, social, and natural capital. Nothing is more important to the future of our nation, morally and economically, than each of us having a clear understanding of the value and worth of our reading, writing and math abilities, our health, our social relationships, and our environmental quality.

My administration will also reach out to industries and standards organizations of all kinds, but especially in economics, finance and accounting, to seek new creative ways for applying these measurement standards in managing our resources. I will also implement a new executive order establishing a wide range of new economic incentives designed to encourage investment in information systems for managing the new metrics in personalized accounts.

This series of initiatives will enable us to harmonize our efforts in new ways. We all know we can accomplish more working together as a team than we can alone. A new system of scientific, legal, and financial tools for managing our real wealth will make us a better team than ever. With these tools we will once again assert our leadership as innovators on a global scale, keeping the dream of a better life alive.

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For more on the science behind these ideas, and their potential applications, see previous posts in this blog, and the following:

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Fisher, W. P., Jr. (2009, November 19). Draft legislation on development and adoption of an intangible assets metric system. Retrieved 6 January 2011, from https://livingcapitalmetrics.wordpress.com/2009/11/19/draft-legislation/.

Fisher, W. P.. Jr. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf) Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2010, 30 September). Distinguishing between consistency and error in reliability coefficients: Improving the estimation and interpretation of information on measurement precision. LivingCapitalMetrics.com, Sausalito, California. Social Science Research Network [Online]. Available: http://ssrn.com/abstract=1685556 .

Fisher, W. P., Jr. (2010, 22 November). Meaningfulness, measurement, value seeking, and the corporate objective function: An introduction to new possibilities., LivingCapitalMetrics.com, Sausalito, California. Social Science Research Network [Online] (http://ssrn.com/abstract=1713467).

Fisher, W. P., Jr. (2010). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics: Conference Series, 238(1), http://iopscience.iop.org/1742-6596/238/1/012016/pdf/1742-6596_238_1_012016.pdf.

Fisher, W. P., Jr. (2010). Statistics and measurement: Clarifying the differences. Rasch Measurement Transactions, 23(4), 1229-1230 [http://www.rasch.org/rmt/rmt234.pdf].

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN millennium development goals project. LivingCapitalMetrics.com, Sausalito, California. Social Science Research Network [Online]. (http://ssrn.com/abstract=1739386).

Fisher, W. P., Jr. (2011). Stochastic and historical resonances of the unit in physics and psychometrics. Measurement: Interdisciplinary Research & Perspectives, 9, 46-50.

Fisher, W. P., Jr. (2012). Measure local, manage global: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (in press). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012). What the world needs now: A bold plan for new standards. Standards Engineering, in press.

Fisher, W. P., Jr., & Burton, E. (2010). Embedding measurement within existing computerized data systems: Scaling clinical laboratory and medical records heart failure data to predict ICU admission. Journal of Applied Measurement, 11(2), 271-287.

Fisher, W. P., Jr., Elbaum, B., & Coulter, W. A. (2012). Construction and validation of two parent-report scales for the evaluation of early intervention programs. Journal of Applied Measurement, 13, in press.

Fisher, W. P., Jr., Eubanks, R. L., & Marier, R. L. (1997). Equating the MOS SF36 and the LSU HSI physical functioning scales. Journal of Outcome Measurement, 1(4), 329-362.

Fisher, W. P., Jr., Harvey, R. F., & Kilgore, K. M. (1995). New developments in functional assessment: Probabilistic models for gold standards. NeuroRehabilitation, 5(1), 3-25.

Fisher, W. P., Jr., Harvey, R. F., Taylor, P., Kilgore, K. M., & Kelly, C. K. (1995, February). Rehabits: A common language of functional assessment. Archives of Physical Medicine and Rehabilitation, 76(2), 113-122.

Fisher, W. P., Jr., & Karabatsos, G. (2005). Fundamental measurement for the MEPS and CAHPS quality of care scales. In N. Bezruczko (Ed.), Rasch measurement in the health sciences (pp. 373-410). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr., & Stenner, A. J. (2011). Geometric and algebraic formulations of scientific laws: Mathematical principles for phenomenology. Journal of Phenomenological Psychology, in review.

Fisher, W. P., Jr., & Stenner, A. J. (2011, April). Integrating qualitative and quantitative research approaches via the phenomenological method. International Journal of Multiple Research Approaches, 5(1), 89-103.

Fisher, W. P., Jr., & Stenner, A. J. (2011). Making clear what something is:  Scientific law, construct validity and reliability in measuring reading ability. Psychological Methods, in review.

Fisher, W. P., Jr., & Stenner, A. J. (2011, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Retrieved 25 October 2011, from National Science Foundation: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Wright, B. D. (Eds.). (1994). Applications of probabilistic conjoint measurement (Special Issue). International Journal of Educational Research, 21(6), 557-664.

Heinemann, A. W., Fisher, W. P., Jr., & Gershon, R. (2006). Improving health care quality with outcomes management. Journal of Prosthetics and Orthotics, 18(1), 46-50 [http://www.oandp.org/jpo/library/2006_01S_046.asp] .

Solloway, S., & Fisher, W. P., Jr. (2007). Mindfulness in measurement: Reconsidering the measurable in mindfulness. International Journal of Transpersonal Studies, 26, 58-81 [http://www.transpersonalstudies.org/volume_26_2007.html].

Sumner, J., & Fisher, W. P., Jr. (2008). The moral construct of caring in nursing as communicative action: The theory and practice of a caring science. Advances in Nursing Science, 31(4), E19-E36.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Reimagining Capitalism Again, Part II: Scientific Credibility in Improving Information Quality

The previous posting here concluded with two questions provoked by a close consideration of a key passage in William Greider’s 2003 book, The Soul of Capitalism. First, how do we create the high quality, solid information markets need to punish and reward relative to ethical and sustainable human, social, and environmental values? Second, what can we learn from the way we created that kind of information for property and manufactured capital? There are good answers to these questions, answers that point in productive directions in need of wide exploration and analysis.

