Archive for the ‘technology road map’ Category

Moore’s Law at 50

May 13, 2015

Thomas Friedman interviewed Gordon Moore on the occasion of the 50th anniversary of Moore’s 1965 article predicting that computing power would exponentially increase at little additional cost. Moore’s ten-year prediction for the doubling rate of the numbers of transistors on microchips held up, and has now, with small adjustments, guided investments and expectations in electronics for five decades.

Friedman makes an especially important point, saying:

But let’s remember that it [Moore’s Law] was enabled by a group of remarkable scientists and engineers, in an America that did not just brag about being exceptional, but invested in the infrastructure and basic scientific research, and set the audacious goals, to make it so. If we want to create more Moore’s Law-like technologies, we need to invest in the building blocks that produced that America.”

These kinds of calls for investments in infrastructure and basic research, for new audacious goals, and for more Moore’s Law-like technologies are, of course, some of the primary and recurring themes of this blog (here, here, here, and here) and presentations and publications of the last several years. For instance, Miller and O’Leary’s (2007) close study of how Moore’s Law has aligned and coordinated investments in the electronics industry has been extrapolated into the education context (Fisher, 2012; Fisher & Stenner, 2011).

Education already has had over 60 years experience with a close parallel to Moore’s Law in reading measurement. Stenner’s Law retrospectively predicts exactly the same doubling period for the increasing numbers from 1960 to 2010 of children’s reading abilities measured in a common (or equatable) unit with known uncertainty and personalized consistency indicators. Knowledge of this kind has enabled manufacturers, suppliers, marketers, customers, and other stakeholders in the electronics industry to plan five and ten years into the future, preparing products and markets to take advantage of increased power and speed at the same or lower cost. Similarly, that same kind of knowledge could be used in education, health care, social services, and natural resource management to define the rules, roles, and responsibilities of actors and institutions involved in literacy, health, community, and natural capital markets.

Reading instruction, for example, requires text complexities to be matched to reader abilities at a comprehension rate that challenges but does not discourage the reader. Uniform grade-level textbooks are often too easy for a third of a given classroom, and too hard for another third. Individualized instruction by teachers in classrooms of 25 and more students is too cumbersome to implement. Connecting classroom reading assessments with known text complexity measures informed by judicious teacher input sets the stage for the realization of new potentials in educational outcomes. Electronic resources tapping existing text complexity measures for millions of articles and books connect individual students’ high stakes and classroom assessments in a common instructional framework (for instance, see here for an offering from Pearson). As the numbers of student reading measures made in a common unit continues to grow exponentially, capacities for connecting readers to texts, and for communicating about what works and what doesn’t in education, will grow as well.

This model is exactly the kind of infrastructure, basic scientific research, and audacious goal setting that’s needed if we are to succeed in creating more Moore’s Law-like technologies. If we as a society made the decision to invest deliberately, intentionally, and massively in infrastructure of this kind across education, health care, social services, and natural resource management, who knows what kinds of powerful results might be attained?

References

Fisher, W. P., Jr. (2012). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

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

Knowledge and skills as the currency of 21st-century economies

March 11, 2012

In his March 11, 2012 New York Times column, Thomas Friedman quotes the OECD’s Andreas Schleicher as saying, “knowledge and skills have become the global currency of 21st-century economies, but there is no central bank that prints this currency. Everyone has to decide on their own how much they will print.” This is a very interesting thing to say, especially because it reveals some common misconceptions about currency, capital, economics, and the institutions in which they are situated.

The question raised in many of the posts in this blog concerns just what kind of bank would print this currency, and what the currency would look like. The issue is of central economic importance, as Schleicher recognizes when he says that economic stimulus certainly has a place in countering a prolonged recession, but “the only sustainable way is to grow our way out by giving more people the knowledge and skills to compete, collaborate and connect in a way that drives our countries forward.”

Following through on the currency metaphor, obvious concerns that arise from Schleicher’s comments stem from the way he conflates the idea of a currency with the value it is supposed to represent. When he says individuals have to decide how much of the currency to print, what he means is they have to decide how much education they want to accrue. This is, of course, far different from simply printing money, which, when this is done and there is no value to back it up, is a sure way to bring about rampant inflation, as Germany learned in the 1920s. Schleicher and Friedman both know this, but the capacity of the metaphor to mislead may not be readily apparent.

Another concern that comes up is why there is no central bank printing the currency for us. Of course, it might seem as though we don’t need banks to print it for us, since, if individuals can print it, then why complicate things by bringing the banks into it? But note, again, that the focus here is on the currency, and nothing is said about the unit in which it is denominated.

The unit of value is the key to the deeper root problem, which is less one of increasing people’s stocks of skills and knowledge (though that is, of course, a great thing to do) and more one of creating the institutions and systems through which we can make order-of-magnitude improvements in the way people invest in and profit from their skills and knowledge. In other words, the problem is in having as many different currencies as there are individuals.

After all, what kind of an economy would we have if the value of the US dollars I hold was different from yours, and from everyone else’s? What if we all printed our own dollars and their value changed depending on who held them (or on how many we each printed)? Everyone would pay different amounts in the grocery store. We’d all spend half our time figuring out how to convert our own currency into someone else’s.

And this is pretty much what we do when it comes to trading on the value of our investments in stocks of knowledge, skills, health, motivations, and trust, loyalty, and commitment, some of the major forms of human and social capital. When we’re able, we put a recognized name brand behind our investments by attending a prestigious university or obtaining care at a hospital known for its stellar outcomes. But proxies like these just aggregate the currencies’ values at a bit higher level of dependence on the company you keep. It doesn’t do anything to solve the problem of actually providing transferable representations you can count on to retain a predictable value in any given exchange.

The crux of the problem is that today’s institutions define the markets in which we trade human and social capital in ways that make certain assumptions, and those assumptions are counterproductive relative to other assumptions that might be made. That is, the dominant form of economic discourse takes it for granted that markets are formed by the buying and selling activities of consumers and producers, which in turn dictates the form of institutions. But this gets the process backwards (Miller and O’Leary, 2007). Markets cannot form in the absence of institutions that define the roles, rules, and relationships embodied in economic exchange, as has been pointed out by Douglass North (1981, 1990), and a very large literature on institutional economics that has emerged from the work of North and his colleagues since the late 1970s.

And so, once again, this is why I keep repeating ad nauseum the same old lines in different ways. In this case, the repetition focuses on the institutions that “print” (so to speak) the currencies in which we express and trade economic and scientific values for mass or weight (kilograms and pounds), length (meters and yards), temperature (degrees Celsius and Fahrenheit), energy (kilowatts), etc. Economic growth and growth in scientific knowledge simultaneously erupted in the 19th century after metrological systems were created to inform trade in commodities and ideas. What we need today is a new investment of resources in the creation of a new array of standardized units for human, social, and natural capital. For more information, see prior posts in this blog, and the publications listed below.

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. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563 [http://www.livingcapitalmetrics.com/images/WP_Fisher_Jr_2000.pdf].

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. (2003). The mathematical metaphysics of measurement and metrology: Towards meaningful quantification in the human sciences. In A. Morales (Ed.), Renascent pragmatism: Studies in law and social science (pp. 118-53). Brookfield, VT: Ashgate Publishing Co.

Fisher, W. P., Jr. (2003). Measurement and communities of inquiry. Rasch Measurement Transactions, 17(3), 936-8 [http://www.rasch.org/rmt/rmt173.pdf].

Fisher, W. P., Jr. (2004, Thursday, January 22). Bringing capital to life via measurement: A contribution to the new economics. In  R. Smith (Chair), Session 3.3B. Rasch Models in Economics and Marketing. 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.

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.

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. (2004, Friday, July 2). Relational networks and trust in the measurement of social capital. Presented at the Twelfth International Objective Measurement Workshops, Cairns, Queensland, Australia: James Cook University.

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-179 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2005, August 1-3). Data standards for living human, social, and natural capital. In Session G: Concluding Discussion, Future Plans, Policy, etc. Conference on Entrepreneurship and Human Rights [http://www.fordham.edu/economics/vinod/ehr05.htm], Pope Auditorium, Lowenstein Bldg, Fordham University.

