Archive for the ‘Rasch’ Category

Excellent articulation of the rationale for living capital metrics 

November 2, 2017

I just found the best analysis of today’s situation I’ve seen yet. And it explicitly articulates and substantiates all my reasons for doing the work I’m doing. Wonderful to have this independent source of validation.

The crux of the problem is spelled out at the end of the article, where the degree of polarizing opposition is so extreme that standards of truth and evidence are completely compromised. My point is that the fact will remain, however, that everyone still uses language, and language still requires certain connections between concepts, words, and things to function. Continuing to use language in everyday functions in ways that assume a common consensus on meaningful reference may eventually come to be unbearably inconsistent with the way language is used politically, creating a social vacuum that will be filled by a new language capable of restoring the balance of meaning in the word-concept-thing triangles.

As is repeatedly argued in this blog, my take is that what we are witnessing is language restructuring itself to incorporate new degrees of complexity at a general institutional, world historic level. The falsehoods of our contemporary institutional definitions of truth and fact are rooted in the insufficiencies of the decision making methods and tools widely used in education, health care, government, business, etc. The numbers called measures are identified using methods that almost universally ignore the gifts of self-organized meaning that offer themselves in the structure of test, assessment, survey, poll, and evaluation response data. Those shortcomings in our information infrastructure and communication systems are causing negative feedback loops of increasingly chaotic noise.

This is why it is so important that precision science is rooted in everyday language and thinking, per Nersessian’s (2002) treatment of Maxwell and Rasch’s (1960, pp. 110-115) adoption of Maxwell’s method of analogy (Fisher, 2010; Fisher & Stenner, 2013). The metric system (System International des Unites, or SI) is a natural language extension of intuitive and historical methods of bringing together words, concepts, and things, renamed instruments, theories, and data. A new SI for human, social, and natural capital built out into science and commerce will be one component of a multilevel and complex adaptive system that resolves today’s epistemic crisis by tapping deeper resources for the creation of meaning than are available in today’s institutions.

Everything is interrelated. The epistemic crisis will be resolved when our institutions base decisions not just on a potentially arbitrary collection of facts but on facts internally consistent enough to support instrument calibration and predictive theory. The facts have to be common sensical to everyday people, to employees, customers, teachers, students, patients, doctors, nurses, managers. People have to be able to see themselves and where they stand relative to their goals, their origins, and everyone else in the pictures drawn by the results of tests, surveys, and evaluations. That’s not possible in today’s systems. And in those systems, some people have systematically unfair advantages. That has to change, not through some kind of Brave New World hobbling of those with advantages but by leveling the playing field to allow everyone the same opportunities for self-improvement and the rewards that follow from it.

That’s it in a nutshell. Really good article:

America is facing an epistemic crisis – Vox

https://apple.news/A0alOElOQT5itYGPAJ3eYPQ

References

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., & Stenner, A. J. (2013). On the potential for improved measurement in the human and social sciences. In Q. Zhang & H. Yang (Eds.), Pacific Rim Objective Measurement Symposium 2012 Conference Proceedings (pp. 1-11). Berlin, Germany: Springer-Verlag.

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

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

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Excerpts and Notes from Goldberg’s “Billions of Drops…”

December 23, 2015

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

p. 8:
Transaction costs: “…nonprofit financial markets are highly disorganized, with considerable duplication of effort, resource diversion, and processes that ‘take a fair amount of time to review grant applications and to make funding decisions’ [citing Harvard Business School Case No. 9-391-096, p. 7, Note on Starting a Nonprofit Venture, 11 Sept 1992]. It would be a major understatement to describe the resulting capital market as inefficient.”

A McKinsey study found that nonprofits spend 2.5 to 12 times more raising capital than for-profits do. When administrative costs are factored in, nonprofits spend 5.5 to 21.5 times more.

For-profit and nonprofit funding efforts contrasted on pages 8 and 9.

p. 10:
Balanced scorecard rating criteria

p. 11:
“Even at double-digit annual growth rates, it will take many years for social entrepreneurs and their funders to address even 10% of the populations in need.”

p. 12:
Exhibit 1.5 shows that the percentages of various needs served by leading social enterprises are barely drops in the respective buckets; they range from 0.07% to 3.30%.

pp. 14-16:
Nonprofit funding is not tied to performance. Even when a nonprofit makes the effort to show measured improvement in impact, it does little or nothing to change their funding picture. It appears that there is some kind of funding ceiling implicitly imposed by funders, since nonprofit growth and success seems to persuade capital sources that their work there is done. Mediocre and low performing nonprofits seem to be able to continue drawing funds indefinitely from sympathetic donors who don’t require evidence of effective use of their money.

p. 34:
“…meaningful reductions in poverty, illiteracy, violence, and hopelessness will require a fundamental restructuring of nonprofit capital markets. Such a restructuring would need to make it much easier for philanthropists of all stripes–large and small, public and private, institutional and individual–to fund nonprofit organizations that maximize social impact.”

p. 54:
Exhibit 2.3 is a chart showing that fewer people rose from poverty, and more remained in it or fell deeper into it, in the period of 1988-98 compared with 1969-1979.

pp. 70-71:
Kotter’s (1996) change cycle.

p. 75:
McKinsey’s seven elements of nonprofit capacity and capacity assessment grid.

pp. 94-95:
Exhibits 3.1 and 3.2 contrast the way financial markets reward for-profit performance with the way nonprofit markets reward fund raising efforts.

Financial markets
1. Market aggregates and disseminates standardized data
2. Analysts publish rigorous research reports
3. Investors proactively search for strong performers
4. Investors penalize weak performers
5. Market promotes performance
6. Strong performers grow

Nonprofit markets
1. Social performance is difficult to measure
2. NPOs don’t have resources or expertise to report results
3. Investors can’t get reliable or standardized results data
4. Strong and weak NPOs spend 40 to 60% of time fundraising
5. Market promotes fundraising
6. Investors can’t fund performance; NPOs can’t scale

p. 95:
“…nonprofits can’t possibly raise enough money to achieve transformative social impact within the constraints of the existing fundraising system. I submit that significant social progress cannot be achieved without what I’m going to call ‘third-stage funding,’ that is, funding that doesn’t suffer from disabling fragmentation. The existing nonprofit capital market is not capable of [p. 97] providing third-stage funding. Such funding can arise only when investors are sufficiently well informed to make big bets at understandable and manageable levels of risk. Existing nonprofit capital markets neither provide investors with the kinds of information needed–actionable information about nonprofit performance–nor provide the kinds of intermediation–active oversight by knowledgeable professionals–needed to mitigate risk. Absent third-stage funding, nonprofit capital will remain irreducibly fragmented, preventing the marshaling of resources that nonprofit organizations need to make meaningful and enduring progress against $100 million problems.”

pp. 99-114:
Text and diagrams on innovation, market adoption, transformative impact.

p. 140:
Exhibit 4.2: Capital distribution of nonprofits, highlighting mid-caps

pages 192-3 make the case for the difference between a regular market and the current state of philanthropic, social capital markets.

p. 192:
“So financial markets provide information investors can use to compare alternative investment opportunities based on their performance, and they provide a dynamic mechanism for moving money away from weak performers and toward strong performers. Just as water seeks its own level, markets continuously recalibrate prices until they achieve a roughly optimal equilibrium at which most companies receive the ‘right’ amount of investment. In this way, good companies thrive and bad ones improve or die.
“The social sector should work the same way. .. But philanthropic capital doesn’t flow toward effective nonprofits and away from ineffective nonprofits for a simple reason: contributors can’t tell the difference between the two. That is, philanthropists just don’t [p. 193] know what various nonprofits actually accomplish. Instead, they only know what nonprofits are trying to accomplish, and they only know that based on what the nonprofits themselves tell them.”

p. 193:
“The signs that the lack of social progress is linked to capital market dysfunctions are unmistakable: fundraising remains the number-one [p. 194] challenge of the sector despite the fact that nonprofit leaders divert some 40 to 60% of their time from productive work to chasing after money; donations raised are almost always too small, too short, and too restricted to enhance productive capacity; most mid-caps are ensnared in the ‘social entrepreneur’s trap’ of focusing on today and neglecting tomorrow; and so on. So any meaningful progress we could make in the direction of helping the nonprofit capital market allocate funds as effectively as the private capital market does could translate into tremendous advances in extending social and economic opportunity.
“Indeed, enhancing nonprofit capital allocation is likely to improve people’s lives much more than, say, further increasing the total amount of donations. Why? Because capital allocation has a multiplier effect.”

