Archive for the ‘network thinking’ Category

Measurement, Metrology, and the Birth of Self-Organizing, Complex Adaptive Systems

February 28, 2011

On page 145 of his book, The Mathematics of Measurement: A Critical History, John Roche quotes Charles de La Condamine (1701-1774), who, in 1747, wrote:

‘It is quite evident that the diversity of weights and measures of different countries, and frequently in the same province, are a source of embarrassment in commerce, in the study of physics, in history, and even in politics itself; the unknown names of foreign measures, the laziness or difficulty in relating them to our own give rise to confusion in our ideas and leave us in ignorance of facts which could be useful to us.’

Roche (1998, p. 145) then explains what de La Condamine is driving at, saying:

“For reasons of international communication and of civic justice, for reasons of stability over time and for accuracy and reliability, the creation of exact, reproducible and well maintained international standards, especially of length and mass, became an increasing concern of the natural philosophers of the seventeenth and eighteenth centuries. This movement, cooperating with a corresponding impulse in governing circles for the reform of weights and measures for the benefit of society and trade, culminated in late eighteenth century France in the metric system. It established not only an exact, rational and international system of measuring length, area, volume and mass, but introduced a similar standard for temperature within the scientific community. It stimulated a wider concern within science to establish all scientific units with equal rigour, basing them wherever possible on the newly established metric units (and on the older exact units of time and angular measurement), because of their accuracy, stability and international availability. This process gradually brought about a profound change in the notation and interpretation of the mathematical formalism of physics: it brought about, for the first time in the history of the mathematical sciences, a true union of mathematics and measurement.”

As it was in the seventeenth and eighteenth centuries for physics, so it has also been in the twentieth and twenty-first for the psychosocial sciences. The creation of exact, reproducible and well maintained international standards is a matter of increasing concern today for the roles they will play in education, health care, the work place, business intelligence, and the economy at large.

As the economic crises persist and perhaps worsen, demand for common product definitions and for interpretable, meaningful measures of impacts and outcomes in education, health care, social services, environmental management, etc. will reach a crescendo. We need an exact, rational and international system of measuring literacy, numeracy, health, motivations, quality of life, community cohesion, and environmental quality, and we needed it fifty years ago. We need to reinvigorate and revive a wider concern across the sciences to establish all scientific units with equal rigor, and to have all measures used in research and practice based wherever possible on consensus standard metrics valued for their accuracy, stability and availability. We need to replicate in the psychosocial sciences the profound change in the notation and interpretation of the mathematical formalism of physics that occurred in the eighteenth and nineteenth centuries. We need to extend the true union of mathematics and measurement from physics to the psychosocial sciences.

Previous posts in this blog speak to the persistent invariance and objectivity exhibited by many of the constructs measured using ability tests, attitude surveys, performance assessments, etc. A question previously raised in this blog concerning the reproductive logic of living meaning deserves more attention, and can be productively explored in terms of complex adaptive functionality.

In a hierarchy of reasons why mathematically rigorous measurement is valuable, few are closer to the top of the list than facilitating the spontaneous self-organization of networks of agents and actors (Latour, 1987). The conception, gestation, birthing, and nurturing of complex adaptive systems constitute a reproductive logic for sociocultural traditions. Scientific traditions, in particular, form mature self-identities via a mutually implied subject-object relation absorbed into the flow of a dialectical give and take, just as economic systems do.

Complex adaptive systems establish the reproductive viability of their offspring and the coherence of an ecological web of meaningful relationships by means of this dialectic. Taylor (2003, pp. 166-8) describes the five moments in the formation and operation of complex adaptive systems, which must be able

  • to identify regularities and patterns in the flow of matter, energy, and information (MEI) in the environment (business, social, economic, natural, etc.);
  • to produce condensed schematic representations of these regularities so they can be identified as the same if they are repeated;
  • to form reproductively interchangeable variants of these representations;
  • to succeed reproductively by means of the accuracy and reliability of the representations’ predictions of regularities in the MEI data flow; and
  • adaptively modify and reorganize representations by means of informational feedback from the environment.

All living systems, from bacteria and viruses to plants and animals to languages and cultures, are complex adaptive systems characterized by these five features.

In the history of science, technologically-embodied measurement facilitates complex adaptive systems of various kinds. That history can be used as a basis for a meta-theoretical perspective on what measurement must look like in the social and human sciences. Each of Taylor’s five moments in the formation and operation of complex adaptive systems describes a capacity of measurement systems, in that:

  • data flow regularities are captured in initial, provisional instrument calibrations;
  • condensed local schematic representations are formed when an instrument’s calibrations are anchored at repeatedly observed, invariant values;
  • interchangeable nonlocal versions of these invariances are created by means of instrument equating, item banking, metrological networks, and selective, tailored, adaptive instrument administration;
  • measures read off inaccurate and unreliable instruments will not support successful reproduction of the data flow regularity, but accurate and reliable instruments calibrated in a shared common unit provide a reference standard metric that enhances communication and reproduces the common voice and shared identity of the research community; and
  • consistently inconsistent anomalous observations provide feedback suggesting new possibilities for as yet unrecognized data flow regularities that might be captured in new calibrations.

Measurement in the social sciences is in the process of extending this functionality into practical applications in business, education, health care, government, and elsewhere. Over the course of the last 50 years, measurement research and practice has already iterated many times through these five moments. In the coming years, a new critical mass will be reached in this process, systematically bringing about scale-of-magnitude improvements in the efficiency of intangible assets markets.

How? What does a “data flow regularity” look like? How is it condensed into a a schematic and used to calibrate an instrument? How are local schematics combined together in a pattern used to recognize new instances of themselves? More specifically, how might enterprise resource planning (ERP) software (such as SAP, Oracle, or PeopleSoft) simultaneously provide both the structure needed to support meaningful comparisons and the flexibility needed for good fit with the dynamic complexity of adaptive and generative self-organizing systems?

Prior work in this area proposes a dual-core, loosely coupled organization using ERP software to build social and intellectual capital, instead of using it as an IT solution addressing organizational inefficiencies (Lengnick-Hall, Lengnick-Hall, & Abdinnour-Helm, 2004). The adaptive and generative functionality (Stenner & Stone, 2003) provided by probabilistic measurement models (Rasch, 1960; Andrich, 2002, 2004; Bond & Fox, 2007; Wilson, 2005; Wright, 1977, 1999) makes it possible to model intra- and inter-organizational interoperability (Weichhart, Feiner, & Stary, 2010) at the same time that social and intellectual capital resources are augmented.

Actor/agent network theory has emerged from social and historical studies of the shared and competing moral, economic, political, and mathematical values disseminated by scientists and technicians in a variety of different successful and failed areas of research (Latour, 2005). The resulting sociohistorical descriptions ought be translated into a practical program for reproducing successful research programs. A metasystem for complex adaptive systems of research is implied in what Roche (1998) calls a “true union of mathematics and measurement.”

Complex adaptive systems are effectively constituted of such a union, even if, in nature, the mathematical character of the data flows and calibrations remains virtual. Probabilistic conjoint models for fundamental measurement are poised to extend this functionality into the human sciences. Though few, if any, have framed the situation in these terms, these and other questions are being explored, explicitly and implicitly, by hundreds of researchers in dozens of fields as they employ unidimensional models for measurement in their investigations.