The short answer to both questions is that better, more scientifically rigorous measurement at the local level needs to be implemented in a context of traceability to universally uniform standards. To think global and act local simultaneously, we need an efficient and transparent way of seeing where we stand in the world relative to everyone else. Having measures expressed in comparable and meaningful units is an important part of how we think global while acting local.

So, for markets to punish and reward businesses in ways able to build human, social, and environmental value, we need to be able to price that value, to track returns on investments in it, and to own shares of it. To do that, we need a new intangible assets metric system that functions in a manner analogous to the existing metric system and other weights and measures standards. In the same way these standards guarantee high quality information on volume, weight, thermal units, and volts in grocery stores and construction sites, we need a new set of standards for human abilities, performances, and health; for social trust, commitment, and loyalty; and for the environment’s air and water processing services, fisheries, gene pools, etc.

Each industry needs an instrumentarium of tools and metrics that mediate relationships universally within its entire sphere of production and/or service. The obvious and immediate reaction to this proposal will likely be that this is impossible, that it would have been done by now if it was possible, and that anyone who proposes something like this is simply unrealistic, perhaps dangerously so. So, here we have another reason to add to those given in the June 8, 2011 issue of The Nation (http://www.thenation.com/article/161267/reimagining-capitalism-bold-ideas-new-economy) as to why bold ideas for a new economy cannot gain any traction in today’s political discourse.

So what basis in scientific authority might be found for this audacious goal of an intangible assets metric system? This blog’s postings offer multiple varieties of evidence and argument in this regard, so I’ll stick to more recent developments, namely, last week’s meeting of the International Measurement Confederation (IMEKO) in Jena, Germany. Membership in IMEKO is dominated by physicists, engineers, chemists, and clinical laboratorians who work in private industry, academia, and government weights and measures standards institutes.

Several IMEKO members past and present are involved with one or more of the seven or eight major international standards organizations responsible for maintaining and improving the metric system (the Systeme Internationale des Unites). Two initiatives undertaken by IMEKO and these standards organizations take up the matter at issue here concerning the audacious goal of standard units for human, social, and natural capital.

First, the recently released third edition of the International Vocabulary of Measurement (VIM, 2008) expands the range of the concepts and terms included to encompass measurement in the human and social sciences. This first effort was not well informed as to the nature of widely realized state of the art developments in measurement in education, health care, and the social sciences. What is important is that an invitation to further dialogue has been extended from the natural to the social sciences.

That invitation was unintentionally accepted and a second initiative advanced just as the new edition of the VIM was being released, in 2008. Members of three IMEKO technical committees (TC 1-7-13; those on Measurement Science, Metrology Education, and Health Care) cultivate a special interest in ideas on the human and social value of measurement. At their 2008 meeting in Annecy, France, I presented a paper (later published in revised form as Fisher, 2009) illustrating how, over the previous 50 years and more, the theory and practice of measurement in the social sciences had developed in ways capable of supporting convenient and useful universally uniform units for human, social, and natural capital.

The same argument was then advanced by my fellow University of Chicago alum, Nikolaus Bezruczko, at the 2009 IMEKO World Congress in Lisbon. Bezruczko and I both spoke at the 2010 TC 1-7-13 meeting in London, and last week our papers were joined by presentations from six of our colleagues at the 2011 IMEKO TC 1-7-13 meeting in Jena, Germany. Another fellow U Chicagoan, Mark Wilson, a long time professor in the Graduate School of Education at the University of California, Berkeley, gave an invited address contrasting four basic approaches to measurement in psychometrics, and emphasizing the value of methods that integrate substantive meaning with mathematical rigor.

Examples from education, health care, and business were then elucidated at this year’s meeting in Jena by myself, Bezruczko, Stefan Cano (University of Plymouth, England), Carl Granger (SUNY, Buffalo; paper presented by Bezruczko, a co-author), Thomas Salzberger (University of Vienna, Austria), Jack Stenner (MetaMetrics, Inc., Durham, NC, USA), and Gordon Cooper (University of Western Australia, Crawley, WA, Australia; paper presented by Fisher, a co-author).

The contrast between these presentations and those made by the existing IMEKO membership hinges on two primary differences in focus. The physicists and engineers take it for granted that all instrument calibration involves traceability to metrological reference standards. Dealing as they are with existing standards and physical or chemical materials that usually possess deterministically structured properties, issues of how to construct linear measures from ordinal observations never come up.

Conversely, the social scientists and psychometricians take it for granted that all instrument calibration involves evaluations of the capacity of ordinal observations to support the construction of linear measures. Dealing as they are with data from tests, surveys, and rating scale assessments, issues of how to relate a given instrument’s unit to a reference standard never come up.

Thus there is significant potential for mutually instructive dialogue between natural and social scientists in this context. Many areas of investigation in the natural sciences have benefited from the introduction of probabilistic concepts in recent decades, but there are perhaps important unexplored opportunities for the application of probabilistic measurement, as opposed to statistical, models. By taking advantage of probabilistic models’ special features, measurement in education and health care has begun to realize the benefit of broad generalizations of comparable units across grades, schools, tests, and curricula.

Though the focus of my interest here is in the capacity of better measurement to improve the efficiency of human, social, and natural capital markets, it may turn out that as many or more benefits will accrue in the natural sciences’ side of the conversation as in the social sciences’ side. The important thing for the time being is that the dialogue is started. New and irreversible mutual understandings between natural and social scientists have already been put on the record. It may happen that the introduction of a new supply of improved human, social, and natural capital metrics will help articulate the largely, as yet, unstated but nonetheless urgent demand for them.