Fisher, W. P., Jr. (2006). Commercial measurement and academic research. Rasch Measurement Transactions, 20(2), 1058 [http://www.rasch.org/rmt/rmt202.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. (2007). Vanishing tricks and intellectualist condescension: Measurement, metrology, and the advancement of science. Rasch Measurement Transactions, 21(3), 1118-1121 [http://www.rasch.org/rmt/rmt213c.htm].

Fisher, W. P., Jr. (2008, 3-5 September). New metrological horizons: Invariant reference standards for instruments measuring human, social, and natural capital. Presented at the 12th IMEKO TC1-TC7 Joint Symposium on Man, Science, and Measurement, Annecy, France: University of Savoie.

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

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

Fisher, W. P.. Jr. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (Tech. Rep. No. http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), 49-66.

Fisher, W. P.. Jr. (2010, June 13-16). Rasch, Maxwell’s method of analogy, and the Chicago tradition. In  G. Cooper (Chair), Https://conference.cbs.dk/index.php/rasch/Rasch2010/paper/view/824. Probabilistic models for measurement in education, psychology, social science and health: Celebrating 50 years since the publication of Rasch’s Probabilistic Models.., University of Copenhagen School of Business, FUHU Conference Centre, Copenhagen, Denmark.

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

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

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

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

Fisher, W. P., Jr., Eubanks, R. L., & Marier, R. L. (1997, May). Health status measurement standards for electronic data sharing: Can the MOS SF36 and the LSU HSI physical functioning scales be equated?. Presented at the American Medical Informatics Association, San Jose, California.

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

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

Fisher, W. P., Jr., & Stenner, A. J. (2005, Tuesday, April 12). Creating a common market for the liberation of literacy capital. In  R. E. Schumacker (Chair), Rasch Measurement: Philosophical, Biological and Attitudinal Impacts. American Educational Research Association, Rasch Measurement SIG, Montreal, Canada.

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

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-34.

North, D. C. (1981). Structure and change in economic history. New York: W. W. Norton & Co.

North, D. C. (1990). Institutions, institutional change, and economic performance. New York: Cambridge University Press.

Creative Commons License
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.

A Second Simple Example of Measurement’s Role in Reducing Transaction Costs, Enhancing Market Efficiency, and Enables the Pricing of Intangible Assets

March 9, 2011

The prior post here showed why we should not confuse counts of things with measures of amounts, though counts are the natural starting place to begin constructing measures. That first simple example focused on an analogy between counting oranges and measuring the weight of oranges, versus counting correct answers on tests and measuring amounts of ability. This second example extends the first by, in effect, showing what happens when we want to aggregate value not just across different counts of some one thing but across different counts of different things. The point will be, in effect, to show how the relative values of apples, oranges, grapes, and bananas can be put into a common frame of reference and compared in a practical and convenient way.

For instance, you may go into a grocery store to buy raspberries and blackberries, and I go in to buy cantaloupe and watermelon. Your cost per individual fruit will be very low, and mine will be very high, but neither of us will find this annoying, confusing, or inconvenient because your fruits are very small, and mine, very large. Conversely, your cost per kilogram will be much higher than mine, but this won’t cause either of us any distress because we both recognize the differences in the labor, handling, nutritional, and culinary value of our purchases.

But what happens when we try to purchase something as complex as a unit of socioeconomic development? The eight UN Millennium Development Goals (MDGs) represent a start at a systematic effort to bring human, social, and natural capital together into the same economic and accountability framework as liquid and manufactured capital, and property. But that effort is stymied by the inefficiency and cost of making and using measures of the goals achieved. The existing MDG databases (http://data.un.org/Browse.aspx?d=MDG), and summary reports present overwhelming numbers of numbers. Individual indicators are presented for each year, each country, each region, and each program, goal by goal, target by target, indicator by indicator, and series by series, in an indigestible volume of data.

Though there are no doubt complex mathematical methods by which a philanthropic, governmental, or NGO investor might determine how much development is gained per million dollars invested, the cost of obtaining impact measures is so high that most funding decisions are made with little information concerning expected returns (Goldberg, 2009). Further, the percentages of various needs met by leading social enterprises typically range from 0.07% to 3.30%, and needs are growing, not diminishing. Progress at current rates means that it would take thousands of years to solve today’s problems of human suffering, social disparity, and environmental quality. The inefficiency of human, social, and natural capital markets is so overwhelming that there is little hope for significant improvements without the introduction of fundamental infrastructural supports, such as an Intangible Assets Metric System.

A basic question that needs to be asked of the MDG system is, how can anyone make any sense out of so much data? Most of the indicators are evaluated in terms of counts of the number of times something happens, the number of people affected, or the number of things observed to be present. These counts are usually then divided by the maximum possible (the count of the total population) and are expressed as percentages or rates.

As previously explained in various posts in this blog, counts and percentages are not measures in any meaningful sense. They are notoriously difficult to interpret, since the quantitative meaning of any given unit difference varies depending on the size of what is counted, or where the percentage falls in the 0-100 continuum. And because counts and percentages are interpreted one at a time, it is very difficult to know if and when any number included in the sheer mass of data is reasonable, all else considered, or if it is inconsistent with other available facts.

A study of the MDG data must focus on these three potential areas of data quality improvement: consistency evaluation, volume reduction, and interpretability. Each builds on the others. With consistent data lending themselves to summarization in sufficient statistics, data volume can be drastically reduced with no loss of information (Andersen, 1977, 1999; Wright, 1977, 1997), data quality can be readily assessed in terms of sufficiency violations (Smith, 2000; Smith & Plackner, 2009), and quantitative measures can be made interpretable in terms of a calibrated ruler’s repeatedly reproducible hierarchy of indicators (Bond & Fox, 2007; Masters, Lokan, & Doig, 1994).

The primary data quality criteria are qualitative relevance and meaningfulness, on the one hand, and mathematical rigor, on the other. The point here is one of following through on the maxim that we manage what we measure, with the goal of measuring in such a way that management is better focused on the program mission and not distracted by accounting irrelevancies.

Method

As written and deployed, each of the MDG indicators has the face and content validity of providing information on each respective substantive area of interest. But, as has been the focus of repeated emphases in this blog, counting something is not the same thing as measuring it.

Counts or rates of literacy or unemployment are not, in and of themselves, measures of development. Their capacity to serve as contributing indications of developmental progress is an empirical question that must be evaluated experimentally against the observable evidence. The measurement of progress toward an overarching developmental goal requires inferences made from a conceptual order of magnitude above and beyond that provided in the individual indicators. The calibration of an instrument for assessing progress toward the realization of the Millennium Development Goals requires, first, a reorganization of the existing data, and then an analysis that tests explicitly the relevant hypotheses as to the potential for quantification, before inferences supporting the comparison of measures can be scientifically supported.

A subset of the MDG data was selected from the MDG database available at http://data.un.org/Browse.aspx?d=MDG, recoded, and analyzed using Winsteps (Linacre, 2011). At least one indicator was selected from each of the eight goals, with 22 in total. All available data from these 22 indicators were recorded for each of 64 countries.

The reorganization of the data is nothing but a way of making the interpretation of the percentages explicit. The meaning of any one country’s percentage or rate of youth unemployment, cell phone users, or literacy has to be kept in context relative to expectations formed from other countries’ experiences. It would be nonsense to interpret any single indicator as good or bad in isolation. Sometimes 30% represents an excellent state of affairs, other times, a terrible one.

Therefore, the distributions of each indicator’s percentages across the 64 countries were divided into ranges and converted to ratings. A lower rating uniformly indicates a status further away from the goal than a higher rating. The ratings were devised by dividing the frequency distribution of each indicator roughly into thirds.

For instance, the youth unemployment rate was found to vary such that the countries furthest from the desired goal had rates of 25% and more(rated 1), and those closest to or exceeding the goal had rates of 0-10% (rated 3), leaving the middle range (10-25%) rated 2. In contrast, percentages of the population that are undernourished were rated 1 for 35% or more, 2 for 15-35%, and 3 for less than 15%.