If so, might then we be on the verge of a yet another new reading and writing of Galileo’s “book of nature,” this time restoring the “loss of meaning for life” suffered in Galileo’s “fateful omission” of the means by which nature came to be understood mathematically (Husserl, 1970)? The elements of a comprehensive, mathematical, and experimental design science of living systems appear on the verge of providing a saturated solution—or better, a nonequilbrium thermodynamic solution—to some of the infamous shortcomings of modern, Enlightenment science. The unity of science may yet be a reality, though not via the reductionist program envisioned by the positivists.

Some 50 years ago, Marshall McLuhan popularized the expression, “The medium is the message.” The special value quantitative measurement in the history of science does not stem from the mere use of number. Instruments are media on which nature, human or other, inscribes legible messages. A renewal of the true union of mathematics and measurement in the context of intangible assets will lead to a new cultural, scientific, and economic renaissance. As Thomas Kuhn (1977, p. 221) wrote,

“The full and intimate quantification of any science is a consummation devoutly to be wished. Nevertheless, it is not a consummation that can effectively be sought by measuring. As in individual development, so in the scientific group, maturity comes most surely to those who know how to wait.”

Given that we have strong indications of how full and intimate quantification consummates a true union of mathematics and measurement, the time for waiting is now past, and the time to act has come. See prior blog posts here for suggestions on an Intangible Assets Metric System, for resources on methods and research, for other philosophical ruminations, and more. This post is based on work presented at Rasch meetings several years ago (Fisher, 2006a, 2006b).

References

Andrich, D. (2002). Understanding resistance to the data-model relationship in Rasch’s paradigm: A reflection for the next generation. Journal of Applied Measurement, 3(3), 325-59.

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

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

Fisher, W. P., Jr. (2006a, Friday, April 28). Complex adaptive functionality via measurement. Presented at the Midwest Objective Measurement Seminar, M. Lunz (Organizer), University of Illinois at Chicago.

Fisher, W. P., Jr. (2006b, June 27-9). Measurement and complex adaptive functionality. Presented at the Pacific Rim Objective Measurement Symposium, T. Bond & M. Wu (Organizers), The Hong Kong Institute of Education, Hong Kong.

Husserl, E. (1970). The crisis of European sciences and transcendental phenomenology: An introduction to phenomenological philosophy (D. Carr, Trans.). Evanston, Illinois: Northwestern University Press (Original work published 1954).

Kuhn, T. S. (1977). The function of measurement in modern physical science. In T. S. Kuhn, The essential tension: Selected studies in scientific tradition and change (pp. 178-224). Chicago: University of Chicago Press. [(Reprinted from Kuhn, T. S. (1961). Isis, 52(168), 161-193.]

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. (Clarendon Lectures in Management Studies). Oxford, England: Oxford University Press.

Lengnick-Hall, C. A., Lengnick-Hall, M. L., & Abdinnour-Helm, S. (2004). The role of social and intellectual capital in achieving competitive advantage through enterprise resource planning (ERP) systems. Journal of Engineering Technology Management, 21, 307-330.

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.

Roche, J. (1998). The mathematics of measurement: A critical history. London: The Athlone Press.

Stenner, A. J., & Stone, M. (2003). Item specification vs. item banking. Rasch Measurement Transactions, 17(3), 929-30 [http://www.rasch.org/rmt/rmt173a.htm].

Taylor, M. C. (2003). The moment of complexity: Emerging network culture. Chicago: University of Chicago Press.

Weichhart, G., Feiner, T., & Stary, C. (2010). Implementing organisational interoperability–The SUddEN approach. Computers in Industry, 61, 152-160.

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. (1997, 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. (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.

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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.
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Consequences of Standardized Technical Effects for Scientific Advancement

January 24, 2011

Note. This is modified from:

Fisher, W. P., Jr. (2004, Wednesday, January 21). Consequences of standardized technical effects for scientific advancement. In  A. Leplège (Chair), Session 2.5A. Rasch Models: History and Philosophy. Second International Conference on Measurement in Health, Education, Psychology, and Marketing: Developments with Rasch Models, The International Laboratory for Measurement in the Social Sciences, School of Education, Murdoch University, Perth, Western Australia.

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Over the last several decades, historians of science have repeatedly produced evidence contradicting the widespread assumption that technology is a product of experimentation and/or theory (Kuhn 1961; Latour 1987; Rabkin 1992; Schaffer 1992; Hankins & Silverman 1999; Baird 2002). Theory and experiment typically advance only within the constraints set by a key technology that is widely available to end users in applied and/or research contexts. Thus, “it is not just a clever historical aphorism, but a general truth, that ‘thermodynamics owes much more to the steam engine than ever the steam engine owed to thermodynamics’” (Price 1986, p. 240).

The prior existence of the relevant technology comes to bear on theory and experiment again in the common, but mistaken, assumption that measures are made and experimentally compared in order to discover scientific laws. History and the logic of measurement show that measures are rarely made until the relevant law is effectively embodied in an instrument (Kuhn 1961; Michell 1999). This points to the difficulty experienced in metrologically fusing (Schaffer 1992, p. 27; Lapré & van Wassenhove 2002) instrumentalists’ often inarticulate, but materially effective, knowledge (know-how) with theoreticians’ often immaterial, but well articulated, knowledge (know-why) (Galison 1999; Baird 2002).

Because technology often dictates what, if any, phenomena can be consistently produced, it constrains experimentation and theorizing by focusing attention selectively on reproducible, potentially interpretable effects, even when those effects are not well understood (Ackermann 1985; Daston & Galison 1992; Ihde 1998; Hankins & Silverman 1999; Maasen & Weingart 2001). Criteria for theory choice in this context stem from competing explanatory frameworks’ experimental capacities to facilitate instrument improvements, prediction of experimental results, and gains in the efficiency with which a phenomenon is produced.

In this context, the relatively recent introduction of measurement models requiring additive, invariant parameterizations (Rasch 1960) provokes speculation as to the effect on the human sciences that might be wrought by the widespread availability of consistently reproducible effects expressed in common quantitative languages. Paraphrasing Price’s comment on steam engines and thermodynamics, might it one day be said that as yet unforeseeable advances in reading theory will owe far more to the Lexile analyzer (Burdick & Stenner 1996) than ever the Lexile analyzer owed reading theory?

Kuhn (1961) speculated that the second scientific revolution of the mid-nineteenth century followed in large part from the full mathematization of physics, i.e., the emergence of metrology as a professional discipline focused on providing universally accessible uniform units of measurement (Roche 1998). Might a similar revolution and new advances in the human sciences follow from the introduction of rigorously mathematical uniform measures?

Measurement technologies capable of supporting the calibration of additive units that remain invariant over instruments and samples (Rasch 1960) have been introduced relatively recently in the human sciences. The invariances produced appear 1) very similar to those produced in the natural sciences (Fisher 1997) and 2) based in the same mathematical metaphysics as that informing the natural sciences (Fisher 2003). Might then it be possible that the human sciences are on the cusp of a revolution analogous to that of nineteenth century physics? Other factors involved in answering this question, such as the professional status of the field, the enculturation of students, and the scale of the relevant enterprises, define the structure of circumstances that might be capable of supporting the kind of theoretical consensus and research productivity that came to characterize, for instance, work in electrical resistance through the early 1880s (Schaffer 1992).