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Reimagining Capitalism Again, Part I: Reflections on Greider’s Soul of Capitalism

In his 2003 book, The Soul of Capitalism, William Greider wrote, “If capitalism were someday found to have a soul, it would probably be located in the mystic qualities of capital itself” (p. 94). The recurring theme in the book is that the resolution of capitalism’s deep conflicts must grow out as organic changes from the roots of capitalism itself.

In the book, Greider quotes Innovest’s Michael Kiernan as suggesting that the goal has to be re-engineering the DNA of Wall Street (p. 119). He says the key to doing this is good reliable information that has heretofore been unavailable but which will make social and environmental issues matter financially. The underlying problems of exactly what solid, high quality information looks like, where it comes from, and how it is created are not stated or examined, but the point, as Kiernan says, is that “the markets are pretty good at punishing and rewarding.” The objective is to use “the financial markets as an engine of reform and positive change rather than destruction.”

This objective is, of course, the focus of multiple postings in this blog (see especially this one and this one). From my point of view, capitalism indeed does have a soul and it is actually located in the qualities of capital itself. Think about it: if a soul is a spirit of something that exists independent of its physical manifestation, then the soul of capitalism is the fungibility of capital. Now, this fungibility is complex and ambiguous. It takes its strength and practical value from the way market exchange are represented in terms of currencies, monetary units that, within some limits, provide an objective basis of comparison useful for rewarding those capable of matching supply with demand.

But the fungibility of capital can also be dangerously misconceived when the rich complexity and diversity of human capital is unjustifiably reduced to labor, when the irreplaceable value of natural capital is unjustifiably reduced to land, and when the trust, loyalty, and commitment of social capital is completely ignored in financial accounting and economic models. As I’ve previously said in this blog, the concept of human capital is inherently immoral so far as it reduces real human beings to interchangeable parts in an economic machine.

So how could it ever be possible to justify any reduction of human, social, and natural value to a mere number? Isn’t this the ultimate in the despicable inhumanity of economic logic, corporate decision making, and, ultimately, the justification of greed? Many among us who profess liberal and progressive perspectives seem to have an automatic and reactionary prejudice of this kind. This makes these well-intentioned souls as much a part of the problem as those among us with sometimes just as well-intentioned perspectives that accept such reductionism as the price of entry into the game.

There is another way. Human, social, and natural value can be measured and made manageable in ways that do not necessitate totalizing reduction to a mere number. The problem is not reduction itself, but unjustified, totalizing reduction. Referring to all people as “man” or “men” is an unjustified reduction dangerous in the way it focuses attention only on males. The tendency to think and act in ways privileging males over females that is fostered by this sense of “man” shortchanges us all, and has happily been largely eliminated from discourse.

Making language more inclusive does not, however, mean that words lose the singular specificity they need to be able to refer to things in the world. Any given word represents an infinite population of possible members of a class of things, actions, and forms of life. Any simple sentence combining words into a coherent utterance then multiplies infinities upon infinities. Discourse inherently reduces multiplicities into texts of limited lengths.

Like any tool, reduction has its uses. Also like any tool, problems arise when the tool is allowed to occupy some hidden and unexamined blind spot from which it can dominate and control the way we think about everything. Critical thinking is most difficult in those instances in which the tools of thinking themselves need to be critically evaluated. To reject reduction uncritically as inherently unjustified is to throw the baby out with the bathwater. Indeed, it is impossible to formulate a statement of the rejection without simultaneously enacting exactly what is supposed to be rejected.

We have numerous ready-to-hand examples of how all reduction has been unjustifiably reduced to one homogenized evil. But one of the results of experiments in communal living in the 1960s and 1970s, as well as of the fall of the Soviet Union, was the realization that the centralized command and control of collectively owned community property cannot compete with the creativity engendered when individuals hold legal title to the fruits of their labors. If individuals cannot own the results of the investments they make, no one makes any investments.

In other words, if everything is owned collectively and is never reduced to individually possessed shares that can be creatively invested for profitable returns, then the system is structured so as to punish innovation and reward doing as little as possible. But there’s another way of thinking about the relation of the collective to the individual. The living soul of capitalism shows itself in the way high quality information makes it possible for markets to efficiently coordinate and align individual producers’ and consumers’ collective behaviors and decisions. What would happen if we could do that for human, social, and natural capital markets? What if “social capitalism” is more than an empty metaphor? What if capital institutions can be configured so that individual profit really does become the driver of socially responsible, sustainable economics?

And here we arrive at the crux of the problem. How do we create the high quality, solid information markets need to punish and reward relative to ethical and sustainable human, social, and environmental values? Well, what can we learn from the way we created that kind of information for property and manufactured capital? These are the questions taken up and explored in the postings in this blog, and in my scientific research publications and meeting presentations. In the near future, I’ll push my reflection on these questions further, and will explore some other possible answers to the questions offered by Greider and his readers in a recent issue of The Nation.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Debt, Revenue, and Changing the Way Washington Works: The Greatest Entrepreneurial Opportunity of Our Time

“Holding the line” on spending and taxes does not make for a fundamental transformation of the way Washington works. Simply doing less of one thing is just a small quantitative change that does nothing to build positive results or set a new direction. What we need is a qualitative metamorphosis akin to a caterpillar becoming a butterfly. In contrast with this beautiful image of natural processes, the arguments and so-called principles being invoked in the sham debate that’s going on are nothing more than fights over where to put deck chairs on the Titanic.

What sort of transformation is possible? What kind of a metamorphosis will start from who and where we are, but redefine us sustainably and responsibly? As I have repeatedly explained in this blog, my conference presentations, and my publications, with numerous citations of authoritative references, we already possess all of the elements of the transformation. We have only to organize and deploy them. Of course, discerning what the resources are and how to put them together is not obvious. And though I believe we will do what needs to be done when we are ready, it never hurts to prepare for that moment. So here’s another take on the situation.