Thirds of the distributions were decided upon only on the basis of the investigator’s prior experience with data of this kind. A more thorough approach to the data would begin from a finer-grained rating system, like that structuring the MDG table at http://mdgs.un.org/unsd/mdg/Resources/Static/Products/Progress2008/MDG_Report_2008_Progress_Chart_En.pdf. This greater detail would be sought in order to determine empirically just how many distinctions each indicator can support and contribute to the overall measurement system.

Sixty-four of the available 336 data points were selected for their representativeness, with no duplications of values and with a proportionate distribution along the entire continuum of observed values.

Data from the same 64 countries and the same years were then sought for the subsequent indicators. It turned out that the years in which data were available varied across data sets. Data within one or two years of the target year were sometimes substituted for missing data.

The data were analyzed twice, first with each indicator allowed its own rating scale, parameterizing each of the category difficulties separately for each item, and then with the full rating scale model, as the results of the first analysis showed all indicators shared strong consistency in the rating structure.

Results

Data were 65.2% complete. Countries were assessed on an average of 14.3 of the 22 indicators, and each indicator was applied on average to 41.7 of the 64 country cases. Measurement reliability was .89-.90, depending on how measurement error is estimated. Cronbach’s alpha for the by-country scores was .94. Calibration reliability was .93-.95. The rating scale worked well (see Linacre, 2002, for criteria). The data fit the measurement model reasonably well, with satisfactory data consistency, meaning that the hypothesis of a measurable developmental construct was not falsified.

The main result for our purposes here concerns how satisfactory data consistency makes it possible to dramatically reduce data volume and improve data interpretability. The figure below illustrates how. What does it mean for data volume to be drastically reduced with no loss of information? Let’s see exactly how much the data volume is reduced for the ten item data subset shown in the figure below.

The horizontal continuum from -100 to 1300 in the figure is the metric, the ruler or yardstick. The number of countries at various locations along that ruler is shown across the bottom of the figure. The mean (M), first standard deviation (S), and second standard deviation (T) are shown beneath the numbers of countries. There are ten countries with a measure of just below 400, just to the left of the mean (M).

The MDG indicators are listed on the right of the figure, with the indicator most often found being achieved relative to the goals at the bottom, and the indicator least often being achieved at the top. The ratings in the middle of the figure increase from 1 to 3 left to right as the probability of goal achievement increases as the measures go from low to high. The position of the ratings in the middle of the figure shifts from left to right as one reads up the list of indicators because the difficulty of achieving the goals is increasing.

Because the ratings of the 64 countries relative to these ten goals are internally consistent, nothing but the developmental level of the country and the developmental challenge of the indicator affects the probability that a given rating will be attained. It is this relation that defines fit to a measurement model, the sufficiency of the summed ratings, and the interpretability of the scores. Given sufficient fit and consistency, any country’s measure implies a given rating on each of the ten indicators.

For instance, imagine a vertical line drawn through the figure at a measure of 500, just above the mean (M). This measure is interpreted relative to the places at which the vertical line crosses the ratings in each row associated with each of the ten items. A measure of 500 is read as implying, within a given range of error, uncertainty, or confidence, a rating of

  • 3 on debt service and female-to-male parity in literacy,
  • 2 or 3 on how much of the population is undernourished and how many children under five years of age are moderately or severely underweight,
  • 2 on infant mortality, the percent of the population aged 15 to 49 with HIV, and the youth unemployment rate,
  • 1 or 2 the poor’s share of the national income, and
  • 1 on CO2 emissions and the rate of personal computers per 100 inhabitants.

For any one country with a measure of 500 on this scale, ten percentages or rates that appear completely incommensurable and incomparable are found to contribute consistently to a single valued function, developmental goal achievement. Instead of managing each separate indicator as a universe unto itself, this scale makes it possible to manage development itself at its own level of complexity. This ten-to-one ratio of reduced data volume is more than doubled when the total of 22 items included in the scale is taken into account.

This reduction is conceptually and practically important because it focuses attention on the actual object of management, development. When the individual indicators are the focus of attention, the forest is lost for the trees. Those who disparage the validity of the maxim, you manage what you measure, are often discouraged by the the feeling of being pulled in too many directions at once. But a measure of the HIV infection rate is not in itself a measure of anything but the HIV infection rate. Interpreting it in terms of broader developmental goals requires evidence that it in fact takes a place in that larger context.

And once a connection with that larger context is established, the consistency of individual data points remains a matter of interest. As the world turns, the order of things may change, but, more likely, data entry errors, temporary data blips, and other factors will alter data quality. Such changes cannot be detected outside of the context defined by an explicit interpretive framework that requires consistent observations.

-100  100     300     500     700     900    1100    1300
|-------+-------+-------+-------+-------+-------+-------|  NUM   INDCTR
1                                 1  :    2    :  3     3    9  PcsPer100
1                         1   :   2    :   3            3    8  CO2Emissions
1                    1  :    2    :   3                 3   10  PoorShareNatInc
1                 1  :    2    :  3                     3   19  YouthUnempRatMF
1              1   :    2   :   3                       3    1  %HIV15-49
1            1   :   2    :   3                         3    7  InfantMortality
1          1  :    2    :  3                            3    4  ChildrenUnder5ModSevUndWgt
1         1   :    2    :  3                            3   12  PopUndernourished
1    1   :    2   :   3                                 3    6  F2MParityLit
1   :    2    :  3                                      3    5  DebtServExpInc
|-------+-------+-------+-------+-------+-------+-------|  NUM   INDCTR
-100  100     300     500     700     900    1100    1300
                   1
       1   1 13445403312323 41 221    2   1   1            COUNTRIES
       T      S       M      S       T

Discussion

A key element in the results obtained here concerns the fact that the data were about 35% missing. Whether or not any given indicator was actually rated for any given country, the measure can still be interpreted as implying the expected rating. This capacity to take missing data into account can be taken advantage of systematically by calibrating a large bank of indicators. With this in hand, it becomes possible to gather only the amount of data needed to make a specific determination, or to adaptively administer the indicators so as to obtain the lowest-error (most reliable) measure at the lowest cost (with the fewest indicators administered). Perhaps most importantly, different collections of indicators can then be equated to measure in the same unit, so that impacts may be compared more efficiently.

Instead of an international developmental aid market that is so inefficient as to preclude any expectation of measured returns on investment, setting up a calibrated bank of indicators to which all measures are traceable opens up numerous desirable possibilities. The cost of assessing and interpreting the data informing aid transactions could be reduced to negligible amounts, and the management of the processes and outcomes in which that aid is invested would be made much more efficient by reduced data volume and enhanced information content. Because capital would flow more efficiently to where supply is meeting demand, nonproducers would be cut out of the market, and the effectiveness of the aid provided would be multiplied many times over.

The capacity to harmonize counts of different but related events into a single measurement system presents the possibility that there may be a bright future for outcomes-based budgeting in education, health care, human resource management, environmental management, housing, corrections, social services, philanthropy, and international development. It may seem wildly unrealistic to imagine such a thing, but the return on the investment would be so monumental that not checking it out would be even crazier.

A full report on the MDG data, with the other references cited, is available on my SSRN page at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1739386.

Goldberg, S. H. (2009). Billions of drops in millions of buckets: Why philanthropy doesn’t advance social progress. New York: Wiley.

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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.

A Simple Example of How Better Measurement Creates New Market Efficiencies, Reduces Transaction Costs, and Enables the Pricing of Intangible Assets

March 4, 2011

One of the ironies of life is that we often overlook the obvious in favor of the obscure. And so one hears of huge resources poured into finding and capitalizing on opportunities that provide infinitesimally small returns, while other opportunities—with equally certain odds of success but far more profitable returns—are completely neglected.

The National Institute for Standards and Technology (NIST) reports returns on investment ranging from 32% to over 400% in 32 metrological improvements made in semiconductors, construction, automation, computers, materials, manufacturing, chemicals, photonics, communications and pharmaceuticals (NIST, 2009). Previous posts in this blog offer more information on the economic value of metrology. The point is that the returns obtained from improvements in the measurement of tangible assets will likely also be achieved in the measurement of intangible assets.