Much could be learned from Rasch’s use of Maxwell’s method of analogy (Nersessian, 2002; Turner, 1955), not just in the modeling of scientific laws but from the social and economic factors that made the regularities of natural phenomena function as scientific capital (Latour, 1987). Quantification must be understood in the fully mathematical sense of commanding a comprehensive grasp of the real root of mathematical thinking. Far from being simply a means of producing numbers, to be useful, quantification has to result in qualitatively transparent figure-meaning relations at any point of use for any one of every different kind of user. Connections between numbers and unit amounts of the variable must remain constant across samples, instruments, time, space, and measurers. Quantification that does not support invariant linear comparisons expressed in a uniform metric available universally to all end users at the point of need is inadequate and incomplete. Such standardization is widely respected in the natural sciences but is virtually unknown in the human sciences, largely due to untested hypotheses and unexamined prejudices concerning the viability of universal uniform measures for the variables measured via tests, surveys, and performance assessments.

Quantity is an effective medium for science to the extent that it comprises an instance of the kind of common language necessary for distributed, collective thinking; for widespread agreement on what makes research results compelling; and for the formation of social capital’s group-level effects. It may be that the primary relevant difference between the case of 19th century physics and today’s human sciences concerns the awareness, widespread among scientists in the 1800s and virtually nonexistent in today’s human sciences, that universal uniform metrics for the variables of interest are both feasible and of great human, scientific, and economic value.

In the creative dynamics of scientific instrument making, as in the making of art, the combination of inspiration and perspiration can sometimes result in cultural gifts of the first order. It nonetheless often happens that some of these superlative gifts, no matter how well executed, are unable to negotiate the conflict between commodity and gift economics characteristic of the marketplace (Baird, 1997; Hagstrom, 1965; Hyde, 1979), and so remain unknown, lost to the audiences they deserve, and unable to render their potential effects historically. Value is not an intrinsic characteristic of the gift; rather, value is ascribed as a function of interests. If interests are not cultivated via the clear definition of positive opportunities for self-advancement, common languages, socio-economic relations, and recruitment, gifts of even the greatest potential value may die with their creators. On the other hand, who has not seen mediocrity disproportionately rewarded merely as a result of intensive marketing?

A central problem is then how to strike a balance between individual or group interests and the public good. Society and individuals are interdependent in that children are enculturated into the specific forms of linguistic and behavioral competence that are valued in communities at the same time that those communities are created, maintained, and reproduced through communicative actions (Habermas, 1995, pp. 199-200). The identities of individuals and societies then co-evolve, as each defines itself through the other via the medium of language. Language is understood broadly in this context to include all perceptual reading of the environment, bodily gestures, social action, etc., as well as the use of spoken or written symbols and signs (Harman, 2005; Heelan, 1983; Ihde, 1998; Nicholson, 1984; Ricoeur, 1981).

Technologies extend language by providing media for the inscription of new kinds of signs (Heelan, 1983a, 1998; Ihde, 1991, 1998; Ihde & Selinger, 2003). Thus, mobility desires and practices are inscribed and projected into the world using the automobile; shelter and life style, via housing and clothing; and communications, via alphabets, scripts, phonemes, pens and paper, telephones, and computers. Similarly, technologies in the form of test, survey, and assessment instruments provide the devices on which we inscribe desires for social mobility, career advancement, health maintenance and improvement, etc.

References

Ackermann, J. R. (1985). Data, instruments, and theory: A dialectical approach to understanding science. Princeton, New Jersey: Princeton University Press.

Baird, D. (1997, Spring-Summer). Scientific instrument making, epistemology, and the conflict between gift and commodity economics. Techné: Journal of the Society for Philosophy and Technology, 2(3-4), 25-46. Retrieved 08/28/2009, from http://scholar.lib.vt.edu/ejournals/SPT/v2n3n4/baird.html.

Baird, D. (2002, Winter). Thing knowledge – function and truth. Techné: Journal of the Society for Philosophy and Technology, 6(2). Retrieved 19/08/2003, from http://scholar.lib.vt.edu/ejournals/SPT/v6n2/baird.html.

Burdick, H., & Stenner, A. J. (1996). Theoretical prediction of test items. Rasch Measurement Transactions, 10(1), 475 [http://www.rasch.org/rmt/rmt101b.htm].

Daston, L., & Galison, P. (1992, Fall). The image of objectivity. Representations, 40, 81-128.

Galison, P. (1999). Trading zone: Coordinating action and belief. In M. Biagioli (Ed.), The science studies reader (pp. 137-160). New York, New York: Routledge.

Habermas, J. (1995). Moral consciousness and communicative action. Cambridge, Massachusetts: MIT Press.

Hagstrom, W. O. (1965). Gift-giving as an organizing principle in science. The Scientific Community. New York: Basic Books, pp. 12-22. (Rpt. in B. Barnes, (Ed.). (1972). Sociology of science: Selected readings (pp. 105-20). Baltimore, Maryland: Penguin Books.

Hankins, T. L., & Silverman, R. J. (1999). Instruments and the imagination. Princeton, New Jersey: Princeton University Press.

Harman, G. (2005). Guerrilla metaphysics: Phenomenology and the carpentry of things. Chicago: Open Court.

Hyde, L. (1979). The gift: Imagination and the erotic life of property. New York: Vintage Books.

Ihde, D. (1998). Expanding hermeneutics: Visualism in science. Northwestern University Studies in Phenomenology and Existential Philosophy). Evanston, Illinois: Northwestern University Press.

Kuhn, T. S. (1961). The function of measurement in modern physical science. Isis, 52(168), 161-193. (Rpt. in The essential tension: Selected studies in scientific tradition and change (pp. 178-224). Chicago, Illinois: University of Chicago Press (Original work published 1977).

Lapré, M. A., & Van Wassenhove, L. N. (2002, October). Learning across lines: The secret to more efficient factories. Harvard Business Review, 80(10), 107-11.

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York, New York: Cambridge University Press.

Maasen, S., & Weingart, P. (2001). Metaphors and the dynamics of knowledge. (Vol. 26. Routledge Studies in Social and Political Thought). London: Routledge.

Michell, J. (1999). Measurement in psychology: A critical history of a methodological concept. Cambridge: Cambridge University Press.

Nersessian, N. J. (2002). Maxwell and “the Method of Physical Analogy”: Model-based reasoning, generic abstraction, and conceptual change. In D. Malament (Ed.), Essays in the history and philosophy of science and mathematics (pp. 129-166). Lasalle, Illinois: Open Court.

Price, D. J. d. S. (1986). Of sealing wax and string. In Little Science, Big Science–and Beyond (pp. 237-253). New York, New York: Columbia University Press. p. 240:

Rabkin, Y. M. (1992). Rediscovering the instrument: Research, industry, and education. In R. Bud & S. E. Cozzens (Eds.), Invisible connections: Instruments, institutions, and science (pp. 57-82). Bellingham, Washington: SPIE Optical Engineering Press.

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.

Roche, J. (1998). The mathematics of measurement: A critical history. London: The Athlone Press.

Schaffer, S. (1992). Late Victorian metrology and its instrumentation: A manufactory of Ohms. In R. Bud & S. E. Cozzens (Eds.), Invisible connections: Instruments, institutions, and science (pp. 23-56). Bellingham, WA: SPIE Optical Engineering Press.

Turner, J. (1955, November). Maxwell on the method of physical analogy. British Journal for the Philosophy of Science, 6, 226-238.

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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.
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How bad will the financial crises have to get before…?