Infrastructure that supports lean thinking is the name of the game. Lean thinking focuses on identifying and removing waste. Anything that consumes resources but does not contribute to the quality of the end product is waste. We have enormous amounts of wasteful inefficiency in many areas of our economy. These inefficiencies are concentrated in areas in which management is hobbled by low quality information, where we lack the infrastructure we need.

Providing and capitalizing on this infrastructure is The Greatest Entrepreneurial Opportunity of Our Time. Changing the way Washington (ha! I just typed “Wastington”!) works is the same thing as mitigating the sources of risk that caused the current economic situation. Making government behave more like a business requires making the human, social, and natural capital markets more efficient. Making those markets more efficient requires reducing the costs of transactions. Those costs are determined in large part by information quality, which is a function of measurement.

It is often said that the best way to reduce the size of government is to move the functions of government into the marketplace. But this proposal has never been associated with any sense of the infrastructural components needed to really make the idea work. Simply reducing government without an alternative way of performing its functions is irresponsible and destructive. And many of those who rail on and on about how bad or inefficient government is fail to recognize that the government is us. We get the government we deserve. The government we get follows directly from the kind of people we are. Government embodies our image of ourselves as a people. In the US, this is what having a representative form of government means. “We the people” participate in our society’s self-governance not just by voting, writing letters to congress, or demonstrating, but in the way we spend our money, where we choose to live, work, and go to school, and in every decision we make. No one can take a breath of air, a drink of water, or a bite of food without trusting everyone else to not carelessly or maliciously poison them. No one can buy anything or drive down the street without expecting others to behave in predictable ways that ensure order and safety.

But we don’t just trust blindly. We have systems in place to guard against those who would ruthlessly seek to gain at everyone else’s expense. And systems are the point. No individual person or firm, no matter how rich, could afford to set up and maintain the systems needed for checking and enforcing air, water, food, and workplace safety measures. Society as a whole invests in the infrastructure of measures created, maintained, and regulated by the government’s Department of Commerce and the National Institute for Standards and Technology (NIST). The moral importance and the economic value of measurement standards has been stressed historically over many millennia, from the Bible and the Quran to the Magna Carta and the French Revolution to the US Constitution. Uniform weights and measures are universally recognized and accepted as essential to fair trade.

So how is it that we nonetheless apparently expect individuals and local organizations like schools, businesses, and hospitals to measure and monitor students’ abilities; employees’ skills and engagement; patients’ health status, functioning, and quality of care; etc.? Why do we not demand common currencies for the exchange of value in human, social, and natural capital markets? Why don’t we as a society compel our representatives in government to institute the will of the people and create new standards for fair trade in education, health care, social services, and environmental management?

Measuring better is not just a local issue! It is a systemic issue! When measurement is objective and when we all think together in the common language of a shared metric (like hours, volts, inches or centimeters, ounces or grams, degrees Fahrenheit or Celsius, etc.), then and only then do we have the means we need to implement lean strategies and create new efficiencies systematically. We need an Intangible Assets Metric System.

The current recession in large part was caused by failures in measuring and managing trust, responsibility, loyalty, and commitment. Similar problems in measuring and managing human, social, and natural capital have led to endlessly spiraling costs in education, health care, social services, and environmental management. The problems we’re experiencing in these areas are intimately tied up with the way we formulate and implement group level decision making processes and policies based in statistics when what we need is to empower individuals with the tools and information they need to make their own decisions and policies. We will not and cannot metamorphose from caterpillar to butterfly until we create the infrastructure through which we each can take full ownership and control of our individual shares of the human, social, and natural capital stock that is rightfully ours.

We well know that we manage what we measure. What counts gets counted. Attention tends to be focused on what we’re accountable for. But–and this is vitally important–many of the numbers called measures do not provide the information we need for management. And not only are lots of numbers giving us low quality information, there are far too many of them! We could have better and more information from far fewer numbers.

Previous postings in this blog document the fact that we have the intellectual, political, scientific, and economic resources we need to measure and manage human, social, and natural capital for authentic wealth. And the issue is not a matter of marshaling the will. It is hard to imagine how there could be more demand for better management of intangible assets than there is right now. The problem in meeting that demand is a matter of imagining how to start the ball rolling. What configuration of investments and resources will start the process of bursting open the chrysalis? How will the demand for meaningful mediating instruments be met in a way that leads to the spreading of the butterfly’s wings? It is an exciting time to be alive.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Questions about measurement: If it is so important, why…?

If measurement is so important, why is measurement quality so uniformly low?

If we manage what we measure, why is measurement leadership virtually nonexistent?

If we can’t tell if things are getting better, staying the same, or getting worse without good metrics, why is measurement so rarely context-sensitive, focused, integrated, and interactive, as Dean Spitzer recommends it should be?

If quantification is valued for its rigor and convenience, why is no one demanding meaningful mappings of substantive, additive amounts of things measured on number lines?

If everyone is drowning in unmanageable floods of data why isn’t measurement used to reduce data volumes dramatically—and not only with no loss of information but with the addition of otherwise unavailable forms of information?

If learning and improvement are the order of the day, why isn’t anyone interested in the organizational and individual learning trajectories that are defined by hierarchies of calibrated items?

If resilient lean thinking is the way to go, why aren’t more measures constructed to retain their meaning and values across changes in item content?

If flexibility is a core value, why aren’t we adapting instruments to people and organizations, instead of vice versa?

If fair, just, and meaningful measurement is often lacking in judge-assigned performance assessments, why isn’t anyone estimating the consistency, and the leniency or harshness, of ratings—and removing those effects from the measures made?