“If we want to materially improve the performance and increase the impact of the nonprofit sector, we need to understand what’s preventing [p. 195] it from doing a better job of allocating philanthropic capital. And figuring out why nonprofit capital markets don’t work very well requires us to understand why the financial markets do such a better job.”

p. 197:
“When all is said and done, securities prices are nothing more than convenient approximations that market participants accept as a way of simplifying their economic interactions, with a full understanding that market prices are useful even when they are way off the mark, as they so often are. In fact, that’s the whole point of markets: to aggregate the imperfect and incomplete knowledge held by vast numbers of traders about much various securities are worth and still make allocation choices that are better than we could without markets.
“Philanthropists face precisely the same problem: how to make better use of limited information to maximize output, in this case, social impact. Considering the dearth of useful tools available to donors today, the solution doesn’t have to be perfect or even all that good, at least at first. It just needs to improve the status quo and get better over time.
“Much of the solution, I believe, lies in finding useful adaptations of market mechanisms that will mitigate the effects of the same lack of reliable and comprehensive information about social sector performance. I would even go so far as to say that social enterprises can’t hope to realize their ‘one day, all children’ visions without a funding allociation system that acts more like a market.
“We can, and indeed do, make incremental improvements in nonprofit funding without market mechanisms. But without markets, I don’t see how we can fix the fragmentation problem or produce transformative social impact, such as ensuring that every child in America has a good education. The problems we face are too big and have too many moving parts to ignore the self-organizing dynamics of market economics. As Thomas Friedman said about the need to impose a carbon tax at a time of falling oil prices, ‘I’ve wracked my brain trying to think of ways to retool America around clean-power technologies without a price signal–i.e., a tax–and there are no effective ones.”

p. 199:
“Prices enable financial markets to work the way nonprofit capital markets should–by sending informative signals about the most effective organizations so that money will flow to them naturally..”

p. 200:
[Quotes Kurtzman citing De Soto on the mystery of capital. Also see p. 209, below.]
“‘Solve the mystery of capital and you solve many seemingly intractable problems along with it.'”
[That’s from page 69 in Kurtzman, 2002.]

p. 201:
[Goldberg says he’s quoting Daniel Yankelovich here, but the footnote does not appear to have anything to do with this quote:]
“‘The first step is to measure what can easily be measured. The second is to disregard what can’t be measured, or give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can’t be measured easily isn’t very important. This is blindness. The fourth step is to say that what can’t be easily measured really doesn’t exist. This is suicide.'”

Goldberg gives example here of $10,000 invested witha a 10% increase in value, compared with $10,000 put into a nonprofit. “But if the nonprofit makes good use of the money and, let’s say, brings the reading scores of 10 elementary school students up from below grade level to grade level, we can’t say how much my initial investment is ‘worth’ now. I could make the argument that the value has increased because the students have received a demonstrated educational benefit that is valuable to them. Since that’s the reason I made the donation, the achievement of higher scores must have value to me, as well.”

p. 202:
Goldberg wonders whether donations to nonprofits would be better conceived as purchases than investments.

p. 207:
Goldberg quotes Jon Gertner from the March 9, 2008, issue of the New York Times Magazine devoted to philanthropy:

“‘Why shouldn’t the world’s smartest capitalists be able to figure out more effective ways to give out money now? And why shouldn’t they want to make sure their philanthropy has significant social impact? If they can measure impact, couldn’t they get past the resistance that [Warren] Buffet highlighted and finally separate what works from what doesn’t?'”

p. 208:
“Once we abandon the false notions that financial markets are precision instruments for measuring unambiguous phenomena, and that the business and nonproft sectors are based in mutually exclusive principles of value, we can deconstruct the true nature of the problems we need to address and adapt market-like mechanisms that are suited to the particulars of the social sector.
“All of this is a long way (okay, a very long way) of saying that even ordinal rankings of nonprofit investments can have tremendous value in choosing among competing donation opportunities, especially when the choices are so numerous and varied. If I’m a social investor, I’d really like to know which nonprofits are likely to produce ‘more’ impact and which ones are likely to produce ‘less.'”

“It isn’t necessary to replicate the complex working of the modern stock markets to fashion an intelligent and useful nonprofit capital allocation mechanism. All we’re looking for is some kind of functional indication that would (1) isolate promising nonprofit investments from among the confusing swarm of too many seemingly worthy social-purpose organizations and (2) roughly differentiate among them based on the likelihood of ‘more’ or ‘less’ impact. This is what I meant earlier by increasing [p. 209] signals and decreasing noise.”

p. 209:
Goldberg apparently didn’t read De Soto, as he says that the mystery of capital is posed by Kurtzman and says it is solved via the collective intelligence and wisdom of crowds. This completely misses the point of the crucial value that transparent representations of structural invariance hold in market functionality. Goldberg is apparently offering a loose kind of market for which there is an aggregate index of stocks for nonprofits that are built up from their various ordinal performance measures. I think I find a better way in my work, building more closely from De Soto (Fisher, 2002, 2003, 2005, 2007, 2009a, 2009b).

p. 231:
Goldberg quotes Harvard’s Allen Grossman (1999) on the cost-benefit boundaries of more effective nonprofit capital allocation:

“‘Is there a significant downside risk in restructuring some portion of the philanthropic capital markets to test the effectiveness of performance driven philanthropy? The short answer is, ‘No.’ The current reality is that most broad-based solutions to social problems have eluded the conventional and fragmented approaches to philanthropy. It is hard to imagine that experiments to change the system to a more performance driven and rational market would negatively impact the effectiveness of the current funding flows–and could have dramatic upside potential.'”

p. 232:
Quotes Douglas Hubbard’s How to Measure Anything book that Stenner endorsed, and Linacre and I didn’t.

p. 233:
Cites Stevens on the four levels of measurement and uses it to justify his position concerning ordinal rankings, recognizing that “we can’t add or subtract ordinals.”

pp. 233-5:
Justifies ordinal measures via example of Google’s PageRank algorithm. [I could connect from here using Mary Garner’s (2009) comparison of PageRank with Rasch.]

p. 236:
Goldberg tries to justify the use of ordinal measures by citing their widespread use in social science and health care. He conveniently ignores the fact that virtually all of the same problems and criticisms that apply to philanthropic capital markets also apply in these areas. In not grasping the fundamental value of De Soto’s concept of transferable and transparent representations, and in knowing nothing of Rasch measurement, he was unable to properly evaluate to potential of ordinal data’s role in the formation of philanthropic capital markets. Ordinal measures aren’t just not good enough, they represent a dangerous diversion of resources that will be put into systems that take on lives of their own, creating a new layer of dysfunctional relationships that will be hard to overcome.

p. 261 [Goldberg shows here his complete ignorance about measurement. He is apparently totally unaware of the work that is in fact most relevant to his cause, going back to Thurstone in 1920s, Rasch in the 1950s-1970s, and Wright in the 1960s to 2000. Both of the problems he identifies have long since been solved in theory and in practice in a wide range of domains in education, psychology, health care, etc.]:
“Having first studied performance evaluation some 30 years ago, I feel confident in saying that all the foundational work has been done. There won’t be a ‘eureka!’ breakthrough where someone finally figures out the one true way to guage nonprofit effectiveness.
“Indeed, I would venture to say that we know virtually everything there is to know about measuring the performance of nonprofit organizations with only two exceptions: (1) How can we compare nonprofits with different missions or approaches, and (2) how can we make actionable performance assessments common practice for growth-ready mid-caps and readily available to all prospective donors?”

p. 263:
“Why would a social entrepreneur divert limited resources to impact assessment if there were no prospects it would increase funding? How could an investor who wanted to maximize the impact of her giving possibly put more golden eggs in fewer impact-producing baskets if she had no way to distinguish one basket from another? The result: there’s no performance data to attract growth capital, and there’s no growth capital to induce performance measurement. Until we fix that Catch-22, performance evaluation will not become an integral part of social enterprise.”