April 30, 2010

More and more states and nations around the world face the possibility of defaulting on their financial obligations. The financial crises are of epic historical proportions. This is a disaster of the first order. And yet, it is so odd–we have the solutions and preventative measures we need at our finger tips, but no one knows about them or is looking for them.

So,  I am persuaded to once again wonder if there might now be some real interest in the possibilities of capitalizing on

  • measurement’s well-known capacity for reducing transaction costs by improving information quality and reducing information volume;
  • instruments calibrated to measure in constant units (not ordinal ones) within known error ranges (not as though the measures are perfectly precise) with known data quality;
  • measures made meaningful by their association with invariant scales defined in terms of the questions asked;
  • adaptive instrument administration methods that make all measures equally precise by targeting the questions asked;
  • judge calibration methods that remove the person rating performances as a factor influencing the measures;
  • the metaphor of transparency by calibrating instruments that we really look right through at the thing measured (risk, governance, abilities, health, performance, etc.);
  • efficient markets for human, social, and natural capital by means of the common currencies of uniform metrics, calibrated instrumentation, and metrological networks;
  • the means available for tuning the instruments of the human, social, and environmental sciences to well-tempered scales that enable us to more easily harmonize, orchestrate, arrange, and choreograph relationships;
  • our understandings that universal human rights require universal uniform measures, that fair dealing requires fair measures, and that our measures define who we are and what we value; and, last but very far from least,
  • the power of love–the back and forth of probing questions and honest answers in caring social intercourse plants seminal ideas in fertile minds that can be nurtured to maturity and Socratically midwifed as living meaning born into supportive ecologies of caring relations.

How bad do things have to get before we systematically and collectively implement the long-established and proven methods we have at our disposal? It is the most surreal kind of schizophrenia or passive-aggressive avoidance pathology to keep on tormenting ourselves with problems for which we have solutions.

For more information on these issues, see prior blogs posted here, the extensive documentation provided, and http://www.livingcapitalmetrics.com.

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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.

Parameterizing Perfection: Practical Applications of a Mathematical Model of the Lean Ideal

April 2, 2010

To properly pursue perfection, we need to parameterize it. That is, taking perfection as the ideal, unattainable standard against which we judge our performance is equivalent to thinking of it as a mathematical model. Organizations are intended to realize their missions independent of the particular employees, customers, suppliers, challenges, products, etc. they happen to engage with at any particular time. Organizational performance measurement (Spitzer, 2007) ought to then be designed in terms of a model that posits, tests for, and capitalizes on the always imperfectly realized independence of those parameters.

Lean thinking (Womack & Jones, 1996) focuses on minimizing waste and maximizing value. At every point at which resources are invested in processes, services, or products, the question is asked, “What value is added here?” Resources are wasted when no value is added, when they can be removed with no detrimental effect on the value of the end product. In their book, Natural Capitalism: Creating the Next Industrial Revolution, Hawken, Lovins, and Lovins (1999, p. 133) say

“Lean thinking … changes the standard for measuring corporate success. … As they [Womack and Jones] express it: ‘Our earnest advice to lean firms today is simple. To hell with your competitors; compete against perfection by identifying all activities that are muda [the Japanese term for waste used in Toyota’s landmark quality programs] and eliminating them. This is an absolute rather than a relative standard which can provide the essential North Star for any organization.”

Further, every input should “be presumed waste until shown otherwise.” A constant, ongoing, persistent pressure for removing waste is the basic characteristic of lean thinking. Perfection is never achieved, but it aptly serves as the ideal against which progress is measured.

Lean thinking sounds a lot like a mathematical model, though it does not seem to have been written out in a mathematical form, or used as the basis for calibrating instruments, estimating measures, evaluating data quality, or for practical assessments of lean organizational performance. The closest anyone seems to have come to parameterizing perfection is in the work of Genichi Taguchi (Ealey, 1988), which has several close parallels with Rasch measurement (Linacre, 1993).  But meaningful and objective quantification, as required and achieved in the theory and practice of fundamental measurement (Andrich, 2004; Bezruczko, 2005; Bond & Fox 2007; Smith & Smith, 2004; Wilson, 2005; Wright, 1999), in fact asserts abstract ideals of perfection as models of organizational, social, and psychological processes in education, health care, marketing, etc. These models test the extent to which outcomes remain invariant across examination or survey questions, across teachers, students, schools, and curricula, or across treatment methods, business processes, or policies.

Though as yet implemented only to a limited extent in business (Drehmer, Belohlav, James, & Coye, 2000; Drehmer & Deklava, 2001;  Lunz & Linacre, 1998; Salzberger, 2009), advanced measurement’s potential rewards are great. Fundamental measurement theory has been successfully applied in research and practice thousands of times over the last 40 years and more, including in very large scale assessments and licensure/certification applications (Adams, Wu, & Macaskill, 1997; Masters, 2007; Smith, Julian, Lunz, et al., 1994). These successes speak to an opportunity for making broad improvements in outcome measurement that could provide more coherent product definition, and significant associated opportunities for improving product quality and the efficiency with which it is produced, in the manner that has followed from the use of fundamental measures in other industries.

Of course, processes and outcomes are never implemented or obtained with perfect consistency. This would be perfectly true only in a perfect world. But to pursue perfection, we need to parameterize it. In other words, to raise the bar in any area of performance assessment, we have to know not only what direction is up, but we also need to know when we have raised the bar far enough. But we cannot tell up from down, we do not know how much to raise the bar, and we cannot properly evaluate the effects of lean experiments when we have no way of locating measures on a number line that embodies the lean ideal.

To think together collectively in ways that lead to significant new innovations, to rise above what Jaron Lanier calls the “global mush” of confused and self-confirming hive thinking, we need the common languages of widely accepted fundamental measures of the relevant processes and outcomes, measures that remain constant across samples of customers, patients, employees, students, etc., and across products, sales techniques, curricula, treatment processes, assessment methods, and brands of instrument.

We are all well aware that the consequences of not knowing where the bar is, of not having product definitions, can be disastrous. In many respects, as I’ve said previously in this blog, the success or failure of health care reform hinges on getting measurement right. The Institute of Medicine report, To Err is Human, of several years ago stresses the fact that system failures pose the greatest threat to safety in health care because they lead to human errors. When a system as complex as health care lacks a standard product definition, and product delivery is fragmented across multiple providers with different amounts and kinds of information in different settings, the system becomes dangerously cumbersome and over-complicated, with unacceptably wide variations and errors in its processes and outcomes, not to even speak of its economic inefficiency.

In contrast with the widespread use of fundamental measures in the product definitions of other industries, health care researchers typically implement neither the longstanding, repeatedly proven, and mathematically rigorous models of fundamental measurement theory nor the metrological networks through which reference standard metrics are engineered. Most industries carefully define, isolate, and estimate the parameters of their products, doing so in ways 1) that ensure industry-wide comparability and standardization, and 2) that facilitate continuous product improvement by revealing multiple opportunities for enhancement. Where organizations in other industries manage by metrics and thereby keep their eyes on the ball of product quality, health care organizations often manage only their own internal processes and cannot in fact bring the product quality ball into view.

In his message concerning the Institute for Healthcare Improvement’s Pursuing Perfection project a few years ago, Don Berwick, like others (Coye, 2001; Coye & Detmer, 1998), observed that health care does not yet have an organization setting new standards in the way that Toyota did for the auto industry in the 1970s. It still doesn’t, of course. Given the differences between the auto and health care industries uses of fundamental measures of product quality and associated abilities to keep their eyes on the quality ball, is it any wonder then, that no one in health care has yet hit a home run? It may well be that no one will hit a home run in health care until reference standard measures of product quality are devised.

