Archive for January, 2011

Twelve principles I’m taking away from recent discussions

January 27, 2011
  1. Hypotheses non fingo A: Ideas about things are not hypothesized and tested against those things so much as things are determined to be what they are by testing them against ideas. Facts are recognizable as such only because they relate with a prior idea.
  2. Hypotheses non fingo B: Cohen’s introduction to Newton’s Opticks makes it plain that Newton is not offering a general methodological pointer in this phrase. Rather, he is answering critics who wanted him to explain what gravity is, and what it’s causes are. In saying, I feign no hypotheses, Newton is merely indicating that he’s not going to make up stories about something he knows nothing about. And in contrast with the Principia, the Opticks provides a much more accessible overview of the investigative process, from the initial engagement with light, where indeed no hypotheses as to its causes are offered, and onto more specific inquiries into its properties, where hypotheses necessarily inform experimental contrasts.
  3. Ideas, such as mathematical/geometrical theorems, natural laws, or the structure of Rasch models, do not exist and are unobservable. No triangle ever fits the Pythagorean theorem, there are no bodies left to themselves or balls rolling on frictionless planes, and there are no test, survey, or assessment results completely unaffected by the particular questions asked and persons answering.
  4. The clarity and transparency of an idea requires careful attention to the unity and sameness of the relevant class of things observed. So far as possible, the observational framework must be constrained by theory to produce observations likely to conform reasonably with the idea.
  5. New ideas come into language when a phenomenon or effect, often technically produced, exhibits persistent and stable properties across samples, observers, instruments, etc.
  6. New word-things that come into language, whether a galaxy, an element in the periodic table, a germ, or a psychosocial construct, may well have existed since the dawn of time and may well have exerted tangible effects on humans for millennia. They did not, however, do so for anyone in terms of the newly-available theory and understanding, which takes a place in a previously unoccupied position within the matrix of interrelated ideas, facts, and social networks.
  7. Number does not delimit the pure ideal concept of amount, but vice versa.
  8. Rasch models are one way of specifying the ideal form observations must approximate if they are to exhibit magnitude amounts divisible into ratios. Fitting data to such a model in the absence of a theory of the construct is only a very early step in the process of devising a measurement system.
  9. The invariant representation of a construct across samples, instruments, observers, etc. exhibiting magnitude amounts divisible into ratios provides the opportunity for allowing a pure ideal concept of amount to delimit number.
  10. Being suspended in language does not imply a denial of concrete reality and the separate independent existence of things. Rather, if those things did not exist, there would be no impetus for anything to come into words, and no criteria for meaningfulness.
  11. Situating objectivity in a sphere of signs removes the need for a separate sphere of facts constituted outside of language. Insofar as an ideal abstraction approximates convergence with and separation from different ways of expressing its meaning, an objective status owing nothing to a sphere of facts existing outside of language is obtained.
  12. The technology of a signifying medium (involving an alphabet, words as names for features of the environment, other symbols, syntactical and semantic rules, tools and instruments, etc.) gives rise to observations (data) that may exhibit regular patterns and that may come to be understood well enough to be reproduced at will via theory. Each facet (instrument, data, theory) mediates the relation of the other two.

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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.


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.


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

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

Burdick, H., & Stenner, A. J. (1996). Theoretical prediction of test items. Rasch Measurement Transactions, 10(1), 475 [].

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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Newton, Metaphysics, and Measurement

January 20, 2011

Though Newton claimed to deduce quantitative propositions from phenomena, the record shows that he brought a whole cartload of presuppositions to bear on his observations (White, 1997), such as his belief that Pythagoras was the discoverer of the inverse square law, his knowledge of Galileo’s freefall experiments, and his theological and astrological beliefs in occult actions at a distance. Without his immersion in this intellectual environment, he likely would not have been able to then contrive the appearance of deducing quantity from phenomena.