If efficiency is valued, why does no one at all seem to care about adjusting measurement precision to the needs of the task at hand, so that time and resources are not wasted in gathering too much or too little data?

If it’s common knowledge that we can do more together than we can as individuals, why isn’t anyone providing the high quality and uniform information needed for the networked collective thinking that is able to keep pace with the demand for innovation?

Since the metric system and uniform product standards are widely recognized as essential to science and commerce, why are longstanding capacities for common metrics for human, social, and natural capital not being used?

If efficient markets are such great things, why isn’t anyone at all concerned about lubricating the flow of human, social, and natural capital by investing in the highest quality measurement obtainable?

If everyone loves a good profit, why aren’t we setting up human, social, and natural capital metric systems to inform competitive pricing of intangible assets, products, and services?

If companies are supposed to be organic entities that mature in a manner akin to human development over the lifespan, why is so little being done to conceive, gestate, midwife, and nurture living capital?

In short, if measurement is really as essential to management as it is so often said to be, why doesn’t anyone seek out the state of the art technology, methods, and experts before going to the trouble of developing and implementing metrics?

I suspect the answers to these questions are all the same. These disconnects between word and deed happen because so few people are aware of the technical advances made in measurement theory and practice over the last several decades.

For the deep background, see previous entries in this blog, various web sites (www.rasch.org, www.rummlab.com, www.winsteps.com, http://bearcenter.berkeley.edu/, etc.), and an extensive body of published work (Rasch, 1960; Wright, 1977, 1997a, 1997b, 1999a, 1999b; Andrich, 1988, 2004, 2005; Bond & Fox, 2007; Fisher, 2009, 2010; Smith & Smith, 2004; Wilson, 2005; Wright & Stone, 1999, 2004).

There is a wealth of published applied research in education, psychology, and health care (Bezruczko, 2005; Fisher & Wright, 1994; Masters, 2007; Masters & Keeves, 1999). To find more search Rasch and the substantive area of interest.

For applications in business contexts, there is a more limited number of published resources (ATP, 2001; Drehmer, Belohlav, & Coye, 2000; Drehmer & Deklava, 2001; Ludlow & Lunz, 1998; Lunz & Linacre, 1998; Mohamed, et al., 2008; Salzberger, 2000; Salzberger & Sinkovics, 2006; Zakaria, et al., 2008). I have, however, just become aware of the November, 2009, publication of what could be a landmark business measurement text (Salzberger, 2009). Hopefully, this book will be just one of many to come, and the questions I’ve raised will no longer need to be asked.

References

Andrich, D. (1988). Rasch models for measurement. (Vols. series no. 07-068). Sage University Paper Series on Quantitative Applications in the Social Sciences). Beverly Hills, California: Sage Publications.

Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Andrich, D. (2005). Georg Rasch: Mathematician and statistician. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (Vol. 3, pp. 299-306). Amsterdam: Academic Press, Inc.

Association of Test Publishers. (2001, Fall). Benjamin D. Wright, Ph.D. honored with the Career Achievement Award in Computer-Based Testing. Test Publisher, 8(2). Retrieved 20 May 2009, from http://www.testpublishers.org/newsletter7.htm#Wright.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Dawson, T. L., & Gabrielian, S. (2003, June). Developing conceptions of authority and contract across the life-span: Two perspectives. Developmental Review, 23(2), 162-218.

Drehmer, D. E., Belohlav, J. A., & Coye, R. W. (2000, Dec). A exploration of employee participation using a scaling approach. Group & Organization Management, 25(4), 397-418.

Drehmer, D. E., & Deklava, S. M. (2001, April). A note on the evolution of software engineering practices. Journal of Systems and Software, 57(1), 1-7.

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [Pre-press version available at http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

Ludlow, L. H., & Lunz, M. E. (1998). The Job Responsibilities Scale: Invariance in a longitudinal prospective study. Journal of Outcome Measurement, 2(4), 326-37.

Lunz, M. E., & Linacre, J. M. (1998). Measurement designs using multifacet Rasch modeling. In G. A. Marcoulides (Ed.), Modern methods for business research. Methodology for business and management (pp. 47-77). Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.

Masters, G. N. (2007). Special issue: Programme for International Student Assessment (PISA). Journal of Applied Measurement, 8(3), 235-335.

Masters, G. N., & Keeves, J. P. (Eds.). (1999). Advances in measurement in educational research and assessment. New York: Pergamon.

Mohamed, A., Aziz, A., Zakaria, S., & Masodi, M. S. (2008). Appraisal of course learning outcomes using Rasch measurement: A case study in information technology education. In L. Kazovsky, P. Borne, N. Mastorakis, A. Kuri-Morales & I. Sakellaris (Eds.), Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (Electrical And Computer Engineering Series) (pp. 222-238). Cambridge, UK: WSEAS.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Salzberger, T. (2000). An extended Rasch analysis of the CETSCALE – implications for scale development and data construction., Department of Marketing, University of Economics and Business Administration, Vienna (WU-Wien) (http://www2.wu-wien.ac.at/marketing/user/salzberger/research/wp_dataconstruction.pdf).

Salzberger, T. (2009). Measurement in marketing research: An alternative framework. Northampton, MA: Edward Elgar.

Salzberger, T., & Sinkovics, R. R. (2006). Reconsidering the problem of data equivalence in international marketing research: Contrasting approaches based on CFA and the Rasch model for measurement. International Marketing Review, 23(4), 390-417.

Smith, E. V., Jr., & Smith, R. M. (2004). Introduction to Rasch measurement. Maple Grove, MN: JAM Press.35.

Spitzer, D. (2007). Transforming performance measurement: Rethinking the way we measure and drive organizational success. New York: AMACOM.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116 [http://www.rasch.org/memo42.htm].