pp. 264-5:
Long quotation from Ken Berger at Charity Navigator on their ongoing efforts at developing an outcome measurement system. [wpf, 8 Nov 2009: I read the passage quoted by Goldberg in Berger’s blog when it came out and have been watching and waiting ever since for the new system. wpf, 8 Feb 2012: The new system has been online for some time but still does not include anything on impacts or outcomes. It has expanded from a sole focus on financials to also include accountability and transparency. But it does not yet address Goldberg’s concerns as there still is no way to tell what works from what doesn’t.]

p. 265:
“The failure of the social sector to coordinate independent assets and create a whole that exceeds the sum of its parts results from an absence of.. platform leadership’: ‘the ability of a company to drive innovation around a particular platform technology at the broad industry level.’ The object is to multiply value by working together: ‘the more people who use the platform products, the more incentives there are for complement producers to introduce more complementary products, causing a virtuous cycle.'” [Quotes here from Cusumano & Gawer (2002). The concept of platform leadership speaks directly to the system of issues raised by Miller & O’Leary (2007) that must be addressed to form effective HSN capital markets.]

p. 266:
“…the nonprofit sector has a great deal of both money and innovation, but too little available information about too many organizations. The result is capital fragmentation that squelches growth. None of the stakeholders has enough horsepower on its own to impose order on this chaos, but some kind of realignment could release all of that pent-up potential energy. While command-and-control authority is neither feasible nor desirable, the conditions are ripe for platform leadership.”

“It is doubtful that the IMPEX could amass all of the resources internally needed to build and grow a virtual nonprofit stock market that could connect large numbers of growth-capital investors with large numbers of [p. 267] growth-ready mid-caps. But it might be able to convene a powerful coalition of complementary actors that could achieve a critical mass of support for performance-based philanthropy. The challenge would be to develop an organization focused on filling the gaps rather than encroaching on the turf of established firms whose participation and innovation would be required to build a platform for nurturing growth of social enterprise..”

p. 268-9:
Intermediated nonprofit capital market shifts fundraising burden from grantees to intermediaries.

p. 271:
“The surging growth of national donor-advised funds, which simplify and reduce the transaction costs of methodical giving, exemplifies the kind of financial innovation that is poised to leverage market-based investment guidance.” [President of Schwab Charitable quoted as wanting to make charitable giving information- and results-driven.]

p. 272:
Rating agencies and organizations: Charity Navigator, Guidestar, Wise Giving Alliance.
Online donor rankings: GlobalGiving, GreatNonprofits, SocialMarkets
Evaluation consultants: Mathematica

Google’s mission statement: “to organize the world’s information and make it universally accessible and useful.”

p. 273:
Exhibit 9.4 Impact Index Whole Product
Image of stakeholders circling IMPEX:
Trading engine
Listed nonprofits
Data producers and aggregators
Trading community
Researchers and analysts
Investors and advisors
Government and business supporters

p. 275:
“That’s the starting point for replication [of social innovations that work]: finding and funding; matching money with performance.”

[WPF bottom line: Because Goldberg misses De Soto’s point about transparent representations resolving the mystery of capital, he is unable to see his way toward making the nonprofit capital markets function more like financial capital markets, with the difference being the focus on the growth of human, social, and natural capital. Though Goldberg intuits good points about the wisdom of crowds, he doesn’t know enough about the flaws of ordinal measurement relative to interval measurement, or about the relatively easy access to interval measures that can be had, to do the job.]

References

Cusumano, M. A., & Gawer, A. (2002, Spring). The elements of platform leadership. MIT Sloan Management Review, 43(3), 58.

De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. New York: Basic Books.

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. (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). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In M. Wilson, K. Draney, N. Brown & B. Duckor (Eds.), Advances in Rasch Measurement, Vol. Two (p. in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf]). Maple Grove, MN: JAM Press.

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

Garner, M. (2009, Autumn). Google’s PageRank algorithm and the Rasch measurement model. Rasch Measurement Transactions, 23(2), 1201-2 [http://www.rasch.org/rmt/rmt232.pdf].

Grossman, A. (1999). Philanthropic social capital markets: Performance driven philanthropy (Social Enterprise Series 12 No. 00-002). Harvard Business School Working Paper.

Kotter, J. (1996). Leading change. Cambridge, Massachusetts: Harvard Business School Press.

Kurtzman, J. (2002). How the markets really work. New York: Crown Business.

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.

Professional capital as product of human, social, and decisional capitals

April 18, 2014

Leslie Pendrill gave me a tip on a very interesting book, Professional Capital, by Michael Fullan. The author’s distinction between business capital and professional capital is somewhat akin to my distinction (Fisher, 2011) between dead and living capital. The primary point of contact between Fullan’s sense of capital and mine stems from his inclusion of social and decisional capital as crucial enhancements of human capital.

Of course, defining human capital as talent, as Fullan does, is not going to go very far toward supporting generalized management of it. Efficient markets require that capital be represented in transparent and universally available instruments (common currencies or metrics). Transparent, systematic representation makes it possible to act on capital abstractly, in laboratories, courts, and banks, without having to do anything at all with the physical resource itself. (Contrast this with socialism’s focus on controlling the actual concrete resources, and the resulting empty store shelves, unfulfilled five-year plans, pogroms and purges, and overall failure.) Universally accessible transparent representations make capital additive (amounts can be accrued), divisible (it can be divided into shares), and mobile (it can be moved around in networks accepting the currency/metric). (See references below for more information.)

Fullan cites research by Carrie Leanna at the U of Pittsburgh showing that teachers with high social capital increased their students math scores by 5.7% more than teachers with low social capital. The teachers with the highest skill levels (most human capital) and high social capital did the overall best. Low-ability teachers in schools with high social capital did as well as average teachers.

This is great, but the real cream of Fullan’s argument concerns the importance of what he calls decisional capital. I don’t think this will likely work out to be entirely separate from human capital, but his point is well taken: the capacity to consistently engage with students with competence, good judgment, insight, inspiration, creative improvisation, and openness to feedback in a context of shared responsibility is vital. All of this is quite consistent with recent work on collective intelligence (Fischer, Giaccardi, Eden, et al., 2005; Hutchins, 2010; Magnus, 2007; Nersessian, 2006; Woolley, Chabris, Pentland, et al., 2010; Woolley and Fuchs, 2011).

And, of course, you can see this coming: decisional capital is precisely what better measurement provides. Integrated formative and summative assessment informs decision making at the individual level in ways that are otherwise impossible. When those assessments are expressed in uniformly interpretable and applicable units of measurement, collective intelligence and social capital are boosted in the ways documented by Leanna as enhancing teacher performance and boosting student outcomes.

Anyway, just wanted to share that. It fits right in with the trading zone concept I presented at IOMW (the slides are available on my LinkedIn page).

Fischer, G., Giaccardi, E., Eden, H., Sugimoto, M., & Ye, Y. (2005). Beyond binary choices: Integrating individual and social creativity. International Journal of Human-Computer Studies, 63, 482-512.

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), 936-938 [http://www.rasch.org/rmt/rmt173.pdf].

Fisher, W. P., Jr. (2004a, 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. Perth, Western Australia:  Murdoch University.

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

Fisher, W. P., Jr. (2005a). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-179.

Fisher, W. P., Jr. (2005b, 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. Pope Auditorium, Lowenstein Bldg, Fordham University.

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-1093 [http://www.rasch.org/rmt/rmt211.pdf].

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

Fisher, W. P., Jr. (2008b, March 28). Rasch, Frisch, two Fishers and the prehistory of the Separability Theorem. In J. William P. Fisher (Ed.), Session 67.056. Reading Rasch Closely: The History and Future of Measurement. American Educational Research Association. New York City [Paper available at SSRN: http://ssrn.com/abstract=1698919%5D: Rasch Measurement SIG.

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 (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology (11 pages).

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

Fisher, W. P. J. (2010b). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital (p. http://ssrn.com/abstract=2340674). Bridge to Business Postdoctoral Certification, Freeman School of Business: Tulane University.