The need for reference standard measures in uniform data systems is crucial, and the methods for obtaining them are widely available and well-known. So what is preventing the health care industry from adopting and deploying them? Part of the answer is the cost of the initial investment required. In 1980, metrology comprised about six percent of the U.S. gross national product (Hunter, 1980). In the period from 1981 to 1994, annual expenditures on research and development in the U.S. were less than three percent of the GNP, and non-defense R&D was about two percent (NIST Subcommittee on Research, National Science and Technology Council, 1996). These costs, however, must be viewed as investments from which high rates of return can be obtained (Barber, 1987; Gallaher, Rowe, Rogozhin, et al., 2007; Swann, 2005).

For instance, the U.S. National Institute of Standards and Technology estimated the economic impact of 12 areas of research in metrology, in four broad areas including semiconductors, electrical calibration and testing, optical industries, and computer systems (NIST, 1996, Appendix C; also see NIST, 2003). The median rate of return in these 12 areas was 147 percent, and returns ranged from 41 to 428 percent. The report notes that these results compare favorably with those obtained in similar studies of return rates from other public and private research and development efforts. Even if health care metrology produces only a small fraction of the return rate produced in physical metrology, its economic impact could still amount to billions of dollars annually. The proposed pilot projects therefore focus on determining what an effective health care outcomes metrology system should look like. What should its primary functions be? What should it cost? What rates of return could be expected from it?

Metrology, the science of measurement (Pennella, 1997), requires 1) that instruments be calibrated within individual laboratories so as to isolate and estimate the values of the required parameters (Wernimont, 1978); and 2) that individual instruments’ capacities to provide the same measure for the same amount, and so be traceable to a reference standard, be established and monitored via interlaboratory round-robin trials (Mandel, 1978).

Fundamental measurement has already succeeded in demonstrating the viability of reference standard measures of health outcomes, measures whose meaningfulness does not depend on the particular samples of items employed or patients measured. Though this work succeeds as far as it goes, it being done in a context that lacks any sense of the need for metrological infrastructure. Health care needs networks of scientists and technicians collaborating not only in the first, intralaboratory phase of metrological work, but also in the interlaboratory trials through which different brands or configurations of instruments intended to measure the same variable would be tuned to harmoniously produce the same measure for the same amount.

Implementation of the two phases of metrological innovation in health care would then begin with the intralaboratory calibration of existing and new instruments for measuring overall organizational performance, quality of care, and patients’ health status, quality of life, functionality, etc.  The second phase takes up the interlaboratory equating of these instruments, and the concomitant deployment of reference standard units of measurement throughout a health care system and the industry as a whole. To answer questions concerning health care metrology’s potential returns on investment, the costs for, and the savings accrued from, accomplishing each phase of each pilot will be tracked or estimated.

When instruments measuring in universally uniform, meaningful units are put in the hands of clinicians, a new scientific revolution will occur in medicine. It will be analogous to previous ones associated with the introduction of the thermometer and the instruments of optometry and the clinical laboratory. Such tools will multiply many times over the quality improvement methods used by Brent James, touted as holding the key to health care reform in a recent New York Times profile. Instead of implicitly hypothesizing models of perfection and assessing performance relative to them informally, what we need is a new science that systematically implements the lean ideal on industry-wide scales. The future belongs to those who master these techniques.

References

Adams, R. J., Wu, M. L., & Macaskill, G. (1997). Scaling methodology and procedures for the mathematics and science scales. In M. O. Martin & D. L. Kelly (Eds.), Third International Mathematics and Science Study Technical Report: Vol. 2: Implementation and Analysis – Primary and Middle School Years (pp. 111-145). Chestnut Hill, MA: Boston College.

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

Barber, J. M. (1987). Economic rationale for government funding of work on measurement standards. In R. Dobbie, J. Darrell, K. Poulter & R. Hobbs (Eds.), Review of DTI work on measurement standards (p. Annex 5). London: Department of Trade and Industry.

Berwick, D. M., James, B., & Coye, M. J. (2003, January). Connections between quality measurement and improvement. Medical Care, 41(1 (Suppl)), I30-38.

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.

Coye, M. J. (2001, November/December). No Toyotas in health care: Why medical care has not evolved to meet patients’ needs. Health Affairs, 20(6), 44-56.

Coye, M. J., & Detmer, D. E. (1998). Quality at a crossroads. The Milbank Quarterly, 76(4), 759-68.

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.

Ealey, L. A. (1988). Quality by design: Taguchi methods and U.S. industry. Dearborn MI: ASI Press.

Gallaher, M. P., Rowe, B. R., Rogozhin, A. V., Houghton, S. A., Davis, J. L., Lamvik, M. K., et al. (2007). Economic impact of measurement in the semiconductor industry (Tech. Rep. No. 07-2). Gaithersburg, MD: National Institute for Standards and Technology.

Hawken, P., Lovins, A., & Lovins, H. L. (1999). Natural capitalism: Creating the next industrial revolution. New York: Little, Brown, and Co.

Hunter, J. S. (1980, November). The national system of scientific measurement. Science, 210(21), 869-874.

Linacre, J. M. (1993). Quality by design: Taguchi and Rasch. Rasch Measurement Transactions, 7(2), 292.

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.

Mandel, J. (1978, December). Interlaboratory testing. ASTM Standardization News, 6, 11-12.

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

National Institute for Standards and Technology (NIST). (1996). Appendix C: Assessment examples. Economic impacts of research in metrology. In C. o. F. S. Subcommittee on Research (Ed.), Assessing fundamental science: A report from the Subcommittee on Research, Committee on Fundamental Science. Washington, DC: National Standards and Technology Council [http://www.nsf.gov/statistics/ostp/assess/nstcafsk.htm#Topic%207; last accessed 18 February 2008].

National Institute for Standards and Technology (NIST). (2003, 15 January). Outputs and outcomes of NIST laboratory research. Retrieved 12 July 2009, from http://www.nist.gov/director/planning/studies.htm#measures.

Pennella, C. R. (1997). Managing the metrology system. Milwaukee, WI: ASQ Quality Press.\

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

Smith, R. M., Julian, E., Lunz, M., Stahl, J., Schulz, M., & Wright, B. D. (1994). Applications of conjoint measurement in admission and professional certification programs. International Journal of Educational Research, 21(6), 653-664.

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

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

Swann, G. M. P. (2005, 2 December). John Barber’s pioneering work on the economics of measurement standards [Electronic version]. Retrieved http://www.cric.ac.uk/cric/events/jbarber/swann.pdf from Notes for Workshop in Honor of John Barber held at University of Manchester.

Wernimont, G. (1978, December). Careful intralaboratory study must come first. ASTM Standardization News, 6, 11-12.

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

Womack, J. P., & Jones, D. T. (1996, Sept./Oct.). Beyond Toyota: How to root out waste and pursue perfection. Harvard Business Review, 74, 140-58.

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.

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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.
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Assignment from Wired’s Predict What’s Next page: “Imagine the Future of Medical Bills”

March 20, 2010

William P. Fisher, Jr.

william@livingcapitalmetrics.com
New Orleans, Louisiana
20 March 2010

Consider the following, formulated in response to Wired magazine’s 18.04 request for ideas on the future of medical bills, for possible use on the Predict What’s Next page. For background on the concepts presented here, see previous posts in this blog, such as https://livingcapitalmetrics.wordpress.com/2010/01/13/reinventing-capitalism/.