How? With a little bit of imagination, each stage in the development of increasingly meaningful, efficient, and useful measures described in this previous post can be seen as implying a significant return on investment. As those returns are sought, investors will coordinate and align different technologies and resources relative to a roadmap of how these stages are likely to unfold in the future, as described in this previous post. The basic concepts of how efficient and meaningful measurement reduces transaction costs and market frictions, and how it brings capital to life, are explained and documented in my publications (Fisher, 2002-2011), but what would a concrete example of the new value created look like?

The examples I have in mind hinge on the difference between counting and measuring. Counting is a natural and obvious thing to do when we need some indication of how much of something there is. But counting is not measuring (Cooper & Humphry, 2010; Wright, 1989, 1992, 1993, 1999). This is not some minor academic distinction of no practical use or consequence. It is rather the source of the vast majority of the problems we have in comparing outcome and performance measures.

Imagine how things would be if we couldn’t weigh fruit in a grocery store, and all we could do was count pieces. We can tell when eight small oranges possess less overall mass of fruit than four large ones by weighing them; the eight small oranges might weigh .75 kilograms (about 1.6 pounds) while the four large ones come in at 1.0 kilo (2.2 pounds). If oranges were sold by count instead of weight, perceptive traders would buy small oranges and make more money selling them than they could if they bought large ones.

But we can’t currently arrive so easily at the comparisons we need when we’re buying and selling intangible assets, like those produced as the outcomes of educational, health care, or other services. So I want to walk through a couple of very down-to-earth examples to bring the point home. Today we’ll focus on the simplest version of the story, and tomorrow we’ll take up a little more complicated version, dealing with the counts, percentages, and scores used in balanced scorecard and dashboard metrics of various kinds.

What if you score eight on one reading test and I score four on a different reading test? Who has more reading ability? In the same way that we might be able to tell just by looking that eight small oranges are likely to have less actual orange fruit than four big ones, we might also be able to tell just by looking that eight easy (short, common) words can likely be read correctly with less reading ability than four difficult (long, rare) words can be.

So let’s analyze the difference between buying oranges and buying reading ability. We’ll set up three scenarios for buying reading ability. In all three, we’ll imagine we’re comparing how we buy oranges with the way we would have to go about buying reading ability today if teachers were paid for the gains made on the tests they administer at the beginning and end of the school year.

In the first scenario, the teachers make up their own tests. In the second, the teachers each use a different standardized test. In the third, each teacher uses a computer program that draws questions from the same online bank of precalibrated items to construct a unique test custom tailored to each student. Reading ability scenario one is likely the most commonly found in real life. Scenario three is the rarest, but nonetheless describes a situation that has been available to millions of students in the U.S., Australia, and elsewhere for several years. Scenarios one, two and three correspond with developmental levels one, three, and five described in a previous blog entry.

Buying Oranges

When you go into one grocery store and I go into another, we don’t have any oranges with us. When we leave, I have eight and you have four. I have twice as many oranges as you, but yours weigh a kilo, about a third more than mine (.75 kilos).

When we paid for the oranges, the transaction was finished in a few seconds. Neither one of us experienced any confusion, annoyance, or inconvenience in relation to the quality of information we had on the amount of orange fruits we were buying. I did not, however, pay twice as much as you did. In fact, you paid more for yours than I did for mine, in direct proportion to the difference in the measured amounts.

No negotiations were necessary to consummate the transactions, and there was no need for special inquiries about how much orange we were buying. We knew from experience in this and other stores that the prices we paid were comparable with those offered in other times and places. Our information was cheap, as it was printed on the bag of oranges or could be read off a scale, and it was very high quality, as the measures were directly comparable with measures from any other scale in any other store. So, in buying oranges, the impact of information quality on the overall cost of the transaction was so inexpensive as to be negligible.

Buying Reading Ability (Scenario 1)

So now you and I go through third grade as eight year olds. You’re in one school and I’m in another. We have different teachers. Each teacher makes up his or her own reading tests. When we started the school year, we each took a reading test (different ones), and we took another (again, different ones) as we ended the school year.

For each test, your teacher counted up your correct answers and divided by the total number of questions; so did mine. You got 72% correct on the first one, and 94% correct on the last one. I got 83% correct on the first one, and 86% correct on the last one. Your score went up 22%, much more than the 3% mine went up. But did you learn more? It is impossible to tell. What if both of your tests were easier—not just for you or for me but for everyone—than both of mine? What if my second test was a lot harder than my first one? On the other hand, what if your tests were harder than mine? Perhaps you did even better than your scores seem to indicate.

We’ll just exclude from consideration other factors that might come to bear, such as whether your tests were significantly longer or shorter than mine, or if one of us ran out of time and did not answer a lot of questions.

If our parents had to pay the reading teacher at the end of the school year for the gains that were made, how would they tell what they were getting for their money? What if your teacher gave a hard test at the start of the year and an easy one at the end of the year so that you’d have a big gain and your parents would have to pay more? What if my teacher gave an easy test at the start of the year and a hard one at the end, so that a really high price could be put on very small gains? If our parents were to compare their experiences in buying our improved reading ability, they would have a lot of questions about how much improvement was actually obtained. They would be confused and annoyed at how inconvenient the scores are, because they are difficult, if not impossible, to compare. A lot of time and effort might be invested in examining the words and sentences in each of the four reading tests to try to determine how easy or hard they are in relation to each other. Or, more likely, everyone would throw their hands up and pay as little as they possibly can for outcomes they don’t understand.

Buying Reading Ability (Scenario 2)

In this scenario, we are third graders again, in different schools with different reading teachers. Now, instead of our teachers making up their own tests, our reading abilities are measured at the beginning and the end of the school year using two different standardized tests sold by competing testing companies. You’re in a private suburban school that’s part of an independent schools association. I’m in a public school along with dozens of others in an urban school district.

For each test, our parents received a report in the mail showing our scores. As before, we know how many questions we each answered correctly, and, unlike before, we don’t know which particular questions we got right or wrong. Finally, we don’t know how easy or hard your tests were relative to mine, but we know that the two tests you took were equated, and so were the two I took. That means your tests will show how much reading ability you gained, and so will mine.

We have one new bit of information we didn’t have before, and that’s a percentile score. Now we know that at the beginning of the year, with a percentile ranking of 72, you performed better than 72% of the other private school third graders taking this test, and at the end of the year you performed better than 76% of them. In contrast, I had percentiles of 84 and 89.

The question we have to ask now is if our parents are going to pay for the percentile gain, or for the actual gain in reading ability. You and I each learned more than our peers did on average, since our percentile scores went up, but this would not work out as a satisfactory way to pay teachers. Averages being averages, if you and I learned more and faster, someone else learned less and slower, so that, in the end, it all balances out. Are we to have teachers paying parents when their children learn less, simply redistributing money in a zero sum game?

And so, additional individualized reports are sent to our parents by the testing companies. Your tests are equated with each other, and they measure in a comparable unit that ranges from 120 to 480. You had a starting score of 235 and finished the year with a score of 420, for a gain of 185.

The tests I took are comparable and measure in the same unit, too, but not the same unit as your tests measure in. Scores on my tests range from 400 to 1200. I started the year with a score of 790, and finished at 1080, for a gain of 290.

Now the confusion in the first scenario is overcome, in part. Our parents can see that we each made real gains in reading ability. The difficulty levels of the two tests you took are the same, as are the difficulties of the two tests I took. But our parents still don’t know what to pay the teacher because they can’t tell if you or I learned more. You had lower percentiles and test scores than I did, but you are being compared with what is likely a higher scoring group of suburban and higher socioeconomic status students than the urban group of disadvantaged students I’m compared against. And your scores aren’t comparable with mine, so you might have started and finished with more reading ability than I did, or maybe I had more than you. There isn’t enough information here to tell.

So, again, the information that is provided is insufficient to the task of settling on a reasonable price for the outcomes obtained. Our parents will again be annoyed and confused by the low quality information that makes it impossible to know what to pay the teacher.