The second edition of the Principia, in which appears the phrase “hypotheses non fingo,” was brought out in part to respond to the charge that Newton had not offered any explanation of what gravity is. De Morgan, in particular, felt that Newton seemed to know more than he could prove (Keynes, 1946). But in his response to the critics, and in asserting that he feigns no hypotheses, Newton was making an important distinction between explaining the causes or composition of gravity and describing how it works. Newton was saying he did not rely on or make or test any hypotheses as to what gravity is; his only concern was with how it behaves. In due course, gravity came to be accepted as a fundamental feature of the universe in no need of explanation.

Heidegger (1977, p. 121) contends that Newton was, as is implied in the translation “I do not feign hypotheses,” saying in effect that the ground plan he was offering as a basis for experiment and practical application was not something he just made up. Despite Newton’s rejection of metaphysical explanations, the charge of not explaining gravity for what it is was being answered with a metaphysics of how, first, to derive the foundation for a science of precise predictive control from nature, and then resituate that foundation back within nature as an experimental method incorporating a mathematical plan or model. This was, of course, quite astute of Newton, as far as he went, but he stopped far short of articulating the background assumptions informing his methods.

Newton’s desire for a logic of experimental science led him to reject anything “metaphysical or physical, or based on occult qualities, or mechanical” as a foundation for proceeding. Following in Descartes’ wake, Newton then was satisfied to solidify the subject-object duality and to move forward on the basis of objective results that seemed to make metaphysics a thing of the past. Unfortunately, as Burtt (1954/1932, pp. 225-230) observes in this context, the only thing that can possibly happen when you presume discourse to be devoid of metaphysical assumptions is that your metaphysics is more subtly insinuated and communicated to others because it is not overtly presented and defended. Thus we have the history of logical positivism as the dominant philosophy of science.

It is relevant to recall here that Newton was known for strong and accurate intuitions, and strong and unorthodox religious views (he held the Lucasian Chair at Cambridge only by royal dispensation, as he was not Anglican). It must be kept in mind that Newton’s combination of personal characteristics was situated in the social context of the emerging scientific culture’s increasing tendency to prioritize results that could be objectively detached from the particular people, equipment, samples, etc. involved in their production (Shapin, 1989). Newton then had insights that, while remarkably accurate, could not be entirely derived from the evidence he offered and that, moreover, could not acceptably be explained informally, psychologically, or theologically.

What is absolutely fascinating about this constellation of factors is that it became a model for the conduct of science. Of course, Newton’s laws of motion were adopted as the hallmark of successful scientific modeling in the form of the Standard Model applied throughout physics in the nineteenth century (Heilbron, 1993). But so was the metaphysical positivist logic of a pure objectivism detached from everything personal, intuitive, metaphorical, social, economic, or religious (Burtt, 1954/1932).

Kuhn (1970) made a major contribution to dismantling this logic when he contrasted textbook presentations of the methodical production of scientific effects with the actual processes of cobbled-together fits and starts that are lived out in the work of practicing scientists. But much earlier, James Clerk Maxwell (1879, pp. 162-163) had made exactly the same observation in a contrast of the work of Ampere with that of Faraday:

“The experimental investigation by which Ampere established the laws of the mechanical action between electric currents is one of the most brilliant achievements in science. The whole, theory and experiment, seems as if it had leaped, full grown and full armed, from the brain of the ‘Newton of electricity.’ It is perfect in form, and unassailable in accuracy, and it is summed up in a formula from which all the phenomena may be deduced, and which must always remain the cardinal formula of electro-dynamics.

“The method of Ampere, however, though cast into an inductive form, does not allow us to trace the formation of the ideas which guided it. We can scarcely believe that Ampere really discovered the law of action by means of the experiments which he describes. We are led to suspect, what, indeed, he tells us himself* [Ampere’s Theorie…, p. 9], that he discovered the law by some process which he has not shewn us, and that when he had afterwards built up a perfect demonstration he removed all traces of the scaffolding by which he had raised it.