Fisher, W. P., Jr. (2010c). 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. (2011a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011b). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com [Online]. Available: http://ssrn.com/abstract=1739386 (Accessed 18 January 2011).

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. (2005, Tuesday, April 12). Creating a common market for the liberation of literacy capital. In R. E. Schumacker (Ed.), Rasch Measurement: Philosophical, Biological and Attitudinal Impacts. American Educational Research Association. Montreal, Canada: Rasch Measurement SIG.

Fisher, W. P., Jr., & Stenner, A. J. (2011a, January). Metrology for the social, behavioral, and economic sciences. Available: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36 (Accessed 12 January 2014).

Fisher, W. P., Jr., & Stenner, A. J. (2011b, 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. Jena, Germany: http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf.

Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2, 705-715.

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

Nersessian, N. J. (2006, December). Model-based reasoning in distributed cognitive systems. Philosophy of Science, pp. 699-709.

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010, 29 October). Evidence for a collective intelligence factor in the performance of human groups. Science, pp. 686-688.

Woolley, A. W., & Fuchs, E. (2011, September-October). Collective intelligence in the organization of science. Organization Science, pp. 1359-1367.

Six Classes of Results Supporting the Measurability of Human Functioning and Capability

April 12, 2014

Another example of high-level analysis that suffers from a lack of input from state of the art measurement arises in Nussbaum (1997, p. 1205), where the author remarks that it is now a matter of course, in development economics, “to recognize distinct domains of human functioning and capability that are not commensurable along a single metric, and with regard to which choice and liberty of agency play a fundamental structuring role.” Though Nussbaum (2011, pp. 58-62) has lately given a more nuanced account of the challenges of measurement relative to human capabilities, appreciation of the power and flexibility of contemporary measurement models, methods, and instruments remains lacking. For a detailed example of the complexities and challenges that must be addressed in the context of global human development, which is Nussbaum’s area of interest, see Fisher (2011).

Though there are indeed domains of human functioning and capability that are not commensurable along a single metric, they are not the ones referred to by Nussbaum or the texts she cites. On the contrary, six different approaches to establishing the measurability of human functioning and capability have been explored and proven as providing, especially in their composite aggregate, a substantial basis for theory and practice (modified from Fisher, 2009, pp. 1279-1281). These six classes of results speak to the abstract, mathematical side of the paradox noted by Ricoeur (see previous post here) concerning the need to simultaneously accept roles for abstract ideal global universals and concrete local historical contexts in strategic planning and thinking. The six classes of results are:

  1. Mathematical proofs of the necessity and sufficiency of test and survey scores for invariant measurement in the context of Rasch’s probabilistic models (Andersen, 1977, 1999; Fischer, 1981; Newby, Conner, Grant, and Bunderson, 2009; van der Linden, 1992).
  2. Reproduction of physical units of measurement (centimeters, grams, etc.) from ordinal observations (Choi, 1997; Moulton, 1993; Pelton and Bunderson, 2003; Stephanou and Fisher, 2013).
  3. The common mathematical form of the laws of nature and Rasch models (Rasch, 1960, pp. 110-115; Fisher, 2010; Fisher and Stenner, 2013).
  4. Multiple independent studies of the same constructs on different (and common) samples using different (and the same) instruments intended to measure the same thing converge on common units, defining the same objects, substantiating theory, and supporting the viability of standardized metrics (Fisher, 1997a, 1997b, 1999, etc.).
  5. Thousands of peer-reviewed publications in hundreds of scientific journals provide a wide-ranging and diverse array of supporting evidence and theory.
  6. Analogous causal attributions and theoretical explanatory power can be created in both natural and social science contexts (Stenner, Fisher, Stone, and Burdick, 2013).

What we have here, in sum, is a combination of Greek axiomatic and Babylonian empirical algorithms, in accord with Toulmin’s (1961, pp. 28-33) sense of the contrasting principled bases for scientific advancement. Feynman (1965, p. 46) called for less of a focus on the Greek chain of reasoning approach, as it is only as strong as its weakest link, whereas the Babylonian algorithms are akin to a platform with enough supporting legs that one or more might fail without compromising its overall stability. The variations in theory and evidence under these six headings provide ample support for the conceptual and practical viability of metrological systems of measurement in education, health care, human resource management, sociology, natural resource management, social services, and many other fields. The philosophical critique of any type of economics will inevitably be wide of the mark if uninformed about these accomplishments in the theory and practice of measurement.

References

Andersen, E. B. (1977). Sufficient statistics and latent trait models. Psychometrika, 42(1), 69-81.

Andersen, E. B. (1999). Sufficient statistics in educational measurement. In G. N. Masters & J. P. Keeves (Eds.), Advances in measurement in educational research and assessment (pp. 122-125). New York: Pergamon.

Choi, S. E. (1997). Rasch invents “ounces.” Rasch Measurement Transactions, 11(2), 557 [http://www.rasch.org/rmt/rmt112.htm#Ounces].

Feynman, R. (1965). The character of physical law. Cambridge, Massachusetts: MIT Press.

Fischer, G. H. (1981). On the existence and uniqueness of maximum-likelihood estimates in the Rasch model. Psychometrika, 46(1), 59-77.

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. (1997). What scale-free measurement means to health outcomes research. Physical Medicine & Rehabilitation State of the Art Reviews, 11(2), 357-373.

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

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). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com. Retrieved 18 January 2011, from Social Science Research Network: http://ssrn.com/abstract=1739386.

Fisher, W. P., Jr., & Stenner, A. J. (2013). On the potential for improved measurement in the human and social sciences. In Q. Zhang & H. Yang (Eds.), Pacific Rim Objective Measurement Symposium 2012 Conference Proceedings (pp. 1-11). Berlin, Germany: Springer-Verlag.

Moulton, M. (1993). Probabilistic mapping. Rasch Measurement Transactions, 7(1), 268 [http://www.rasch.org/rmt/rmt71b.htm].

Newby, V. A., Conner, G. R., Grant, C. P., & Bunderson, C. V. (2009). The Rasch model and additive conjoint measurement. Journal of Applied Measurement, 107(4), 348-354.

Nussbaum, M. (1997). Flawed foundations: The philosophical critique of (a particular type of) economics. University of Chicago Law Review, 64, 1197-1214.

Nussbaum, M. (2011). Creating capabilities: The human development approach. Cambridge, MA: The Belknap Press.

Pelton, T., & Bunderson, V. (2003). The recovery of the density scale using a stochastic quasi-realization of additive conjoint measurement. Journal of Applied Measurement, 4(3), 269-281.

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

Rasch, G. (1977). On specific objectivity: An attempt at formalizing the request for generality and validity of scientific statements. Danish Yearbook of Philosophy, 14, 58-94.

Stenner, A. J., Fisher, W. P., Jr., Stone, M. H., & Burdick, D. S. (2013). Causal Rasch models. Frontiers in Psychology: Quantitative Psychology and Measurement, 4(536), 1-14.

Stephanou, A., & Fisher, W. P., Jr. (2013). From concrete to abstract in the measurement of length. Journal of Physics Conference Series, 459, http://iopscience.iop.org/1742-6596/459/1/012026.

Toulmin, S. E. (1961). Foresight and understanding: An enquiry into the aims of science. London, England: Hutchinson.

van der Linden, W. J. (1992). Sufficient and necessary statistics. Rasch Measurement Transactions, 6(3), 231 [http://www.rasch.org/rmt/rmt63d.htm].

 

Convergence, Divergence, and the Continuum of Field-Organizing Activities

March 29, 2014

So what are the possibilities for growing out green shoots from the seeds and roots of an ethical orientation to keeping the dialogue going? What kinds of fruits might be expected from cultivating a common ground for choosing discourse over violence? What are the consequences for practice of planting this seed in this ground?

The same participant in the conversation earlier this week at Convergence XV who spoke of the peace building processes taking place around the world also described a developmental context for these issues of mutual understanding. The work of Theo Dawson and her colleagues (Dawson, 2002a, 2002b, 2004; Dawson, Fischer, and Stein, 2006) is especially pertinent here. Their comparisons of multiple approaches to cognitive and moral development have provided clear and decisive theory, evidence, and instrumentation concerning the conceptual integrations that take place in the evolution of hierarchical complexity.