Buying Reading Ability (Scenario 3)

In the third scenario, we are still third graders in different schools with different reading teachers. This time our reading abilities are measured by tests that are completely unique. Every student has a test custom tailored to their particular ability. Unlike the tests in the first and second scenarios, however, now all of the tests have been constructed carefully on the basis of extensive data analysis and experimental tests. Different testing companies are providing the service, but they have gone to the trouble to work together to create consensus standards defining the unit of measurement for any and all reading test items.

For each test, our parents received a report in the mail showing our measures. As before, we know how many questions we each answered correctly. Now, though we don’t know which particular questions we got right or wrong, we can see typical items ordered by difficulty lined up in a way that shows us what kind of items we got wrong, and which kind we got right. And now we also know your tests were equated relative to mine, so we can compare how much reading ability you gained relative to how much I gained. Now our parents can confidently determine how much they should pay the teacher, at least in proportion to their children’s relative measures. If our measured gains are equal, the same payment can be made. If one of us obtained more value, then proportionately more should be paid.

In this third scenario, we have a situation directly analogous to buying oranges. You have a measured amount of increased reading ability that is expressed in the same unit as my gain in reading ability, just as the weights of the oranges are comparable. Further, your test items were not identical with mine, and so the difficulties of the items we took surely differed, just as the sizes of the oranges we bought did.

This third scenario could be made yet more efficient by removing the need for creating and maintaining a calibrated item bank, as described by Stenner and Stone (2003) and in the sixth developmental level in a prior blog post here. Also, additional efficiencies could be gained by unifying the interpretation of the reading ability measures, so that progress through high school can be tracked with respect to the reading demands of adult life (Williamson, 2008).

Comparison of the Purchasing Experiences

In contrast with the grocery store experience, paying for increased reading ability in the first scenario is fraught with low quality information that greatly increases the cost of the transactions. The information is of such low quality that, of course, hardly anyone bothers to go to the trouble to try to decipher it. Too much cost is associated with the effort to make it worthwhile. So, no one knows how much gain in reading ability is obtained, or what a unit gain might cost.

When a school district or educational researchers mount studies to try to find out what it costs to improve reading ability in third graders in some standardized unit, they find so much unexplained variation in the costs that they, too, raise more questions than answers.

In grocery stores and other markets, we don’t place the cost of making the value comparison on the consumer or the merchant. Instead, society as a whole picks up the cost by funding the creation and maintenance of consensus standard metrics. Until we take up the task of doing the same thing for intangible assets, we cannot expect human, social, and natural capital markets to obtain the efficiencies we take for granted in markets for tangible assets and property.

References

Cooper, G., & Humphry, S. M. (2010). The ontological distinction between units and entities. Synthese, pp. DOI 10.1007/s11229-010-9832-1.

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. (2003). Measurement and communities of inquiry. Rasch Measurement Transactions, 17(3), 936-8 [http://www.rasch.org/rmt/rmt173.pdf].

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

Fisher, W. P.. Jr. (2009b). NIST Critical national need idea White Paper: Metrological infrastructure for human, social, and natural capital (Tech. Rep., http://www.livingcapitalmetrics.com/images/FisherNISTWhitePaper2.pdf). New Orleans: LivingCapitalMetrics.com.

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), in press.

NIST. (2009, 20 July). Outputs and outcomes of NIST laboratory research. Available: http://www.nist.gov/director/planning/studies.cfm (Accessed 1 March 2011).

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].

Williamson, G. L. (2008). A text readability continuum for postsecondary readiness. Journal of Advanced Academics, 19(4), 602-632.

Wright, B. D. (1989). Rasch model from counting right answers: Raw scores as sufficient statistics. Rasch Measurement Transactions, 3(2), 62 [http://www.rasch.org/rmt/rmt32e.htm].

Wright, B. D. (1992, Summer). Scores are not measures. Rasch Measurement Transactions, 6(1), 208 [http://www.rasch.org/rmt/rmt61n.htm].

Wright, B. D. (1993). Thinking with raw scores. Rasch Measurement Transactions, 7(2), 299-300 [http://www.rasch.org/rmt/rmt72r.htm].

Wright, B. D. (1999). Common sense for measurement. Rasch Measurement Transactions, 13(3), 704-5  [http://www.rasch.org/rmt/rmt133h.htm].

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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.

 

One of the ironies of life is that we often overlook the obvious in favor of the obscure. And so one hears of huge resources poured into finding and capitalizing on opportunities that provide infinitesimally small returns, while other opportunities—with equally certain odds of success but far more profitable returns—are completely neglected.

The National Institute for Standards and Technology (NIST) reports returns on investment ranging from 32% to over 400% in 32 metrological improvements made in semiconductors, construction, automation, computers, materials, manufacturing, chemicals, photonics, communications and pharmaceuticals (NIST, 2009). Previous posts in this blog offer more information on the economic value of metrology. The point is that the returns obtained from improvements in the measurement of tangible assets will likely also be achieved in the measurement of intangible assets.

How? With a little bit of imagination, each stage in the development of increasingly meaningful, efficient, and useful measures described in this previous post can be seen as implying a significant return on investment. As those returns are sought, investors will coordinate and align different technologies and resources relative to a roadmap of how these stages are likely to unfold in the future, as described in this previous post. But what would a concrete example of the new value created look like?

The examples I have in mind hinge on the difference between counting and measuring. Counting is a natural and obvious thing to do when we need some indication of how much of something there is. But counting is not measuring (Cooper & Humphry, 2010; Wright, 1989, 1992, 1993, 1999). This is not some minor academic distinction of no practical use or consequence. It is rather the source of the vast majority of the problems we have in comparing outcome and performance measures.

Imagine how things would be if we couldn’t weigh fruit in a grocery store, and all we could do was count pieces. We can tell when eight small oranges possess less overall mass of fruit than four large ones by weighing them; the eight small oranges might weigh .75 kilograms (about 1.6 pounds) while the four large ones come in at 1.0 kilo (2.2 pounds). If oranges were sold by count instead of weight, perceptive traders would buy small oranges and make more money selling them than they could if they bought large ones.

But we can’t currently arrive so easily at the comparisons we need when we’re buying and selling intangible assets, like those produced as the outcomes of educational, health care, or other services. So I want to walk through a couple of very down-to-earth examples to bring the point home. Today we’ll focus on the simplest version of the story, and tomorrow we’ll take up a little more complicated version, dealing with the counts, percentages, and scores used in balanced scorecard and dashboard metrics of various kinds.

What if you score eight on one reading test and I score four on a different reading test? Who has more reading ability? In the same way that we might be able to tell just by looking that eight small oranges are likely to have less actual orange fruit than four big ones, we might also be able to tell just by looking that eight easy (short, common) words can likely be read correctly with less reading ability than four difficult (long, rare) words can be.

So let’s analyze the difference between buying oranges and buying reading ability. We’ll set up three scenarios for buying reading ability. In all three, we’ll imagine we’re comparing how we buy oranges with the way we would have to go about buying reading ability today if teachers were paid for the gains made on the tests they administer at the beginning and end of the school year.

In the first scenario, the teachers make up their own tests. In the second, the teachers each use a different standardized test. In the third, each teacher uses a computer program that draws questions from the same online bank of precalibrated items to construct a unique test custom tailored to each student. Reading ability scenario one is likely the most commonly found in real life. Scenario three is the rarest, but nonetheless describes a situation that has been available to millions of students in the U.S., Australia, and elsewhere for several years. Scenarios one, two and three correspond with developmental levels one, three, and five described in a previous blog entry.

Buying Oranges

When you go into one grocery store and I go into another, we don’t have any oranges with us. When we leave, I have eight and you have four. I have twice as many oranges as you, but yours weigh a kilo, about a third more than mine (.75 kilos).

When we paid for the oranges, the transaction was finished in a few seconds. Neither one of us experienced any confusion, annoyance, or inconvenience in relation to the quality of information we had on the amount of orange fruits we were buying. I did not, however, pay twice as much as you did. In fact, you paid more for yours than I did for mine, in direct proportion to the difference in the measured amounts.

No negotiations were necessary to consummate the transactions, and there was no need for special inquiries about how much orange we were buying. We knew from experience in this and other stores that the prices we paid were comparable with those offered in other times and places. Our information was cheap, as it was printed on the bag of oranges or could be read off a scale, and it was very high quality, as the measures were directly comparable with measures from any other scale in any other store. So, in buying oranges, the impact of information quality on the overall cost of the transaction was so inexpensive as to be negligible.