Visualize an online image of a Maiuetic Renaissance Bank’s Monthly Living Capital Stock, Investment, and Income Report. The report is shown projected as a vertical plane in the space above an old antique desk. Credits and debits to and from Mary Smith’s health capital account are listed, along with similar information on all of her capital accounts. Lying on the desk is a personalized MRB Living Capital Credit/Debit card, evidently somehow projecting the report from the eyes of Mary’s holographic image on it.

The report shows headings and entries for Mary Smith’s various capital accounts:

  • liquid (cash, checking and savings),
  • property (home, car, boat, rental, investments, etc.),
  • social capital (trust, honesty, commitment, loyalty, community building, etc.) credits/debits:
    • personal,
    • community’s,
    • employer’s,
    • regional,
    • national;
  • human capital:
    • literacy credits (shown in Lexiles; http://www.lexile.com),
    • numeracy credits (shown in Quantiles; http://www.quantiles.com),
    • occupational credits (hireability, promotability, retainability, productivity),
    • health credits/debits (genetic, cognitive reasoning, physical function, emotional function, chronic disease management status, etc.); and
  • natural capital:
    • carbon credits/debits,
    • local and global air, water, ecological diversity, and environmental quality share values.

Example social capital credits/debits shown in the report might include volunteering to build houses in N’Awlins Ninth Ward, tutoring fifth-graders in math, jury duty, voting, writing letters to congress, or charitable donations (credits), on the one hand, or library fines, a parking ticket, unmaintained property, etc. (debits), on the other.

Natural capital credits might be increased or decreased depending on new efficiencies obtained in electrical grid or in power generation, a newly installed solar panel, or by a recent major industrial accident, environmental disaster, hurricane, etc.

Mary’s share of the current value of the overall Genuine National Product, or Happiness Index, is broken out by each major form of capital (liquid, property, social, human, natural).

The monetary values of credits are shown at the going market rates, alongside the changes from last month, last year, and three years ago.

One entry could be a deferred income and property tax amount, given a social capital investment level above a recommended minimum. Another entry would show new profit potentials expressed in proportions of investments wasted due to inefficiencies, with suggestions for how these can be reduced, and with time to investment recovery and amount of new social capital generated also indicated.

The health capital portion of the report is broken out in a magnified overlay. Mary’s physical and emotional function measures are shown by an arrow pointing at a level on a vertical ruler. Other arrows point at the average levels for people her age (globally, nationally, regionally, and locally), for women and women of different ages, living in different countries/cities, etc.

Mary’s diabetes-specific chronic disease management metric is shown at a high level, indicating her success in using diet and exercise to control her condition. Her life expectancy and lifetime earning potentials are shown, alongside comparable values for others.

Recent clinical visits for preventative diabetes and dental care would be shown as debits against one account and as an investment in her health capital account. The debits might be paid out of a sale of shares of stock from her quite high social or natural capital accounts, or from credits transferred from those to her checking account.

Cost of declining function in the next ten years, given typical aging patterns, shown as lower rates of new capital investment in her stock and lower ROIs.

Cost of maintaining or improving function, in terms of required investments of time and resources in exercise, equipment, etc. balanced against constant rate of new investments and ROI.

Also shown:

A footnote could read: Given your recent completion of post-baccalaureate courses in political economy and advanced living capital finance, your increased stocks of literacy, numeracy, and occupational capital qualify you for a promotion or new positions currently compensated at annual rates 17.7% higher than your current one. Watch for tweets and beams from new investors interested in your rising stock!

A warning box: We all pay when dead capital lies unleveragable in currencies expressed in ordinal or otherwise nonstandard metrics! Visit http://www.CapitalResuscitationServices.com today to convert your unaccredited capital currencies into recognized value. (Not responsible for fraudulent misrepresentations of value should your credits prove incommensurable or counterfeit. Always check your vendor’s social capital valuations before investing in any stock offering. Go to http://www.Rasch.org for accredited capital metrics equating information, courses, texts, and consultants.)

Ad: Click here to put your occupational capital stock on the market now! Employers are bidding $$$, ¥¥¥ and €€€ on others at your valuation level!

Ad: You are only 110 Lexiles away from a literacy capital stock level on which others receive 23% higher investment returns! Enroll at BobsOnlineLiteracyCapitalBoosters.com now for your increased income tomorrow! (Past performance is not a guarantee of future results. Your returns may vary. Click here to see Bob’s current social capital valuations.)

Bottom line: Think global, act local! It is up to you to represent your shares in the global marketplace. Only you can demand the improvements you seek by shifting and/or intensifying your investments. Do so whenever you are dissatisfied with your own, your global and local business partners’, your community’s, your employer’s, your region’s, or your nation’s stock valuations.

For background on the concepts involved in this scenario, see:

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

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

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. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (Tech. Rep. No. http://www.livingcapitalmetrics.com/images/FisherNISTWhitePaper2.pdf). New Orleans: http://www.LivingCapitalMetrics.com.

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

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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…?

January 28, 2010

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].

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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.

How to Trade “Global Mush” for Beauty, Meaning, and Value: Reflections on Lanier’s New Book

January 15, 2010

Implicit in many of my recent posts here is the idea that we must learn how to follow through on the appropriation of meaning to proper ownership of the properties characteristic of our own proprietary capital resources: the creativities, abilities, skills, talents, health, motivations, trust, etc.  that make us each reliable citizens and neighbors, and economically viable in being hireable, promotable, productive, and retainable. Individual control of investment in, income from, and returns on our own shares of human, social, and natural capital ought to be a fundamental constitutional human right.

But, just as property rights are constitutionally guaranteed by nations around the world that don’t take the trouble to enforce them or even to provide their necessary infrastructural capacities, so, too, are human rights to equal opportunities widely guaranteed without being properly provided for or enforced. And now in the Internet age, we have succeeded in innovating ever more fluid media for the expression of our individual capacities for making original cultural, technical, and human contributions, but we have yet to figure out how to exert effective control over the returns and income generated by these contributions.

Jaron Lanier’s new book, “You Are Not a Gadget,” is taking up this theme in interesting ways. In his recent Wall Street Journal article, Lanier says:

“There’s a dominant dogma in the online culture of the moment that collectives make the best stuff, but it hasn’t proven to be true. The most sophisticated, influential and lucrative examples of computer code—like the page-rank algorithms in the top search engines or Adobe’s Flash— always turn out to be the results of proprietary development. Indeed, the adored iPhone came out of what many regard as the most closed, tyrannically managed software-development shop on Earth.

Actually, Silicon Valley is remarkably good at not making collectivization mistakes when our own fortunes are at stake. On the one hand we want to avoid physical work and instead benefit from intellectual property. On the other hand, we’re undermining intellectual property so that information can roam around for nothing, or more precisely as bait for advertisements. That’s a formula that leaves no way for our nation to earn a living in the long term.
The “open” paradigm rests on the assumption that the way to get ahead is to give away your brain’s work—your music, writing, computer code and so on—and earn kudos instead of money. You are then supposedly compensated because your occasional dollop of online recognition will help you get some kind of less cerebral work that can earn money. For instance, maybe you can sell custom branded T-shirts.
We’re well over a decade into this utopia of demonetized sharing and almost everyone who does the kind of work that has been collectivized online is getting poorer. There are only a tiny handful of writers or musicians who actually make a living in the new utopia, for instance. Almost everyone else is becoming more like a peasant every day.”
Lanier’s suggestions of revised software structures and micropayment systems in an extension of intellectual property rights correctly recognizes the scope of the challenges we face. He also describes the motivations driving the ongoing collectivization process, saying that “youthful fascination with collectivism is in part simply a way to address perceived ‘unfairness’.” This radical way of enforcing a very low lowest common denominator points straight at the essential problem, and that problem is apparent in the repeated use of the key word, collective.