Conceptual integrations occur when previously tacit, unexamined, and assumed principles informing a sphere of operations are brought into conscious awareness and are transformed into explicit objects of new operations. Developmentally, this is the process of discovery that takes place from the earliest stages of life, in utero. Organisms of all kinds mature in a process of interaction with their environments. Young children at the “terrible two” stage, for instance, are realizing that anything they can detach from, whether by throwing or by denying (“No!”), is not part of them. Only a few months earlier, the same children will have been fascinated with their fingers and toes, realizing these are parts of their own bodies, often by putting them in their mouths.

There are as many opportunities for conceptual integrations between the ages of 21 to 99 as there are between birth and 21. Developmental differences in perspectives can make for riotously comic situations, and can also lead to conflicts, even when the participants agree on more than they disagree on. And so here we arrive at a position from which we can get a grip on how to integrate convergence and divergence in a common framework that follows from the prior post’s brief description of the ontological method’s three moments of reduction, application, and deconstruction.

Image

Woolley and colleagues (Woolley, et al., 2010; Woolley and Fuchs, 2011) describe a continuum of five field-organizing activities categorizing the types of information needed for effective collective intelligence (Figure 1). Four of these five activities (defining, bounding, opening, and bridging) vary in the convergent versus divergent processes they bring to bear in collective thinking. Defining and bounding are convergent processes that inform judgment and decision making. These activities are especially important in the emergence of a new field or organization, when the object of interest and the methods of recognizing and producing it are in contention. Opening and bridging activities, in contrast, diverge from accepted definitions and transgress boundaries in the creative process of pushing into new areas. Undergirding the continuum as a whole is the fifth activity, grounding, which serves as a theory- and evidence-informed connection to meaningful and useful results.

There are instances in which defining and bounding activities have progressed to the point that the explanatory power of theory enables the calibration of test items from knowledge of the component parts included in those items. The efficiencies and cost reductions gained from computer-based item generation and administration are significant. Research in this area takes a variety of approaches; for more information, see Daniel and Embretson (2010), DeBoeck and Wilson (2004), Stenner, et al. (2013), and others.

The value of clear definitions and boundaries in this context stems in large part from the capacity to identify exceptions that prove (test) the rules, and that then also provide opportunities for opening and bridging. Kuhn (1961, p. 180; 1977, p. 205) noted that

To the extent that measurement and quantitative technique play an especially significant role in scientific discovery, they do so precisely because, by displaying significant anomaly, they tell scientists when and where to look for a new qualitative phenomenon.

Rasch (1960, p. 124) similarly understood that “Once a law has been established within a certain field then the law itself may serve as a tool for deciding whether or not added stimuli and/or objects belong to the original group.” Rasch gives the example of mechanical force applied to various masses with resulting accelerations, introducing idea that one of the instruments might exert magnetic as well as mechanical force, with noticeable effects on steel masses, but not on wooden masses. Rasch suggests that exploration of these anomalies may result in the discovery of other similar instruments that vary in the extent to which they also exert the new force, with the possible consequence of discovering a law of magnetic attraction.

There has been an intense interest in the assessment of divergent inconsistencies in measurement research and practice following in the wake of Rasch’s early work in psychological and social measurement (examples from a very large literature in this area include Karabatsos and Ulrich, 2002, and Smith and Plackner, 2009). Andrich, for instance, makes explicit reference to Kuhn (1961), saying, “…the function of a model for measurement…is to disclose anomalies, not merely to describe data” (Andrich, 2002, p. 352; also see Andrich, 1996, 2004, 2011). Typical software for applying Rasch models (Andrich, et al., 2013; Linacre, 2011, 2013; Wu, et al., 2007) thus accordingly provides many more qualitative numbers evaluating potential anomalies than quantitative measuring numbers. These qualitative numbers (digits that do not stand for something substantive that adds up in a constant unit) include uncertainty and confidence indicators that vary with sample size; mean square and standardized model fit statistics; and principal components analysis factor loadings and eigenvalues.

The opportunities for divergent openings onto new qualitative phenomena provided by data consistency evaluations are complemented in Rasch measurement by a variety of bridging activities. Different instruments intended to measure the same or closely related constructs may often be equated or co-calibrated, so they measure in a common unit (among many publications in this area, see Dawson, 2002a, 2004; Fisher, 1997; Fisher, et al., 1995; Massof and Ahmadian, 2007; Smith and Taylor, 2004). Similarly, the same instrument calibrated on different samples from the same population may exhibit consistent properties across those samples, offering further evidence of a potential for defining a common unit (Fisher, 1999).

Other opening and bridging activities include capacities (a) to drop items or questions from a test or survey, or to add them; (b) to adaptively administer subsets of custom-selected items from a large bank; and (c) to adjust measures for the leniency or severity of judges assigning ratings, all of which can be done, within the limits of the relevant definitions and boundaries, without compromising the unit of comparison. For methodological overviews, see Bond and Fox (2007), Wilson (2005), and others.

The various field-organizing activities spanning the range from convergence to divergence are implicated not only in research on collective thinking, but also in the history and philosophy of science. Galison and colleagues (Galison, 1997, 1999; Galison and Stump, 1996) closely examine positivist and antipositivist perspectives on the unity of science, finding their conclusions inconsistent with the evidence of history. A postpositivist perspective (Galison, 1999, p. 138), in contrast, finds “distinct communities and incommensurable beliefs” between and often within the areas of theory, experiment, and instrument-making. But instead of finding these communities “utterly condemned to passing one another without any possibility of significant interaction,” Galison (1999, p. 138) observes that “two groups can agree on rules of exchange even if they ascribe utterly different significance to the objects being exchanged; they may even disagree on the meaning of the exchange process itself.” In practice, “trading partners can hammer out a local coordination despite vast global differences.”

In accord with Woolley and colleagues’ work on convergent and divergent field-organizing activities, Galison (1999, p. 137) concludes, then, that “science is disunified, and—against our first intuitions—it is precisely the disunification of science that underpins its strength and stability.” Galison (1997, pp. 843-844) concludes with a section entitled “Cables, Bricks, and Metaphysics” in which the postpositivist disunity of science is seen to provide its unexpected coherence from the simultaneously convergent and divergent ways theories, experiments, and instruments interact.

But as Galison recognizes, a metaphor based on the intertwined strands in a cable is too mechanical to support the dynamic processes by which order arises from particular kinds of noise and chaos. Not cited by Galison is a burgeoning literature on the phenomenon of noise-induced order termed stochastic resonance (Andò  and Graziani 2000, Benzi, et al., 1981; Dykman and McClintock, 1998; Fisher, 1992, 2011; Hess and Albano, 1998; Repperger and Farris, 2010). Where the metaphor of a cable’s strands breaks down, stochastic resonance provides multiple ways of illustrating how the disorder of finite and partially independent processes can give rise to an otherwise inaccessible order and structure.

Stochastic resonance involves small noisy signals that can be amplified to have very large effects. The noise has to be of a particular kind, and too much of it will drown out rather than amplify the effect. Examples include the interaction of neuronal ensembles in the brain (Chialvo, Lontin, and Müller-Gerking, 1996), speech recognition (Moskowitz and Dickinson, 2002), and perceptual interpretation (Rianni and Simonotto, 1994). Given that Rasch’s models for measurement are stochastic versions of Guttman’s deterministic models (Andrich, 1985), the question has been raised as to how Rasch’s seemingly weaker assumptions could lead to a measurement model that is stronger than Guttman’s (Duncan, 1984, p. 220). Stochastic resonance may provide an essential clue to this puzzle (Fisher, 1992, 2011).

Another description of what might be a manifestation of stochastic resonance akin to that brought up by Galison arises in Berg and Timmermans’ (2000, p. 56) study of the constitution of universalities in a medical network. They note that, “Paradoxically, then, the increased stability and reach of this network was not due to more (precise) instructions: the protocol’s logistics could thrive only by parasitically drawing upon its own disorder.” Much the same has been said about the behaviors of markets (Mandelbrot, 2004), bringing us back to the topic of the day at Convergence XV earlier this week. I’ll have more to say on this issue of universalities constituted via noise-induced order in due course.