“Faraday, on the other hand, shews us his unsuccessful as well as his successful experiments, and his crude ideas as well as his developed ones, and the reader, however inferior to him in inductive power, feels sympathy even more than admiration, and is tempted to believe that, if he had the opportunity, he too would be a discoverer. Every student therefore should read Ampere’s research as a splendid example of scientific style in the statement of a discovery, but he should also study Faraday for the cultivation of a scientific spirit, by means of the action and reaction which will take place between newly discovered facts and nascent ideas in his own mind.”

Where does this leave us? In sum, Rasch emulated Ampere in two ways. He did so first in wanting to become the “Newton of reading,” or even the “Newton of psychosocial constructs,” when he sought to show that data from reading test items and readers are structured with an invariance analogous to that of data from instruments applying a force to an object with mass (Rasch, 1960, pp. 110-115). Rasch emulated Ampere again when, like Ampere, after building up a perfect demonstration of a reading law structured in the form of Newton’s second law, he did not report the means by which he had constructed test items capable of producing the data fitting the model, effectively removing all traces of the scaffolding.

The scaffolding has been reconstructed for reading (Stenner, et al., 2006) and has also been left in plain view by others doing analogous work involving other constructs (cognitive and moral development, mathematics ability, short-term memory, etc.). Dawson (2002), for instance, compares developmental scoring systems of varying sophistication and predictive control. And it may turn out that the plethora of uncritically applied Rasch analyses may turn out to be a capital resource for researchers interested in focusing on possible universal laws, predictive theories, and uniform metrics.

That is, published reports of calibration, error, and fit estimates open up opportunities for “pseudo-equating” (Beltyukova, Stone, & Fox, 2004; Fisher 1997, 1999) in their documentation of the invariance, or lack thereof, of constructs over samples and instruments. The evidence will point to a need for theoretical and metric unification directly analogous to what happened in the study and use of electricity in the nineteenth century:

“…’the existence of quantitative correlations between the various forms of energy, imposes upon men of science the duty of bringing all kinds of physical quantity to one common scale of comparison.’” [Schaffer, 1992, p. 26; quoting Everett 1881; see Smith & Wise 1989, pp. 684-4]

Qualitative and quantitative correlations in scaling results converged on a common construct in the domain of reading measurement through the 1960s and 1970s, culminating in the Anchor Test Study and the calibration of the National Reference Scale for Reading (Jaeger, 1973; Rentz & Bashaw, 1977). The lack of a predictive theory and the entirely empirical nature of the scale estimates prevented the scale from wide application, as the items in the tests that were equated were soon replaced with new items.

But the broad scale of the invariance observed across tests and readers suggests that some mechanism must be at work (Stenner, Stone, & Burdick, 2009), or that some form of life must be at play (Fisher, 2003a, 2003b, 2004, 2010a), structuring the data. Eventually, some explanation accounting for the structure ought to become apparent, as it did for reading (Stenner, Smith, & Burdick, 1983; Stenner, et al., 2006). This emergence of self-organizing structures repeatedly asserting themselves as independently existing real things is the medium of the message we need to hear. That message is that instruments play a very large and widely unrecognized role in science. By facilitating the routine production of mutually consistent, regularly observable, and comparable results they set the stage for theorizing, the emergence of consensus on what’s what, and uniform metrics (Daston & Galison, 2007; Hankins & Silverman, 1999; Latour, 1987, 2005; Wise, 1988, 1995). The form of Rasch’s models as extensions of Maxwell’s method of analogy (Fisher, 2010b) makes them particularly productive as a means of providing self-organizing invariances with a medium for their self-inscription. But that’s a story for another day.


Beltyukova, S. A., Stone, G. E., & Fox, C. M. (2004). Equating student satisfaction measures. Journal of Applied Measurement, 5(1), 62-9.

Burtt, E. A. (1954/1932). The metaphysical foundations of modern physical science (Rev. ed.) [First edition published in 1924]. Garden City, New York: Doubleday Anchor.

Daston, L., & Galison, P. (2007). Objectivity. Cambridge, MA: MIT Press.