Wright, B. D. (1997a, June). Fundamental measurement for outcome evaluation. Physical Medicine & Rehabilitation State of the Art Reviews, 11(2), 261-88.

Wright, B. D. (1997b, Winter). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45, 52 [http://www.rasch.org/memo62.htm].

Wright, B. D. (1999a). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D. (1999b). Rasch measurement models. In G. N. Masters & J. P. Keeves (Eds.), Advances in measurement in educational research and assessment (pp. 85-97). New York: Pergamon.

Wright, B. D., & Stone, M. H. (1999). Measurement essentials. Wilmington, DE: Wide Range, Inc. [http://www.rasch.org/memos.htm#measess].

Wright, B. D., & Stone, M. H. (2004). Making measures. Chicago: Phaneron Press.

Zakaria, S., Aziz, A. A., Mohamed, A., Arshad, N. H., Ghulman, H. A., & Masodi, M. S. (2008, November 11-13). Assessment of information managers’ competency using Rasch measurement. iccit: Third International Conference on Convergence and Hybrid Information Technology, 1, 190-196 [http://www.computer.org/portal/web/csdl/doi/10.1109/ICCIT.2008.387].

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Review of Spitzer’s Transforming Performance Measurement

Everyone interested in practical measurement applications needs to read Dean R. Spitzer’s 2007 book, Transforming performance measurement: Rethinking the way we measure and drive organizational success (New York, AMACOM). Spitzer describes how measurement, properly understood and implemented, can transform organizational performance by empowering and motivating individuals. Measurement understood in this way moves beyond quick fixes and fads to sustainable processes based on a measurement infrastructure that coordinates decisions and actions uniformly throughout the organization.

Measurement leadership, Spitzer says, is essential. He advocates, and many organizations have instituted, the C-suite position of Chief Measurement Officer (Chapter 9). This person is responsible for instituting and managing the four keys to transformational performance measurement (Chapters 5-8):

• Context sets the tone by presenting the purpose of measurement as either negative (to inspect, control, report, manipulate) or positive (to give feedback, learn, improve).
• Focus concentrates attention on what’s important, aligning measures with the mission, strategy, and with what needs to be managed, relative to the opportunities, capacities, and skills at hand.
• Integration addresses the flow of measured information throughout the organization so that the covariations of different measures can be observed relative to the overall value created.
• Interactivity speaks to the inherently social nature of the purposes of measurement, so that it embodies an alignment with the business model, strategy, and operational imperatives.

Spitzer takes a developmental approach to measurement improvement, providing a Measurement Maturity Assessment in Chapter 12, and also speaking to the issues of the “living company” raised by Arie de Geus’ classic book of that title. Plainly, the transformative potential of performance measurement is dependent on the maturational complexity of the context in which it is implemented.

Spitzer clearly outlines the ways in which each of the four keys and measurement leadership play into or hinder transformation and maturation. He also provides practical action plans and detailed guidelines, stresses the essential need for an experimental attitude toward evaluating change, speaks directly to the difficulty of measuring intangible assets like partnership, trust, skills, etc., and shows appreciation for the value of qualitative data.

Transforming Performance Measurement is not an academic treatise, though all sources are documented, with the endnotes and bibliography running to 25 pages. It was written for executives, managers, and entrepreneurs who need practical advice expressed in direct, simple terms. Further, the book does not include any awareness of the technical capacities of measurement as these have been realized in numerous commercial applications in high stakes and licensure/certification testing over the last 50 years (Andrich, 2005; Bezruczko, 2005; Bond & Fox, 2007; Masters, 2007; Wilson, 2005). This can hardly be counted as a major criticism, since no books of this kind have yet to date been able to incorporate the often highly technical and mathematical presentations of advanced psychometrics.

That said, the sophistication of Spitzer’s conceptual framework and recommendations make them remarkably ready to incorporate insights from measurement theory, testing practice, developmental psychology, and the history of science. Doing so will propel the strategies recommended in this book into widespread adoption and will be a catalyst for the emerging re-invention of capitalism. In this coming cultural revolution, intangible forms of capital will be brought to life in common currencies for the exchange of value that perform the same function performed by kilowatts, bushels, barrels, and hours for tangible forms of capital (Fisher, 2009, 2010).

Pretty big claim, you say? Yes, it is. Here’s how it’s going to work.

• First, measurement leadership within organizations that implements policies and procedures that are context-sensitive, focused, integrated, and interactive (i.e., that have Spitzer’s keys in hand) will benefit from instruments calibrated to facilitate:
  • meaningful mapping of substantive, additive amounts of things measured on number lines;
  • data volume reductions on the order of 80-95% and more, with no loss of information;
  • organizational and individual learning trajectories defined by hierarchies of calibrated items;
  • measures that retain their meaning and values across changes in item content;
  • adapting instruments to people and organizations, instead of vice versa;
  • estimating the consistency, and the leniency or harshness, of ratings assigned by judges evaluating performance quality, with the ability to remove those effects from the performance measures made;
  • adjusting measurement precision to the needs of the task at hand, so that time and resources are not wasted in gathering too much or too little data; and
  • providing the high quality and uniform information needed for networked collective thinking able to keep pace with the demand for innovation.
• Second, measurement leadership sensitive to the four keys across organizations, both within and across industries, will find value in:
  • establishing industry-wide metrological standards defining common metrics for the expression of the primary human, social, and natural capital constructs of interest;
  • lubricating the flow of human, social, and natural capital in efficient markets broadly defined so as to inform competitive pricing of intangible assets, products, and services; and
  • new opportunities for determining returns on investments in human, community, and environmental resource management.
• Third, living companies need to be able to mature in a manner akin to human development over the lifespan. Theories of hierarchical complexity and developmental stage transitions that inform the rigorous measurement of cognitive and moral transformations (Dawson & Gabrielian, 2003) will increasingly find highly practical applications in organizational contexts.