References

Andò, B., & Graziani, S. (2000). Stochastic resonance theory and applications. New York: Kluwer Academic Publishers.

Andrich, D. (1985). An elaboration of Guttman scaling with Rasch models for measurement. In N. B. Tuma (Ed.), Sociological methodology 1985 (pp. 33-80). San Francisco, California: Jossey-Bass.

Andrich, D. (1996). Measurement criteria for choosing among models with graded responses. In A. von Eye & C. Clogg (Eds.), Categorical variables in developmental research: Methods of analysis (pp. 3-35). New York: Academic Press, Inc.

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), 325-359.

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

Andrich, D. (2011). Rating scales and Rasch measurement. Expert Reviews in Pharmacoeconomics Outcome Research, 11(5), 571-585.

Andrich, D., Lyne, A., Sheridan, B., & Luo, G. (2013). RUMM 2030: Rasch unidimensional models for measurement. Perth, Australia: RUMM Laboratory Pty Ltd [www.rummlab.com.au].

Benzi, R., Sutera, A., & Vulpiani, A. (1981). The mechanism of stochastic resonance. Journal of Physics. A. Mathematical and General, 14, L453-L457.

Berg, M., & Timmermans, S. (2000). Order and their others: On the constitution of universalities in medical work. Configurations, 8(1), 31-61.

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

Chialvo, D., Longtin, A., & Müller-Gerking, J. (1996). Stochastic resonance in models of neuronal ensembles revisited [Electronic version].

Daniel, R. C., & Embretson, S. E. (2010). Designing cognitive complexity in mathematical problem-solving items. Applied Psychological Measurement, 34(5), 348-364.

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

Dawson, T. L. (2002b, March). New tools, new insights: Kohlberg’s moral reasoning stages revisited. International Journal of Behavioral Development, 26(2), 154-66.

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

Dawson, T. L., Fischer, K. W., & Stein, Z. (2006). Reconsidering qualitative and quantitative research approaches: A cognitive developmental perspective. New Ideas in Psychology, 24, 229-239.

De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. Statistics for Social and Behavioral Sciences). New York: Springer-Verlag.

Duncan, O. D. (1984). Notes on social measurement: Historical and critical. New York: Russell Sage Foundation.

Dykman, M. I., & McClintock, P. V. E. (1998, January 22). What can stochastic resonance do? Nature, 391(6665), 344.

Fisher, W. P., Jr. (1992, Spring). Stochastic resonance and Rasch measurement. Rasch Measurement Transactions, 5(4), 186-187 [http://www.rasch.org/rmt/rmt54k.htm].

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

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.

Galison, P. (1997). Image and logic: A material culture of microphysics. Chicago: University of Chicago Press.

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

Galison, P., & Stump, D. J. (1996). The disunity of science: Boundaries, contexts, and power. Palo Alto, California: Stanford University Press.

Hess, S. M., & Albano, A. M. (1998, February). Minimum requirements for stochastic resonance in threshold systems. International Journal of Bifurcation and Chaos, 8(2), 395-400.

Karabatsos, G., & Ullrich, J. R. (2002). Enumerating and testing conjoint measurement models. Mathematical Social Sciences, 43, 487-505.

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

Linacre, J. M. (2011). A user’s guide to WINSTEPS Rasch-Model computer program, v. 3.72.0. Chicago, Illinois: Winsteps.com.

Linacre, J. M. (2013). A user’s guide to FACETS Rasch-Model computer program, v. 3.71.0. Chicago, Illinois: Winsteps.com.

Mandelbrot, B. (2004). The misbehavior of markets. New York: Basic Books.

Massof, R. W., & Ahmadian, L. (2007, July). What do different visual function questionnaires measure? Ophthalmic Epidemiology, 14(4), 198-204.

Moskowitz, M. T., & Dickinson, B. W. (2002). Stochastic resonance in speech recognition: Differentiating between /b/ and /v/. Proceedings of the IEEE International Symposium on Circuits and Systems, 3, 855-858.

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

Repperger, D. W., & Farris, K. A. (2010, July). Stochastic resonance –a nonlinear control theory interpretation. International Journal of Systems Science, 41(7), 897-907.

Riani, M., & Simonotto, E. (1994). Stochastic resonance in the perceptual interpretation of ambiguous figures: A neural network model. Physical Review Letters, 72(19), 3120-3123.

Smith, R. M., & Plackner, C. (2009). The family approach to assessing fit in Rasch measurement. Journal of Applied Measurement, 10(4), 424-437.

Smith, R. M., & Taylor, P. (2004). Equating rehabilitation outcome scales: Developing common metrics. Journal of Applied Measurement, 5(3), 229-42.

Stenner, A. J., Fisher, W. P., Jr., Stone, M. H., & Burdick, D. S. (2013, August). Causal Rasch models. Frontiers in Psychology: Quantitative Psychology and Measurement, 4(536), 1-14 [doi: 10.3389/fpsyg.2013.00536].

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

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010, 29 October). Evidence for a collective intelligence factor in the performance of human groups. Science, 330, 686-688.

Woolley, A. W., & Fuchs, E. (2011, September-October). Collective intelligence in the organization of science. Organization Science, 22(5), 1359-1367.

Wu, M. L., Adams, R. J., Wilson, M. R., Haldane, S.A. (2007). ACER ConQuest Version 2: Generalised item response modelling software. Camberwell: Australian Council for Educational Research.

Dispelling Myths about Measurement in Psychology and the Social Sciences

August 27, 2013

Seven common assumptions about measurement and method in psychology and the social sciences stand as inconsistent anomalies in the experience of those who have taken the trouble to challenge them. As evidence, theory, and instrumentation accumulate, will we see a revolutionary break and disruptive change across multiple social and economic levels and areas as a result? Will there be a slower, more gradual transition to a new paradigm? Or will the status quo simply roll on, oblivious to the potential for new questions and new directions? We shall see.

1. Myth: Qualitative data and methods cannot really be integrated with quantitative data and methods because of opposing philosophical assumptions.

Fact: Qualitative methods incorporate a critique of quantitative methods that leads to a more scientific theory and practice of measurement.

2. Myth: Statistics is the logic of measurement.

Fact: Statistics did not emerge as a discipline until the 19th century, while measurement, of course, has been around for millennia. Measurement is modeled at the individual level within a single variable whereas statistics model at the population level between variables. Data are fit to prescriptive measurement models using the Garbage-In, Garbage-Out (GIGO) Principle, while descriptive statistical models are fit to data.

3. Myth: Linear measurement from ordinal test and survey data is impossible.

Fact: Ordinal data have been used as a basis for invariant linear measures for decades.

4. Myth: Scientific laws like Newton’s laws of motion cannot be successfully formulated, tested, or validated in psychology and the social sciences.

Fact: Mathematical laws of human behavior and cognition in the same form as Newton’s laws are formulated, tested, and validated in numerous Rasch model applications.

5. Myth: Experimental manipulations of psychological and social phenomena are inherently impossible or unethical.

Fact: Decades of research across multiple fields have successfully shown how theory-informed interventions on items/indicators/questions can result in predictable, consistent, and substantively meaningful quantitative changes.

6. Myth: “Real” measurement is impossible in psychology and the social sciences.

Fact: Success in predictive theory, instrument calibration, and in maintaining stable units of comparison over time are all evidence supporting the viability of meaningful uniform units of measurement in psychology and the social sciences.

7. Myth: Efficient economic markets can incorporate only manufactured and liquid capital, and property. Human, social, and natural capital, being intangible, have permanent status as market externalities as they cannot be measured well enough to enable accountability, pricing, or transferable representations (common currency instruments).

Fact: The theory and methods necessary for establishing an Intangible Assets Metric System are in hand. What’s missing is the awareness of the scientific, human, social, and economic value that would be returned from the admittedly very large investments that would be required.

References and examples are available in other posts in this blog, in my publications, or on request.

Comments on the New ANSI Human Capital Investor Metrics Standard

April 16, 2012

The full text of the proposed standard is available here.