It was not so long ago that it was impossible to use that word without immediately evoking images of Soviet central planning and committees. The “global mush” of mediocrity Lanier complains about as a direct result of collective thinking is a very good way of describing the failures of socialism that brought down the Soviet Union by undercutting its economic viability. Lanier speaks of growing up and enthusiastically participating various forms of collective life, like food co-ops and shared housing. I, too, have shared those experiences. I saw, as Lanier sees and as the members of communes in the U.S. during the 1960s saw, that nothing gets done when no one owns the process and stands to reap the rewards: when housekeeping is everyone’s responsibility, no one does it.

Further and more to the point, nothing goes right when supply and demand are dictated by a central committee driven by ideological assumptions concerning exactly what does and does not constitute the greater good.  On the contrary, innovation is stifled, inefficiencies are rampant, and no one takes the initiative to do better because there are no incentives for doing so. Though considerable pain is experienced in allowing the invisible hand to coordinate the flux and flows of markets, no better path to prosperity has yet been found. The current struggle is less one of figuring out how to do without markets than it is one of figuring out how to organize them for greater long term stability. As previous posts in this blog endeavor to show, we ought to be looking more toward bringing all forms of capital into the market instead of toward regulating some to death while others ravage the economy, scot-free.

Friedrich von Hayek (1988, 1994) is an economist and philosopher often noted for his on-target evaluations of the errors of socialism. He tellingly focused on the difference between the laborious micromanagement of socialism’s thought police and the wealth-creating liberation of capital’s capacity for self-organization. It is interesting that Lanier describes the effects of demonetized online sharing as driving most of us toward peasant status, as Hayek (1994) describes socialism as a “road to serfdom.” Of course, capitalism itself is far from perfect, since private property, and manufactured and liquid capital, have enjoyed a freedom of movement that too often recklessly tramples human rights, community life, and the natural environment. But as is described in a previous blog I posted on re-inventing capitalism, we can go a long way toward rectifying the errors of capitalism by setting up the rules of law that will lubricate and improve the efficiency of human, social, and natural capital markets.

Now, I’ve always been fascinated with the Latin root shared in words like property, propriety, proprietary, appropriation, proper, and the French propre (which means both clean and one’s own, or belonging to oneself, depending on whether it comes before or after the noun; une maison propre = a clean house and sa propre maison = his/her own house). I was then happy to encounter in graduate school Ricoeur’s (1981) theory of text interpretation, which focuses on the way we create meaning by appropriating it. Real understanding requires that we must make a text our own if we are to be able to give proper evidence of understanding it by restating or summarizing it in our own words.

Such restating is, of course, also the criterion for demonstrating that a scientific theory of the properties of a phenomenon is adequate to the task of reproducing its effects on demand. As Ricoeur (1981, p. 210) says, situating science in a sphere of signs puts the human and natural sciences together on the same footing in the context of linguistically-mediated social relations. This unification of the sciences has profound consequences, not just for philosophy, the social sciences, or economics, but for the practical task of transforming the current “global mush” into a beautiful, meaningful, and effective living creativity system. So, there is real practical significance in realizing what appropriation is and how its processes feed into our conceptualizations of property, propriety, and ownership.

When we can devise a new instrument or measuring method that gives the same results as an existing instrument or method, we have demonstrated theoretical control over the properties of the phenomenon (Heelan, 1983, 2001; Ihde, 1991; Ihde & Selinger, 2003; Fisher, 2004, 2006, 2010b). The more precisely the effects are reproduced, the purer they become, the clearer their representation, and the greater their independence from the local contingencies of sample, instrument, observer, method, etc. When we can package a technique for reproducing the desired effects (radio or tv broadcast/reception, vibrating toothbrushes, or what have you), we can export the phenomenon from the laboratory via networks of distribution, supply, sales, marketing, manufacture, repair, etc. (Latour, 1987). Proprietary methods, instruments, and effects can then be patented and ownership secured.

What we have in the current “global mush” of collective aggregations are nothing at all of this kind. There are specific criteria for information quality and network configuration (Akkerman, et al., 2007; Latour, 1987, pp. 247-257; Latour, 1995; Magnus, 2007; Mandel, 1978; Wise, 1995) that have to be met for collective cognition to realize its potential in the manner described by Surowiecki (2004) or Brafman and Beckstrom (2006), for instance.  The difference is the difference between living and dead capital, between capitalism and socialism, and between scientific measurement and funny numbers that don’t stand for the repetitive additivity of a constant unit (Fisher, 2002, 2009, 2010a). As Lanier notes, Silicon Valley understands very well the nature of this difference, and protects its own interests by vigilantly ensuring that its collective cognitions are based in properly constructed information and networks.

And here we find the crux of the lesson to be learned. We need to focus very carefully on the details of how we create meaningful relationships, of how things come into words, of how instruments are calibrated and linked together in shared systems of signification, and of how economies thrive on the productive efficiencies of well-lubricated markets. Everything we need to turn things around is available, though seeing things for what they are is one of the most daunting and difficult tasks we can undertake.

The postmodern implications of the way appropriation is more a letting-go than a possessing (Ricoeur, 1981, p. 191) will be taken up another time, in the context of the playful flow of signification we are always already caught up within. For now, it is enough to point the way toward the issues raised and examined in other posts in this blog as to how capital is brought to life. We are well on the way toward a convergence of efforts that may well result in exactly the kind of fierce individuals and competing teams able to reap their just due, as Lanier envisions.

References

Akkerman, S., Van den Bossche, P., Admiraal, W., Gijselaers, W., Segers, M., Simons, R.-J., Kirschnerd, P. (2007, February). Reconsidering group cognition: From conceptual confusion to a boundary area between cognitive and socio-cultural perspectives? Educational Research Review, 2, 39-63.
Brafman, O., & Beckstrom, R. A. (2006). The starfish and the spider: The unstoppable power of leaderless organizations. New York: Portfolio (Penguin Group).

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2004, October). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy and the Social Sciences, 27(4), 429-54.

Fisher, W. P., Jr. (2006). Meaningfulness, sufficiency, invariance, and conjoint additivity. Rasch Measurement Transactions, 20(1), 1053 [http://www.rasch.org/rmt/rmt201.htm].

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. (2010a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

Fisher, W. P., Jr. (2010)b. Reducible or irreducible? Mathematical reasoning and the ontological method. Journal of Applied Measurement, 11, in press.

von Hayek, F. A. (1988). The fatal conceit: The errors of socialism (W. W. Bartley, III, Ed.) (Vol. I). The Collected Works of F. A. Hayek. Chicago: University of Chicago Press.

von Hayek, F. A. (1994/1944). The road to serfdom (Fiftieth Anniversary Edition; Introduction by Milton Friedman). Chicago: University of Chicago Press.

Heelan, P. A. (1983, June). Natural science as a hermeneutic of instrumentation. Philosophy of Science, 50, 181-204.