Leadership of the kind described by Spitzer is needed not just to make measurement contextualized, focused, integrated, and interactive—and so productive at new levels of effectiveness—but to apply systematically the technical, financial, and social resources needed to realize the rich potentials he describes for the transformation of organizations and empowerment of individuals. Spitzer’s program surpasses the usual focus on centralized statistical analyses and reports to demand the organization-wide dissemination of calibrated instruments that measure in common metrics. The flexibility, convenience, and scientific rigor of instruments calibrated to measure in units that really add up fit the bill exactly. Here’s to putting tools that work in the hands of those who know what to do with them!

References

Andrich, D. (2005). Georg Rasch: Mathematician and statistician. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (Vol. 3, pp. 299-306). Amsterdam: Academic Press, Inc.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Dawson, T. L., & Gabrielian, S. (2003, June). Developing conceptions of authority and contract across the life-span: Two perspectives. Developmental Review, 23(2), 162-218.

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [Pre-press version available at http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf%5D.

Masters, G. N. (2007). Special issue: Programme for International Student Assessment (PISA). Journal of Applied Measurement, 8(3), 235-335.

Spitzer, D. (2007). Transforming performance measurement: Rethinking the way we measure and drive organizational success. New York: AMACOM.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Information and Leadership: New Opportunities for Advancing Strategy, Engaging Customers, and Motivating Employees

Or, What’s a Mathematical Model a Model Of, After All?
Or, How to Build Scale Models of Organizations and Use Them to Learn About Organizational Identity, Purpose, and Mission

William P. Fisher, Jr., Ph.D.

The greatest opportunity and most significant challenge to leadership in every area of life today is the management of information. So says Carol Bartz, CEO of Yahoo! in her entry in The Economist’s annual overview of world events, “The World in 2010.” Information can be both a blessing and a curse. The right information in the right hands at the right time is essential to effectiveness and efficiency. But unorganized and incoherent information can be worse than none at all. Too often leaders and managers are faced with deciding between gut instincts based in unaccountable intuitions and facts that are potentially seriously flawed, or that are merely presented in such overwhelming volumes as to be useless.

This situation is only going to get worse as information volumes continue to increase. The upside is that solutions exist, solutions that not only reduce data volume by factors as high as hundreds to one with no loss of information, but which also distinguish between merely apparent and really reliable information. What we have in these solutions are the means of following through on Carol Bartz’s information leadership warnings and recommendations.

Clearly communicating what matters, for instance, requires leaders to find meaning in new facts and the changing scene. They have to be able to use their vision of the organization, its mission, and its place in the world to tell what’s important and what isn’t, to put each event or opportunity in perspective. And what’s more is that the vision of the organization has to be dynamic. It, too, has to be able to change with the changing circumstances.

And this is where a whole new class of useful information solutions comes to bear. It may seem odd to say so, but leadership is fundamentally mathematical. You can begin to get a sense of what I mean in the ambiguity of the way leaders can be calculating. Making use of people’s skills and talents is a challenge that requires being able to assess facts and potentials in a way that intuitively gauges likelihoods of success. It is possible to lead, of course, without being manipulative; the point is that leadership requires an ability to envision and project an abstract heuristic ideal as a fundamental principle for focusing attention and separating the wheat from the chaff. A leader who dithers and wastes time and resources on irrelevancies is a contradiction in terms. An organization is supposed to have an identity, a purpose, and a mission in life independent of the local particulars of who its actual employees, customers, and suppliers are, and independent of the opportunities and challenges that arise in different times and places.

Of course, every organization is colored and shaped to some extent by every different person that comes into contact with it, and by the times and places it finds itself in. No one wants to feel like an interchangeable part in machine, but neither does anyone want to feel completely out of place, with no role to play. If an organization was entirely dependent on the particulars of who, what, when, and where, it’s status as a coherent organization with an identifiable presence would be compromised. So what we need is to find the right balance between the ideal and the real, the abstract and the concrete, and, as the philosopher Paul Ricoeur put it, between belonging and distanciation.

And indeed, scientists often note that no mathematical model ever holds in every detail in the real world. That isn’t what they’re intended to do, in fact. Mathematical models serve the purpose of being guides to creating meaningful, useful relationships. One of the leading lights of measurement theory, Georg Rasch, said it well over 50 years ago: models aren’t meant to be true, but to be useful.

Rasch accordingly also pointed out that, if we measure mass, force, and acceleration with enough precision, we see that even Newton’s laws of motion are not perfectly true. Measured to the nth decimal place, what we find is that observed amounts of mass, force, and acceleration form probability distributions that do indeed satisfy Newton’s laws. Even in classical physics, then, measurement models are best conceived probabilistically.

Over the last several decades, use of Rasch’s probabilistic measurement models in scaling tests, surveys, and assessments has grown exponentially. As has been explored at length in previous posts in this blog, most applications of Rasch’s models mistakenly treat them as statistical models, as so their real value and importance is missed. But even those actively engaged in using the models appropriately often do not engage with the basic question concerning what the model is a model of, in their particular application of it. The basic assumption seems to be that the model is a mathematical representation of relations between observations recorded in a data set, but this is an extremely narrow and unproductive point of view.

Let’s ask ourselves, instead, how we would model an organization. Why would we want to do that? We would want to do that for the same reasons we model anything, such as creating a safe and efficient way of experimenting with different configurations, and of coming to new understandings of basic principles. If we had a standard model of organizations of a certain type, or of organizations in a particular industry, we could use it to see how different variations on the basic structure and processes cause or are associated with different outcomes. Further, given that such models could be used to calibrate scales meaningfully measuring organizational development, industry-wide standards could be brought to bear in policy, decision making, and education, effecting new degrees of efficiency and effectiveness.