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
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2011 IMEKO Conference Papers Published Online

January 13, 2012

Papers from the Joint International IMEKO TC1+ TC7+ TC13 Symposium held August 31st to September 2nd,  2011, in Jena, Germany are now available online at http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24575/IMEKO2011_TOC.pdf. The following will be of particular interest to those interested in measurement applications in the social sciences, education, health care, and psychology:

Nikolaus Bezruczko
Foundational Imperatives for Measurement with Mathematical Models
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24419/ilm1-2011imeko-030.pdf

Nikolaus Bezruczko, Shu-Pi C. Chen, Connie Hill, Joyce M. Chesniak
A Clinical Scale for Measuring Functional Caregiving of Children Assisted with Medical Technologies
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24507/ilm1-2011imeko-032.pdf

Stefan Cano, Anne F. Klassen, Andrea L. Pusic, Andrea
From Breast-Q © to Q-Score ©: Using Rasch Measurement to Better Capture Breast Surgery Outcomes
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24429/ilm1-2011imeko-039.pdf

Gordon A. Cooper, William P. Fisher, Jr.
Continuous Quantity and Unit; Their Centrality to Measurement
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24494/ilm1-2011imeko-019.pdf

William P. Fisher, Jr.
Measurement, Metrology and the Coordination of Sociotechnical Networks
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24491/ilm1-2011imeko-017.pdf

William .P Fisher, Jr., A. Jackson Stenner
A Technology Roadmap for Intangible Assets Metrology
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf

Carl V. Granger, Nikolaus Bezruczko
Body, Mind, and Spirit are Instrumental to Functional Health: A Case Study
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24494/ilm1-2011imeko-019.pdf

Thomas Salzberger
The Quantification of Latent Variables in the Social Sciences: Requirements for Scientific Measurement and Shortcomings of Current Procedures
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24417/ilm1-2011imeko-029.pdf

A. Jackson Stenner, Mark Stone, Donald Burdick
How to Model and Test for the Mechanisms that Make Measurement Systems Tick
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24416/ilm1-2011imeko-027.pdf

Mark Wilson
The Role of Mathematical Models in Measurement: A Perspective from Psychometrics
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24178/ilm1-2011imeko-005.pdf

Also of interest will be Karl Ruhm’s plenary lecture and papers from the Fundamentals of Measurement Science session and the Special Session on the Role of Mathematical Models in Measurement:

Karl H. Ruhm
From Verbal Models to Mathematical Models – A Didactical Concept not just in Metrology
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24167/ilm1-2011imeko-002.pdf

Alessandro Giordani, Luca Mari
Quantity and Quantity Value
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24414/ilm1-2011imeko-025.pdf

Eric Benoit
Uncertainty in Fuzzy Scales Based Measurements
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24415/ilm1-2011imeko-020.pdf

Susanne C.N. Töpfer
Application of Mathematical Models in Optical Coordinate Metrology
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24445/ilm1-2011imeko-008.pdf

Giovanni Battista Rossi
Measurement Modelling: Foundations and Probabilistic Approach
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24446/ilm1-2011imeko-009.pdf

Sanowar H. Khan, Ludwik Finkelstein
The Role of Mathematical Modelling in the Analysis and Design of Measurement Systems
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24448/ilm1-2011imeko-010.pdf

Roman Z. Morawski
Application-Oriented Approach to Mathematical Modelling of Measurement Processes
http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24449/ilm1-2011imeko-011.pdf

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

Rasch Measurement as a Basis for a New Standards Framework

October 26, 2011

The 2011 U.S. celebration of World Standards Day took place on October 13 at the Fairmont Hotel in Washington, D.C., with the theme of “Advancing Safety and Sustainability Standards Worldwide.” The evening began with a reception in a hall of exhibits from the celebrations sponsors, which included the National Institute for Standards and Technology (NIST), the Society for Standards Professionals (SES), the American National Standards Institute (ANSI), Microsoft, IEEE, Underwriters Laboratories, the Consumer Electronics Association, ASME, ASTM International, Qualcomm, Techstreet, and many others. Several speakers took the podium after dinner to welcome the 400 or so attendees and to present the World Standards Day Paper Competition Awards and the Ronald H. Brown Standards Leadership Award.

Dr. Patrick Gallagher, Under Secretary of Commerce for Standards and Technology, and Director of NIST, was the first speaker after dinner. He directed his remarks at the value of a decentralized, voluntary, and demand-driven system of standards in promoting innovation and economic prosperity. Gallagher emphasized that “standards provide the common language that keeps domestic and international trade flowing,” concluding that “it is difficult to overestimate their critical value to both the U.S. and global economy.”

James Shannon, President of the National Fire Protection Association (NFPA), accepted the R. H. Brown Standards Leadership Award in recognition for his work initiating or improving the National Electrical Code, the Life Safety Code, and the Fire Safe Cigarette and Residential Sprinkler Campaigns.

Ellen Emard, President of SES, introduced the paper competition award winners. As of this writing the titles and authors of the first and second place awards are not yet available on the SES web site (http://www.ses-standards.org/displaycommon.cfm?an=1&subarticlenbr=56). I took third place for my paper, “What the World Needs Now: A Bold Plan for New Standards.” Where the other winning papers took up traditional engineering issues concerning the role of standards in advancing safety and sustainability issues, my paper spoke to the potential scientific and economic benefits that could be realized by standard metrics and common product definitions for outcomes in education, health care, social services, and environmental resource management. All three of the award-winning papers will appear in a forthcoming issue of Standards Engineering, the journal of SES.

I was coincidentally seated at the dinner alongside Gordon Gillerman, winner of third place in the 2004 paper competition (http://www.ses-standards.org/associations/3698/files/WSD%202004%20-%203%20-%20Gillerman.pdf) and currently Chief of the Standards Services Division at NIST. Gillerman has a broad range of experience in coordinating standards across multiple domains, including environmental protection, homeland security, safety, and health care. Having recently been involved in a workshop focused on measuring, evaluating, and improving the usability of electronic health records (http://www.nist.gov/healthcare/usability/upload/EHR-Usability-Workshop-2011-6-03-2011_final.pdf), Gillerman was quite interested in the potential Rasch measurement techniques hold for reducing data volume with no loss of information, and so for streamlining computer interfaces.

Robert Massof of Johns Hopkins University accompanied me to the dinner, and was seated at a nearby table. Also at Massof’s table were several representatives of the National Institute of Building Sciences, some of whom Massof had recently met at a workshop on adaptations for persons with low vision disabilities. Massof’s work equating the main instruments used for assessing visual function in low vision rehabilitation could lead to a standard metric useful in improving the safety and convenience of buildings.

As is stated in educational materials distributed at the World Standards Day celebration by ANSI, standards are a constant behind-the-scenes presence in nearly all areas of everyday life. Everything from air, water, and food to buildings, clothing, automobiles, roads, and electricity are produced in conformity with voluntary consensus standards of various kinds. In the U.S. alone, more than 100,000 standards specify product and system features and interconnections, making it possible for appliances to tap the electrical grid with the same results no matter where they are plugged in, and for products of all kinds to be purchased with confidence. Life is safer and more convenient, and science and industry are more innovative and profitable, because of standards.

The point of my third-place paper is that life could be even safer and more convenient, and science and industry could be yet more innovative and profitable, if standards and conformity assessment procedures for outcomes in education, health care, social services, and environmental resource management were developed and implemented. Rasch measurement demonstrates the consistent reproducibility of meaningful measures across samples and different collections of construct-relevant items. Within any specific area of interest, then, Rasch measures have the potential of serving as the kind of mediating instruments or objects recognized as essential to the process of linking science with the economy (Fisher & Stenner, 2011b; Hussenot & Missonier, 2010; Miller & O’Leary, 2007). Recent white papers published by NIST and NSF document the challenges and benefits likely to be encountered and produced by initiatives moving in this direction (Fisher, 2009; Fisher & Stenner, 2011a).