Buying Reading Ability (Scenario 1)

So now you and I go through third grade as eight year olds. You’re in one school and I’m in another. We have different teachers. Each teacher makes up his or her own reading tests. When we started the school year, we each took a reading test (different ones), and we took another (again, different ones) as we ended the school year.

For each test, your teacher counted up your correct answers and divided by the total number of questions; so did mine. You got 72% correct on the first one, and 94% correct on the last one. I got 83% correct on the first one, and 86% correct on the last one. Your score went up 22%, much more than the 3% mine went up. But did you learn more? It is impossible to tell. What if both of your tests were easier—not just for you or for me but for everyone—than both of mine? What if my second test was a lot harder than my first one? On the other hand, what if your tests were harder than mine? Perhaps you did even better than your scores seem to indicate.

We’ll just exclude from consideration other factors that might come to bear, such as whether your tests were significantly longer or shorter than mine, or if one of us ran out of time and did not answer a lot of questions.

If our parents had to pay the reading teacher at the end of the school year for the gains that were made, how would they tell what they were getting for their money? What if your teacher gave a hard test at the start of the year and an easy one at the end of the year so that you’d have a big gain and your parents would have to pay more? What if my teacher gave an easy test at the start of the year and a hard one at the end, so that a really high price could be put on very small gains? If our parents were to compare their experiences in buying our improved reading ability, they would have a lot of questions about how much improvement was actually obtained. They would be confused and annoyed at how inconvenient the scores are, because they are difficult, if not impossible, to compare. A lot of time and effort might be invested in examining the words and sentences in each of the four reading tests to try to determine how easy or hard they are in relation to each other. Or, more likely, everyone would throw their hands up and pay as little as they possibly can for outcomes they don’t understand.

Buying Reading Ability (Scenario 2)

In this scenario, we are third graders again, in different schools with different reading teachers. Now, instead of our teachers making up their own tests, our reading abilities are measured at the beginning and the end of the school year using two different standardized tests sold by competing testing companies. You’re in a private suburban school that’s part of an independent schools association. I’m in a public school along with dozens of others in an urban school district.

For each test, our parents received a report in the mail showing our scores. As before, we know how many questions we each answered correctly, and, as before, we don’t know which particular questions we got right or wrong. Finally, we don’t know how easy or hard your tests were relative to mine, but we know that the two tests you took were equated, and so were the two I took. That means your tests will show how much reading ability you gained, and so will mine.

But we have one new bit of information we didn’t have before, and that’s a percentile score. Now we know that at the beginning of the year, with a percentile ranking of 72, you performed better than 72% of the other private school third graders taking this test, and at the end of the year you performed better than 76% of them. In contrast, I had percentiles of 84 and 89.

The question we have to ask now is if our parents are going to pay for the percentile gain, or for the actual gain in reading ability. You and I each learned more than our peers did on average, since our percentile scores went up, but this would not work out as a satisfactory way to pay teachers. Averages being averages, if you and I learned more and faster, someone else learned less and slower, so that, in the end, it all balances out. Are we to have teachers paying parents when their children learn less, simply redistributing money in a zero sum game?

And so, additional individualized reports are sent to our parents by the testing companies. Your tests are equated with each other, so they measure in a comparable unit that ranges from 120 to 480. You had a starting score of 235 and finished the year with a score of 420, for a gain of 185.

The tests I took are comparable and measure in the same unit, too, but not the same unit as your tests measure in. Scores on my tests range from 400 to 1200. I started the year with a score of 790, and finished at 1080, for a gain of 290.

Now the confusion in the first scenario is overcome, in part. Our parents can see that we each made real gains in reading ability. The difficulty levels of the two tests you took are the same, as are the difficulties of the two tests I took. But our parents still don’t know what to pay the teacher because they can’t tell if you or I learned more. You had lower percentiles and test scores than I did, but you are being compared with what is likely a higher scoring group of suburban and higher socioeconomic status students than the urban group of disadvantaged students I’m compared against. And your scores aren’t comparable with mine, so you might have started and finished with more reading ability than I did, or maybe I had more than you. There isn’t enough information here to tell.

So, again, the information that is provided is insufficient to the task of settling on a reasonable price for the outcomes obtained. Our parents will again be annoyed and confused by the low quality information that makes it impossible to know what to pay the teacher.

Buying Reading Ability (Scenario 3)

In the third scenario, we are still third graders in different schools with different reading teachers. This time our reading abilities are measured by tests that are completely unique. Every student has a test custom tailored to their particular ability. Unlike the tests in the first and second scenarios, however, now all of the tests have been constructed carefully on the basis of extensive data analysis and experimental tests. Different testing companies are providing the service, but they have gone to the trouble to work together to create consensus standards defining the unit of measurement for any and all reading test items.

For each test, our parents received a report in the mail showing our measures. As before, we know how many questions we each answered correctly. Now, though we don’t know which particular questions we got right or wrong, we can see typical items ordered by difficulty lined up in a way that shows us what kind of items we got wrong, and which kind we got right. And now we also know your tests were equated relative to mine, so we can compare how much reading ability you gained relative to how much I gained. Now our parents can confidently determine how much they should pay the teacher, at least in proportion to their children’s relative measures. If our measured gains are equal, the same payment can be made. If one of us obtained more value, then proportionately more should be paid.

In this third scenario, we have a situation directly analogous to buying oranges. You have a measured amount of increased reading ability that is expressed in the same unit as my gain in reading ability, just as the weights of the oranges are comparable. Further, your test items were not identical with mine, and so the difficulties of the items we took surely differed, just as the sizes of the oranges we bought did.

This third scenario could be made yet more efficient by removing the need for creating and maintaining a calibrated item bank, as described by Stenner and Stone (2003) and in the sixth developmental level in a prior blog post here. Also, additional efficiencies could be gained by unifying the interpretation of the reading ability measures, so that progress through high school can be tracked with respect to the reading demands of adult life (Williamson, 2008).

Comparison of the Purchasing Experiences

In contrast with the grocery store experience, paying for increased reading ability in the first scenario is fraught with low quality information that greatly increases the cost of the transactions. The information is of such low quality that, of course, hardly anyone bothers to go to the trouble to try to decipher it. Too much cost is associated with the effort to make it worthwhile. So, no one knows how much gain in reading ability is obtained, or what a unit gain might cost.

When a school district or educational researchers mount studies to try to find out what it costs to improve reading ability in third graders in some standardized unit, they find so much unexplained variation in the costs that they, too, raise more questions than answers.

But we don’t place the cost of making the value comparison on the consumer or the merchant in the grocery store. Instead, society as a whole picks up the cost by funding the creation and maintenance of consensus standard metrics. Until we take up the task of doing the same thing for intangible assets, we cannot expect human, social, and natural capital markets to obtain the efficiencies we take for granted in markets for tangible assets and property.

References

Cooper, G., & Humphry, S. M. (2010). The ontological distinction between units and entities. Synthese, pp. DOI 10.1007/s11229-010-9832-1.

NIST. (2009, 20 July). Outputs and outcomes of NIST laboratory research. Available: http://www.nist.gov/director/planning/studies.cfm (Accessed 1 March 2011).

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].

Williamson, G. L. (2008). A text readability continuum for postsecondary readiness. Journal of Advanced Academics, 19(4), 602-632.

Wright, B. D. (1989). Rasch model from counting right answers: Raw scores as sufficient statistics. Rasch Measurement Transactions, 3(2), 62 [http://www.rasch.org/rmt/rmt32e.htm].

Wright, B. D. (1992, Summer). Scores are not measures. Rasch Measurement Transactions, 6(1), 208 [http://www.rasch.org/rmt/rmt61n.htm].

Wright, B. D. (1993). Thinking with raw scores. Rasch Measurement Transactions, 7(2), 299-300 [http://www.rasch.org/rmt/rmt72r.htm].

Wright, B. D. (1999). Common sense for measurement. Rasch Measurement Transactions, 13(3), 704-5  [http://www.rasch.org/rmt/rmt133h.htm].