So, we’d previously said that the extent to which an organization finds its identity, realizes its purpose, and advances its mission (i.e., develops) is, within certain limits, a function of its capacity to be independent from local particulars. What we mean by this is that we expect employees to be able to perform their jobs no matter what day of the week it is, no matter who the customer is, no matter which particular instance of a product is involved, etc. Though no amount of skill, training, or experience can prepare someone for every possible contingency, people working in a given job description prepare themselves for a certain set of tasks, and are chosen by the organization for their capacities in that regard.

Similarly, we expect policies, job descriptions, work flows, etc. to function in similar fashions. Though the exact specifics of each employee’s abilities and each situation’s demands cannot be known in advance, enough is known that the defined aims will be achieved with high degrees of success. Of course, this is the point at which the interchangeability of employee ability and task difficulty can become demeaning and alienating. It will be important that we allow room for some creative play, and situate each level of ability along a continuum that allows everyone to see a developmental trajectory personalized to their particular strengths and needs.

So, how do we mathematically model the independence of the organization from its employees, policies, customers, and challenges, and scientifically evaluate that independence?

One way to begin is to posit that organizational development is equal to the differences between the abilities of the people employed, the efficiencies of the policies, alignments, and linkages implemented; and the challenges presented by the market. If we observe the abilities, efficiencies, and challenges in by means of a rating scale, the resulting model could be written as:

ln(P_moas/(1-P_moas)) = b_m – f_o – c_a – r_s

which hypothesizes that the natural logarithm of the response odds (the response probabilities divided by one minus themselves) is equal to the ability b of employee m minus the efficiency f of policy o minus the challenge c of market a minus the difficulty r of obtaining rating in category s. This model has the form of a multifaceted Rasch model (Linacre, 1989; others), used in academic research, rehabilitative functional assessments, and medical licensure testing.

What does it take for each of these model parameters to be independent of the others in the manner that we take for granted in actual practice? Can we frame our observations of the members of each facet in the model in ways that will clearly show us when we have failed to obtain the desired independence? Can we do that in a way that simultaneously provides us with a means for communicating information about individual employees, policies, and challenges efficiently in a common language?

Can that common language be expressed in words and numbers that capitalize on the independence of the model parameters and so mean the same thing across local particulars? Can we set up a system for checking and maintaining the meaning of the parameters over time? Can we build measures of employee abilities, policy efficiencies, and market challenges into our information systems in useful ways? Can we improve the overall quality, efficiency, and meaningfulness of our industry by collaborating with other firms, schools, non-profits, and government agencies in the development of reference standard metrics?

These questions all have the same answer: Yes, we can. These questions set the stage for understanding how effective leadership depends on effective information management. If, as Yahoo! CEO Bartz says, leadership has become more difficult in the age of blogospherical second-guessing and “opposition research,” why not tap all of that critical energy as a resource and put it to work figuring out what differences make a difference? If critics think they have important questions that need to be answered, the independence and consistency, or lack thereof, of their and others’ responses gives real heft to a “put-up-or-shut-up” criterion for distinguishing signal from noise.

This kind of a BS-detector supports leadership in two ways, by focusing attention on meaningful information, and by highlighting significant divergences from accepted opinion. The latter might turn out to be nothing more than exceptionally loud noise, but it might also signal something very important, a contrary opinion sensitive to special information available only from a particular perspective.

Bartz is right on, then, in saying that the central role of information in leadership has made listening and mentoring more important than ever. Modeling the organization and experimenting with it makes it possible to listen and mentor in completely new ways. Testing data for independent model parameters is akin to tuning the organization like an instrument. When independence is achieved, everything harmonizes. The path forward is clear, since the ratings delineate the range in which organizational performance consistently varies.

Variation in the measures is illustrated by the hierarchy of the policy and market items rated, which take positions in their distributions showing what consistently comes first, and what precedents have to be set for later challenges to be met successfully. By demanding that the model parameters be independent of one another, we have set ourselves up to learn something from the past that can be used to predict the future.

Further and quite importantly, as experience is repeatedly related to these quantitatively-scaled hierarchies, the factors that make policies and challenges take particular positions on the ruler come to be understood, theory is refined, and leadership gains an edge. Now, it is becoming possible to predict where new policies and challenges will fall on the measurement continuum, making it possible for more rapid responses and earlier anticipations of previously unseen opportunities.

It’s a different story, though, when dependencies emerge, as when one or more employees in a particular area unexpectedly disagree with otherwise broadly accepted policy efficiencies or market challenges, or when a particular policy provokes anomalous evaluations relative to some market challenges but not others. There’s a qualitatively different kind of learning that takes place when expectations are refuted. Instead of getting an answer to the question we asked, we got an answer to one we didn’t ask.

It might just be noise or error, but it is imperative to ask and find out what question the unexpected answer responds to. Routine management thrives on learning how to ever more efficiently predict quantitative results; its polar opposite, innovation, lives on the mystery of unexpected anomalies. If someone hadn’t been able to wonder what value hardened rubber left on a stove might have, what might have killed bacteria in a petri dish, or why an experimental effect disappeared when a lead plate was moved, Vulcanized tires, Penicillin, and X-ray devices might never have come about.

We are on the cusp of the information analogues of these ground-breaking innovations. Methods of integrating rigorously scientific quantities with qualitative creative grist clarify information in previously unimagined ways, and in so doing make it more leveragable than ever before for advancing strategy, engaging customers, and motivating employees.

The only thing in Carol Bartz’s article that I might take issue with comes in the first line, with the words “will be.” The truth is that information already is our greatest opportunity.

LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.