A diverse array of Rasch measurement presentations were made at the recent International Measurement Confederation (IMEKO) meeting of metrology engineers in Jena, Germany (see RMT 25 (1), p. 1318). With that start at a new dialogue between the natural and social sciences, the NIST and NSF white papers, and with the award in the World Standards Day paper competition, the U.S. and international standards development communities have shown their interest in exploring possibilities for a new array of standard units of measurement, standardized outcome product definitions, standard conformity assessment procedures, and outcome product quality standards. The increasing acceptance and recognition of the viability of such standards is a logical consequence of observations like these:

  • “Where this law [relating reading ability and text difficulty to comprehension rate] can be applied it provides a principle of measurement on a ratio scale of both stimulus parameters and object parameters, the conceptual status of which is comparable to that of measuring mass and force. Thus…the reading accuracy of a child…can be measured with the same kind of objectivity as we may tell its weight” (Rasch, 1960, p. 115).
  • “Today there is no methodological reason why social science cannot become as stable, as reproducible, and hence as useful as physics” (Wright, 1997, p. 44).
  • “…when the key features of a statistical model relevant to the analysis of social science data are the same as those of the laws of physics, then those features are difficult to ignore” (Andrich, 1988, p. 22).

Rasch’s work has been wrongly assimilated in social science research practice as just another example of the “standard model” of statistical analysis. Rasch measurement rightly ought instead to be treated as a general articulation of the three-variable structure of natural law useful in framing the context of scientific practice. That is, Rasch’s models ought to be employed primarily in calibrating instruments quantitatively interpretable at the point of use in a mathematical language shared by a community of research and practice. To be shared in this way as a universally uniform coin of the realm, that language must be embodied in a consensus standard defining universally uniform units of comparison.

Rasch measurement offers the potential of shifting the focus of quantitative psychosocial research away from data analysis to integrated qualitative and quantitative methods enabling the definition of standard units and the calibration of instruments measuring in that unit. An intangible assets metric system will, in turn, support the emergence of new product- and performance-based standards, management system standards, and personnel certification standards. Reiterating once again Rasch’s (1960, p. xx) insight, we can acknowledge with him that “this is a huge challenge, but once the problem has been formulated it does seem possible to meet it.”

 References

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

Fisher, W. P.. Jr. (2009). Metrological infrastructure for human, social, and natural capital (NIST Critical National Need Idea White Paper Series, Retrieved 25 October 2011 from 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., & Stenner, A. J. (2011a, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Retrieved 25 October 2011 from http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36. Washington, DC: National Science Foundation.

Fisher, W. P., Jr., & Stenner, A. J. (2011b). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO), Jena, Germany, August 31 to September 2.

Hussenot, A., & Missonier, S. (2010). A deeper understanding of evolution of the role of the object in organizational process. The concept of ‘mediation object.’ Journal of Organizational Change Management, 23(3), 269-286.

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.

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

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].

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
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Reimagining Capitalism Again, Part III: Reflections on Greider’s “Bold Ideas” in The Nation

September 10, 2011

And so, The Nation’s “Bold Ideas for a New Economy” is disappointing for not doing more to start from the beginning identified by its own writer, William Greider. The soul of capitalism needs to be celebrated and nourished, if we are to make our economy “less destructive and domineering,” and “more focused on what people really need for fulfilling lives.” The only real alternative to celebrating and nourishing the soul of capitalism is to kill it, in the manner of the Soviet Union’s failed experiments in socialism and communism.

The article speaks the truth, though, when it says there is no point in trying to persuade the powers that be to make the needed changes. Republicans see the market as it exists as a one-size-fits-all economic panacea, when all it can accomplish in its current incomplete state is the continuing externalization of anything and everything important about human, social, and environmental decency. For their part, Democrats do indeed “insist that regulation will somehow fix whatever is broken,” in an ever-expanding socialistic micromanagement of every possible exception to the rules that emerges.

To date, the president’s efforts at a nonpartisan third way amount only to vacillations between these opposing poles. The leadership that is needed, however, is something else altogether. Yes, as The Nation article says, capitalism needs to be made to serve the interests of society, and this will require deep structural change, not just new policies. But none of the contributors of the “bold ideas” presented propose deep structural changes of a kind that actually gets at the soul of capitalism. All of the suggestions are ultimately just new policies tweaking superficial aspects of the economy in mechanical, static, and very limited ways.

The article calls for “Democratizing reforms that will compel business and finance to share decision-making and distribute rewards more fairly.” It says the vision has different names but “the essence is a fundamental redistribution of power and money.” But corporate distortions of liability law, the introduction of boardroom watchdogs, and a tax on financial speculation do not by any stretch of the imagination address the root causes of social and environmental irresponsibility in business. They “sound like obscure technical fixes” because that’s what they are. The same thing goes for low-cost lending from public banks, the double or triple bottom lines of Benefit Corporations, new anti-trust laws, calls for “open information” policies, added personal stakes for big-time CEOs, employee ownership plans, the elimination of tax subsidies for, new standards for sound investing, new measures of GDP, and government guarantees of full employment.

All of these proposals sound like what ought to be the effects and outcomes of efforts addressing the root causes of capitalisms’ shortcomings. Instead, they are band aids applied to scratched fingers and arms when multiple by-pass surgery is called for. That is, what we need is to understand how to bring the spirit of capitalism to life in the new domains of human, social, and environmental interests, but what we’re getting are nothing but more of the same piecemeal ways of moving around the deck chairs on the Titanic.

There is some truth in the assertion that what really needs reinventing is our moral and spiritual imagination. As someone (Einstein or Edison?) is supposed to have put it, originality is simply a matter of having a source for an analogy no one else has considered. Ironically, the best model is often the one most taken for granted and nearest to hand. Such is the case with the two-sided scientific and economic effects of standardized units of measurement. The fundamental moral aspect here is nothing other than the Golden Rule, independently derived and offered in cultures throughout history, globally. Individualized social measurement is nothing if not a matter of determining whether others are being treated in the way you yourself would want to be treated.

And so, yes, to stress the major point of agreement with The Nation, “the new politics does not start in Washington.” Historically, at their best, governments work to keep pace with the social and technical innovations introduced by their peoples. Margaret Mead said it well a long time ago when she asserted that small groups of committed citizens are the only sources of real social change.

Not to be just one of many “advocates with bold imaginations” who wind up marginalized by the constraints of status quo politics, I claim my personal role in imagining a new economic future by tapping as deeply as I can into the positive, pre-existing structures needed for a transition into a new democratic capitalism. We learn through what we already know. Standards are well established as essential to commerce and innovation, but 90% of the capital under management in our economy—the human, social, and natural capital—lacks the standards needed for optimal market efficiency and effectiveness. An intangible assets metric system will be a vitally important way in which we extend what is right and good in the world today into new domains.

To conclude, what sets this proposal apart from those offered by The Nation and its readers hinges on our common agreement that “the most threatening challenge to capitalism is arguably the finite carrying capacity of the natural world.” The bold ideas proposed by The Nation’s readers respond to this challenge in ways that share an important feature in common: people have to understand the message and act on it. That fact dooms all of these ideas from the start. If we have to articulate and communicate a message that people then have to act on, we remain a part of the problem and not part of the solution.

As I argue in my “The Problem is the Problem” blog post of some months ago, this way of defining problems is itself the problem. That is, we can no longer think of ourselves as separate from the challenges we face. If we think we are not all implicated through and through as participants in the construction and maintenance of the problem, then we have not understood it. The bold ideas offered to date are all responses to the state of a broken system that seek to reform one or another element in the system when what we need is a whole new system.

What we need is a system that so fully embodies nature’s own ecological wisdom that the medium becomes the message. When the ground rules for economic success are put in place such that it is impossible to earn a profit without increasing stocks of human, social, and natural capital, there will be no need to spell out the details of a microregulatory structure of controlling new anti-trust laws, “open information” policies, personal stakes for big-time CEOs, employee ownership plans, the elimination of tax subsidies, etc. What we need is precisely what Greider reported from Innovest in his book: reliable, high quality information that makes human, social, and environmental issues matter financially. Situated in a context like that described by Bernstein in his 2004 The Birth of Plenty, with the relevant property rights, rule of law, scientific rationality, capital markets, and communications networks in place, it will be impossible to stop a new economic expansion of historic proportions.

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