Heelan, P. A. (2001). The lifeworld and scientific interpretation. In S. K. Toombs (Ed.), Handbook of phenomenology and medicine (pp. 47-66). Chicago: University of Chicago Press.

Ihde, D., & Selinger, E. (Eds.). (2003). Chasing technoscience: Matrix for materiality. (Indiana Series in Philosophy of Technology). Bloomington, Indiana: Indiana University Press.
Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (1995). Cogito ergo sumus! Or psychology swept inside out by the fresh air of the upper deck: Review of Hutchins’ Cognition in the Wild, MIT Press, 1995. Mind, Culture, and Activity: An International Journal, 3(192), 54-63.

Magnus, P. D. (2007). Distributed cognition and the task of science. Social Studies of Science, 37(2), 297-310.

Mandel, J. (1978, December). Interlaboratory testing. ASTM Standardization News, 6, 11-12.

Ricoeur, P. (1981). Hermeneutics and the human sciences: Essays on language, action and interpretation (J. B. Thompson, Ed. & Trans). Cambridge, England: Cambridge University Press.

Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York: Doubleday.
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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.
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Contrasting Network Communities: Transparent, Efficient, and Invested vs Not

November 30, 2009

Different networks and different communities have different amounts of social capital going for them. As was originally described by Putnam (1993), some networks are organized hierarchically in a command-and-control structure. The top layers here are the autocrats, nobility, or bosses who run the show. Rigid conformity is the name of the game to get by. Those in power can make or break anyone. Market transactions in this context are characterized by the thumb on the scale, the bribe, and the kickback. Everyone is watching out for themselves.

At the opposite extreme are horizontal networks characterized by altruism and a sense that doing what’s good for everyone will eventually come back around to be good for me. The ideal here is a republic in which the law rules and everyone has the same price of entry into the market.

What I’d like to focus on is what’s going on in these horizontal networks. What makes one a more tightly-knit community than another? The closeness people feel should not be oppressive or claustrophic or smothering. I’m thinking of community relations in which people feel safe, not just personally but creatively. How and when are diversity, dissent and innovation not just tolerated but celebrated? What makes it possible for a market in new ideas and new ways of doing things to take off?

And how does a community like this differ from another one that is just as horizontally structured but that does not give rise to anything at all creative?

The answers to all of these questions seem to me to hinge on the transparency, efficiency, and volume of investments in the relationships making up the networks. What kinds of investments? All kinds: emotional, social, intellectual, financial, spiritual, etc. Less transparent, inefficient, and low volume investments don’t have the thickness or complexity of the relationships that we can see through, that are well lubricated, and that are reinforced with frequent visits.

Putnam (1993, p. 183) has a very illuminating way of putting this: “The harmonies of a choral society illustrate how voluntary collaboration can create value that no individual, no matter how wealthy, no matter how wily, could produce alone.” Social capital is the coordination of thought and behavior that embodies trust, good will, and loyalty. Social capital is at play when an individual can rely on a thickly elaborated network of largely unknown others who provide clean water, nutritious food, effective public health practices (sanitation, restaurant inspections, and sewers), fire and police protection, a fair and just judiciary, electrical and information technology, affordably priced consumer goods, medical care, and who ensure the future by educating the next generation.

Life would be incredibly difficult if we could not trust others to obey traffic laws, or to do their jobs without taking unfair advantage of access to special knowledge (credit card numbers, cash, inside information), etc. But beyond that, we gain huge efficiencies in our lives because of the way our thoughts and behaviors are harmonized and coordinated on mass scales. We just simply do not have to worry about millions of things that are being taken care of, things that would completely freeze us in our tracks if they weren’t being done.

Thus, later on the same page, Putnam also observes that, “For political stability, for government effectiveness, and even for economic progress social capital may be even more important than physical or human capital.” And so, he says, “Where norms and networks of civic engagement are lacking, the outlook for collective action appears bleak.”

But what if two communities have identical norms and networks, but they differ in one crucial way: one relies on everyday language, used in conversations and written messages, to get things done, and the other has a new language, one with a heightened capacity for transparent meaningfulness and precision efficiency? Which one is likely to be more creative and innovative?

The question can be re-expressed in terms of Gladwell’s (2000) sense of the factors contributing to reaching a tipping point: the mavens, connectors, salespeople, and the stickiness of the messages. What if the mavens in two communities are equally knowledgeable, the connectors just as interconnected, and the salespeople just as persuasive, but messages are dramatically less sticky in one community than the other? In one network of networks, saying things once gets the right response 99% of the time, but in the other things have to be repeated seven times before the right response comes back even 50% of the time, and hardly anyone makes the effort to repeat things that many times. Guess which community will be safer, more creative, and thriving?

All of this, of course, is just another way to bring out the importance of improved measurement for improving network quality and community life. As Surowiecki put it in The Wisdom of Crowds, the SARS virus was sequenced in a matter of weeks by a network of labs sharing common technical standards; without those standards, it would have taken any one of them weeks to do the same job alone. The messages these labs sent back and forth had an elevated stickiness index because they were more transparently and efficiently codified than messages were back in the days before the technical standards were created.

So the question emerges, given the means to create common languages with enhanced stickiness properties, such as we have in advanced measurement models, what kinds of creativity and innovation can we expect when these languages are introduced in the domains of human, social, and natural capital markets? That is the question of the age, it seems to me…

Gladwell, M. (2000). The tipping point: How little things can make a big difference. Boston: Little, Brown, and Company.

Putnam, R. D. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, New Jersey: Princeton University Press.

Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York: Doubleday.

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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.
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Clarifying the Goal: Submitting Rasch-based White Papers to NIST

October 23, 2009

NIST does not currently have any metrological standards (metrics to which all instruments measuring a particular construct are traceable) for anything measured with tests, surveys, rating scale assessments, or rankings–i.e., for anything of core interest in education, psychology, sociology, health status assessment, etc.

The ostensible reason for the lack of these standards is that no one has stepped up to demand them, to demonstrate their feasibility, or argue on behalf of their value. So anything of general interest as something for which we would want univerally uniform and available metrics could be proposed. As can be seen in the NIST call, you have to be able to argue for the viability of a fundamentally new innovation that would produce high returns on the investment in a system of networked, equated, or item banked instruments all measuring in a common metric.

Jack Stenner expressed the opinion some years ago that constructs already measured on mass scales using many different instruments that could conceivably be equated present the most persuasive cases for which strong metrological arguments could be made. I have wondered if that is necessarily true.

The idea is to establish a new division in NIST, managed jointly with the National Institutes of Health and of Education, that focuses on creating a new kind of metric system for informing human, social, and natural capital management, quality improvement, and research.

Because NIST has historically focused on metrological systems in the physical sciences, the immediate goal is only one of informing researchers at NIST as to the viability and potential value to be realized in analogous systems for the psychosocial sciences. No one understands the human, social, and economic value of measurement standards like NIST does.

Work that results in fundamental measures of psychosocial constructs should be proposed as areas deserving of NIST’s support. White Papers describing the “high risk-high reward” potential of Rasch applications might get them to start to consider the possibility of a whole new domain of metrics.

For more info, see http://www.nist.gov/tip/call_for_white_papers_sept09.pdf, and feel free to reference the arguments I made in the White Paper I submitted (www.livingcapitalmetrics.com/images/FisherNISTWhitePaper2.pdf), or in my recent paper in Measurement: 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.

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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.