A Technology Road Map for Efficient Intangible Assets Markets

February 24, 2011

Scientific technologies, instruments and conceptual images have been found to play vitally important roles in economic success because of the way they enable accurate predictions of future industry and market states (Miller & O’Leary, 2007). The technology road map for the microprocessor industry, based in Moore’s Law, has successfully guided market expectations and coordinated research investment decisions for over 40 years. When the earlier electromechanical, relay, vacuum tube, and transistor computing technology paradigms are included, the same trajectory has dominated the computer industry for over 100 years (Kurzweil, 2005, pp. 66-67).

We need a similar technology road map to guide the creation and development of intangible asset markets for human, social, and natural (HSN) capital. This will involve intensive research on what the primary constructs are, determining what is measurable and what is not, creating consensus standards for uniform metrics and the metrology networks through which those standards will function. Alignments with these developments will require comprehensively integrated economic models, accounting frameworks, and investment platforms, in addition to specific applications deploying the capital formations.

What I’m proposing is, in a sense, just an extension in a new direction of the metrology challenges and issues summarized in Table ITWG15 on page 48 in the 2010 update to the International Technology Roadmap for Semiconductors (http://www.itrs.net/about.html). Distributed electronic communication facilitated by computers and the Internet is well on the way to creating a globally uniform instantaneous information network. But much of what needs to be communicated through this network remains expressed in locally defined languages that lack common points of reference. Meaningful connectivity demands a shared language.

To those who say we already have the technology necessary and sufficient to the measurement and management of human, social, and natural capital, I say think again. The difference between what we have and what we need is the same as the difference between (a) an economy whose capital resources are not represented in transferable representations like titles and deeds, and that are denominated in a flood of money circulating in different currencies, and, (b) an economy whose capital resources are represented in transferable documents and are traded using a single currency with a restricted money supply. The measurement of intangible assets is today akin to the former economy, with little actual living capital and hundreds of incommensurable instruments and scoring systems, when what we need is the latter. (See previous entries in this blog for more on the difference between dead and living capital.)

Given the model of a road map detailing the significant features of the living capital terrain, industry-specific variations will inform the development of explicit market expectations, the alignment of HSN capital budgeting decisions, and the coordination of research investments. The concept of a technology road map for HSN capital is based in and expands on an integration of hierarchical complexity (Commons & Richards, 2002; Dawson, 2004), complex adaptive functionality (Taylor, 2003), Peirce’s semiotic developmental map of creative thought (Wright, 1999), and historical stages in the development of measuring systems (Stenner & Horabin, 1992; Stenner, Burdick, Sanford, & Burdick, 2006).

Technology road maps replace organizational amnesia with organizational learning by providing the structure of a memory that not only stores information, knowledge, understanding, and wisdom, but makes it available for use in new situations. Othman and Hashim (2004) describe organizational amnesia (OA) relative to organizational learning (OL) in a way that opens the door to a rich application of Miller and O’Leary’s (2007) detailed account of how technology road maps contribute to the creation of new markets and industries. Technology road maps function as the higher organizational principles needed for transforming individual and social expertise into economically useful products and services. Organizational learning and adaptability further need to be framed at the inter-organizational level where their various dimensions or facets are aligned not only within individual organizations but between them within the industry as a whole.

The mediation of the individual and organizational levels, and of the organizational and inter-organizational levels, is facilitated by measurement. In the microprocessor industry, Moore’s Law enabled the creation of technology road maps charting the structure, processes, and outcomes that had to be aligned at the individual, organizational, and inter-organizational levels to coordinate the entire microprocessor industry’s economic success. Such road maps need to be created for each major form of human, social, and natural capital, with the associated alignments and coordinations put in play at all levels of every firm, industry, and government.

It is a basic fact of contemporary life that the technologies we employ every day are so complex that hardly anyone understands how they do what they do. Technological miracles are commonplace events, from transportation to entertainment, from health care to manufacturing. And we usually suffer little in the way of adverse consequences from not knowing how an automatic transmission, a thermometer, or digital video reproduction works. It is enough to know how to use the tool.

This passive acceptance of technical details beyond our ken extends into areas in which standards, methods, and products are much less well defined. Managers, executives, researchers, teachers, clinicians, and others who need measurement but who are unaware of its technicalities are then put in the position of being passive consumers accepting the lowest common denominator in the quality of the services and products obtained.

And that’s not all. Just as the mass market of measurement consumers is typically passive and uninformed, in complementary fashion the supply side is fragmented and contentious. There is little agreement among measurement experts as to which quantitative methods set the standard as the state of the art. Virtually any method can be justified in terms of some body of research and practice, so the confused consumer accepts whatever is easily available or is most likely to support a preconceived agenda.

It may be possible, however, to separate the measurement wheat from the chaff. For instance, measurement consumers may value a way of distinguishing among methods that is based in a simple criterion of meaningful utility. What if all measurement consumers’ own interests in, and reasons for, measuring something in particular, such as literacy or community, were emphasized and embodied in a common framework? What if a path of small steps from currently popular methods of less value to more scientific ones of more value could be mapped? Such a continuum of methods could range from those doing the least to advance the users’ business interests to those doing the most to advance those interests.

The aesthetics, simplicity, meaningfulness, rigor, and practical consequences of strong theoretical requirements for instrument calibration provide such criteria for choices as to models and methods (Andrich, 2002, 2004; Busemeyer and Wang, 2000; Myung, 2000; Pitt, Kim, Myung, 2003; Wright, 1997, 1999). These criteria could be used to develop and guide explicit considerations of data quality, construct theory, instrument calibration, quantitative comparisons, measurement standard metrics, etc. along a continuum from the most passive and least objective to the most actively involved and most objective.

The passive approach to measurement typically starts from and prioritizes content validity. The questions asked on tests, surveys, and assessments are considered relevant primarily on the basis of the words they use and the concepts they appear to address. Evidence that the questions actually cohere together and measure the same thing is not needed. If there is any awareness of the existence of axiomatically prescribed measurement requirements, these are not considered to be essential. That is, if failures of invariance are observed, they usually provoke a turn to less stringent data treatments instead of a push to remove or prevent them. Little or no measurement or construct theory is implemented, meaning that all results remain dependent on local samples of items and people. Passively approaching measurement in this way is then encumbered by the need for repeated data gathering and analysis, and by the local dependency of the results. Researchers working in this mode are akin to the woodcutters who say they are too busy cutting trees to sharpen their saws.

An alternative, active approach to measurement starts from and prioritizes construct validity and the satisfaction of the axiomatic measurement requirements. Failures of invariance provoke further questioning, and there is significant practical use of measurement and construct theory. Results are then independent of local samples, sometimes to the point that researchers and practical applications are not encumbered with usual test- or survey-based data gathering and analysis.

As is often the case, this black and white portrayal tells far from the whole story. There are multiple shades of grey in the contrast between passive and active approaches to measurement. The actual range of implementations is much more diverse that the simple binary contrast would suggest (see the previous post in this blog for a description of a hierarchy of increasingly complex stages in measurement). Spelling out the variation that exists could be helpful for making deliberate, conscious choices and decisions in measurement practice.

It is inevitable that we would start from the materials we have at hand, and that we would then move through a hierarchy of increasing efficiency and predictive control as understanding of any given variable grows. Previous considerations of the problem have offered different categorizations for the transformations characterizing development on this continuum. Stenner and Horabin (1992) distinguish between 1) impressionistic and qualitative, nominal gradations found in the earliest conceptualizations of temperature, 2) local, data-based quantitative measures of temperature, and 3) generalized, universally uniform, theory-based quantitative measures of temperature.

The latter is prized for the way that thermodynamic theory enables the calibration of individual thermometers with no need for testing each one in empirical studies of its performance. Theory makes it possible to know in advance what the results of such tests would be with enough precision to greatly reduce the burden and expenses of instrument calibration.

Reflecting on the history of psychosocial measurement in this context, it then becomes apparent that these three stages can then be further broken down. The previous post in this blog lists the distinguishing features for each of six stages in the evolution of measurement systems, building on the five stages described by Stenner, Burdick, Sanford, and Burdick (2006).