Dawson, T. L. (2002, Summer). A comparison of three developmental stage scoring systems. Journal of Applied Measurement, 3(2), 146-89.

Fisher, W. P., Jr. (1997). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1999). Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. Journal of the Louisiana State Medical Society, 151(11), 566-578.

Fisher, W. P., Jr. (2003a, December). Mathematics, measurement, metaphor, metaphysics: Part I. Implications for method in postmodern science. Theory & Psychology, 13(6), 753-90.

Fisher, W. P., Jr. (2003b, December). Mathematics, measurement, metaphor, metaphysics: Part II. Accounting for Galileo’s “fateful omission.” Theory & Psychology, 13(6), 791-828.

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. (2010a). Reducible or irreducible? Mathematical reasoning and the ontological method. Journal of Applied Measurement, 11(1), 38-59.

Fisher, W. P., Jr. (2010b). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics: Conference Series, 238(1),

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

Jaeger, R. M. (1973). The national test equating study in reading (The Anchor Test Study). Measurement in Education, 4, 1-8.

Keynes, J. M. (1946, July). Newton, the man. (Speech given at the Celebration of the Tercentenary of Newton’s birth in 1642.) MacMillan St. Martin’s Press (London, England), The Collected Writings of John Maynard Keynes Volume X, 363-364.

Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago, Illinois: University of Chicago Press.

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.

Maxwell, J. C. (1879). Treatise on electricity and magnetism, Volumes I and II. London, England: Macmillan.

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.

Rentz, R. R., & Bashaw, W. L. (1977, Summer). The National Reference Scale for Reading: An application of the Rasch model. Journal of Educational Measurement, 14(2), 161-179.

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.

Shapin, S. (1989, November-December). The invisible technician. American Scientist, 77, 554-563.

Stenner, A. J., Burdick, H., Sanford, E. E., & Burdick, D. S. (2006). How accurate are Lexile text measures? Journal of Applied Measurement, 7(3), 307-22.

Stenner, A. J., Smith, M., III, & Burdick, D. S. (1983, Winter). Toward a theory of construct definition. Journal of Educational Measurement, 20(4), 305-316.

Stenner, A. J., Stone, M., & Burdick, D. (2009, Autumn). The concept of a measurement mechanism. Rasch Measurement Transactions, 23(2), 1204-1206.

White, M. (1997). Isaac Newton: The last sorcerer. New York: Basic Books.

Wise, M. N. (1988). Mediating machines. Science in Context, 2(1), 77-113.

Wise, M. N. (Ed.). (1995). The values of precision. Princeton, New Jersey: Princeton University Press.

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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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2010 in review

January 2, 2011

The stats helper monkeys at mulled over how this blog did in 2010, and here’s a high level summary of its overall blog health:

Healthy blog!

The Blog-Health-o-Meter™ reads This blog is doing awesome!.

Crunchy numbers

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A Boeing 747-400 passenger jet can hold 416 passengers. This blog was viewed about 2,800 times in 2010. That’s about 7 full 747s.


In 2010, there were 23 new posts, growing the total archive of this blog to 68 posts. There were 3 pictures uploaded, taking up a total of 419kb.

The busiest day of the year was June 24th with 83 views. The most popular post that day was Geometrical and algebraic expressions of scientific laws.

Where did they come from?

The top referring sites in 2010 were,,,, and

Some visitors came searching, mostly for reducing transaction costs, questions about measurement, the science of liberty reviews, global mush, and reasoning by analogy.

Attractions in 2010

These are the posts and pages that got the most views in 2010.


Geometrical and algebraic expressions of scientific laws April 2010
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Reinventing Capitalism: Diagramming Living Capital Flows in a Green, Sustainable, and Responsible Economy January 2010


The Role of Measurement in Reducing Transaction Costs and Creating Efficient Markets for Externalities March 2010


Questions about measurement: If it is so important, why…? January 2010


Review of “The Science of Liberty” by Timothy Ferris February 2010