And so what analogue of Moore’s Law might be projected? What kind of timetable can be projected for the unfolding of what might be called Stenner’s Law? Guidance for reasonable expectations is found in Kurzweil’s (2005) charting of historical and projected future exponential increases in the volume of information and computer processing speed. The accelerating growth in knowledge taking place in the world today speaks directly to a systematic integration of criteria for what shall count as meaningful new learning. Maps of the roads we’re traveling will provide some needed guidance and make the trip more enjoyable, efficient, and productive. Perhaps somewhere not far down the road we’ll be able to project doubling rates for growth in the volume of fungible literacy capital globally, or the halving rates in the cost of health capital stocks. We manage what we measure, so when we begin measuring well what we want to manage well, we’ll all be better off.

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.

Busemeyer, J. R., & Wang, Y.-M. (2000, March). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44(1), 171-189 [http://quantrm2.psy.ohio-state.edu/injae/jmpsp.htm].

Commons, M. L., & Richards, F. A. (2002, Jul). Organizing components into combinations: How stage transition works. Journal of Adult Development, 9(3), 159-177.

Dawson, T. L. (2004, April). Assessing intellectual development: Three approaches, one sequence. Journal of Adult Development, 11(2), 71-85.

Kurzweil, R. (2005). The singularity is near: When humans transcend biology. New York: Viking Penguin.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-34.

Myung, I. J. (2000). Importance of complexity in model selection. Journal of Mathematical Psychology, 44(1), 190-204.

Othman, R., & Hashim, N. A. (2004). Typologizing organizational amnesia. The Learning Organization, 11(3), 273-84.

Pitt, M. A., Kim, W., & Myung, I. J. (2003). Flexibility versus generalizability in model selection. Psychonomic Bulletin & Review, 10, 29-44.

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., & Horabin, I. (1992). Three stages of construct definition. Rasch Measurement Transactions, 6(3), 229 [http://www.rasch.org/rmt/rmt63b.htm].

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

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.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Stages in the Development of Meaningful, Efficient, and Useful Measures

February 21, 2011

In all learning, we use what we already know as a means of identifying what we do not yet know. When someone can read a written language, knows an alphabet and has a vocabulary, understands grammar and syntax, then that knowledge can be used to learn about the world. Then, knowing what birds are, for instance, one might learn about different kinds of birds or the typical behaviors of one bird species.

And so with measurement, we start from where we find ourselves, as with anything else. There is no need or possibility for everyone to master all the technical details of every different area of life that’s important. But it is essential that we know what is technically possible, so that we can seek out and find the tools that help us achieve our goals. We can’t get what we can’t or don’t ask for. In the domain of measurement, it seems that hardly anyone is looking for what’s actually readily available.

So it seems pertinent to offer a description of a continuum of increasingly meaningful, efficient and useful ways of measuring. Previous considerations of the problem have offered different categorizations for the transformations characterizing development on this continuum. Stenner and Horabin (1992) distinguish between 1) impressionistic and qualitative, nominal gradations found in the earliest conceptualizations of temperature, 2) local, data-based quantitative measures of temperature, and 3) generalized, universally uniform, theory-based quantitative measures of temperature.

Theory-based temperature measurement is prized for the way that thermodynamic theory enables the calibration of individual thermometers with no need for testing each one in empirical studies of its performance. As Lewin (1951, p. 169) put it, “There is nothing so practical as a good theory.” Thus we have electromagnetic theory making it possible to know the conduction and resistance characteristics of electrical cable from the properties of the metal alloys and insulators used, with no need to test more than a small fraction of that cable as a quality check.

Theory makes it possible to know in advance what the results of such tests would be with enough precision to greatly reduce the burden and expenses of instrument calibration. There likely would be no electrical industry at all if the properties of every centimeter of cable and every appliance had to be experimentally tested. This principle has been employed in measuring human, social, and natural capital for some time, but, for a variety of reasons, it has not yet been adopted on a wide scale.

Reflecting on the history of psychosocial measurement in this context, it then becomes apparent that Stenner and Horabin’s (1992) three stages can then be further broken down. Listed below are the distinguishing features for each of six stages in the evolution of measurement systems, building on the five stages described by Stenner, Burdick, Sanford, and Burdick (2006). This progression of increasing complexity, meaning, efficiency, and utility can be used as a basis for a technology roadmap that will enable the coordination and alignment of various services and products in the domain of intangible assets, as I will take up in a forthcoming post.

Stage 1. Least meaning, utility, efficiency, and value

Purely passive, receptive

Statistics describe data: What you see is what you get

Content defines measure

Additivity, invariance, etc. not tested, so numbers do not stand for something that adds up like they do

Measurement defined statistically in terms of group-level intervariable relations

Meaning of numbers changes with questions asked and persons answering

No theory

Data must be gathered and analyzed to have results

Commercial applications are instrument-dependent

Standards based in ensuring fair methods and processes

Stage 2

Slightly less passive, receptive but still descriptively oriented

Additivity, invariance, etc. tested, so numbers might stand for something that adds up like they do

Measurement still defined statistically in terms of group-level intervariable relations

Falsification of additive hypothesis effectively derails measurement effort

Descriptive models with interaction effects accepted as viable alternatives

Typically little or no attention to theory of item hierarchy and construct definition

Empirical (data-based) calibrations only

Data must be gathered and analyzed to have results

Initial awareness of measurement theory

Commercial applications are instrument-dependent

Standards based in ensuring fair methods and processes

Stage 3

Even less purely passive & receptive, more active

Instrument still designed relative to content specifications

Additivity, invariance, etc. tested, so numbers might stand for something that adds up like they do

Falsification of additive hypothesis provokes questions as to why

Descriptive models with interaction effects not accepted as viable alternatives

Measurement defined prescriptively in terms of individual-level intravariable invariance

Significant attention to theory of item hierarchy and construct definition

Empirical calibrations only

Data has to be gathered and analyzed to have results

More significant use of measurement theory in prescribing acceptable data quality

Limited construct theory (no predictive power)

Commercial applications are instrument-dependent

Standards based in ensuring fair methods and processes

Stage 4

First stage that is more active than passive

Initial efforts to (re-)design instrument relative to construct specifications and theory

Additivity, invariance, etc. tested in thoroughly prescriptive focus on calibrating instrument

Numbers not accepted unless they stand for something that adds up like they do

Falsification of additive hypothesis provokes questions as to why and corrective action

Models with interaction effects not accepted as viable alternatives

Measurement defined prescriptively in terms of individual-level intravariable invariance

Significant attention to theory of item hierarchy and construct definition relative to instrument design

Empirical calibrations only but model prescribes data quality

Data usually has to be gathered and analyzed to have results

Point of use self-scoring forms might provide immediate measurement results to end user

Some construct theory (limited predictive power)

Some commercial applications are not instrument-dependent (as in CAT item bank implementations)

Standards based in ensuring fair methods and processes

Stage 5

Significantly active approach to measurement

Item hierarchy translated into construct theory

Construct specification equation predicts item difficulties

Theory-predicted (not empirical) calibrations used in applications

Item banks superseded by single-use items created on the fly

Calibrations checked against empirical results but data gathering and analysis not necessary

Point of use self-scoring forms or computer apps provide immediate measurement results to end user

Used routinely in commercial applications

Awareness that standards might be based in metrological traceability to consensus standard uniform metric

Stage 6. Most meaning, utility, efficiency, and value

Most purely active approach to measurement

Item hierarchy translated into construct theory

Construct specification equation predicts item ensemble difficulties

Theory-predicted calibrations enable single-use items created from context

Checked against empirical results for quality assessment but data gathering and analysis not necessary

Point of use self-scoring forms or computer apps provide immediate measurement results to end user

Used routinely in commercial applications

Standards based in metrological traceability to consensus standard uniform metric

 

References

Lewin, K. (1951). Field theory in social science: Selected theoretical papers (D. Cartwright, Ed.). New York: Harper & Row.

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., & Horabin, I. (1992). Three stages of construct definition. Rasch Measurement Transactions, 6(3), 229 [http://www.rasch.org/rmt/rmt63b.htm].

Creative Commons License
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.