How Evidence-Based Decision Making Suffers in the Absence of Theory and Instrument: The Power of a More Balanced Approach

The Basis of Evidence in Theory and Instrument

The ostensible point of basing decisions in evidence is to have reasons for proceeding in one direction versus any other. We want to be able to say why we are proceeding as we are. When we give evidence-based reasons for our decisions, we typically couch them in terms of what worked in past experience. That experience might have been accrued over time in practical applications, or it might have been deliberately arranged in one or more experimental comparisons and tests of concisely stated hypotheses.

At its best, generalizing from past experience to as yet unmet future experiences enables us to navigate life and succeed in ways that would not be possible if we could not learn and had no memories. The application of a lesson learned from particular past events to particular future events involves a very specific inferential process. To be able to recognize repeated iterations of the same things requires the accumulation of patterns of evidence. Experience in observing such patterns allows us to develop confidence in our understanding of what that pattern represents in terms of pleasant or painful consequences. When we are able to conceptualize and articulate an idea of a pattern, and when we are then able to recognize a new occurrence of that pattern, we have an idea of it.

Evidence-based decision making is then a matter of formulating expectations from repeatedly demonstrated and routinely reproducible patterns of observations that lend themselves to conceptual representations, as ideas expressed in words. Linguistic and cultural frameworks selectively focus attention by projecting expectations and filtering observations into meaningful patterns represented by words, numbers, and other symbols. The point of efforts aimed at basing decisions in evidence is to try to go with the flow of this inferential process more deliberately and effectively than might otherwise be the case.

None of this is new or controversial. However, the inferential step from evidence to decision always involves unexamined and unjustified assumptions. That is, there is always an element of metaphysical faith behind the expectation that any given symbol or word is going to work as a representation of something in the same way that it has in the past. We can never completely eliminate this leap of faith, since we cannot predict the future with 100% confidence. We can, however, do a lot to reduce the size of the leap, and the risks that go with it, by questioning our assumptions in experimental research that tests hypotheses as to the invariant stability and predictive utility of the representations we make.

Theoretical and Instrumental Assumptions Hidden Behind the Evidence

For instance, evidence as to the effectiveness of an intervention or treatment is often expressed in terms of measures commonly described as quantitative. But it is unusual for any evidence to be produced justifying that description in terms of something that really adds up in the way numbers do. So we often find ourselves in situations in which our evidence is much less meaningful, reliable, and valid than we suppose it to be.

Quantitative measures are often valued as the hallmark of rational science. But their capacity to live up to this billing depends on the quality of the inferences that can be supported. Very few researchers thoroughly investigate the quality of their measures and justify the inferences they make relative to that quality.

Measurement presumes a reproducible pattern of evidence that can serve as the basis for a decision concerning how much of something has been observed. It naturally follows that we often base measurement in counts of some kind—successes, failures, ratings, frequencies, etc. The counts, scores, or sums are then often transformed into percentages by dividing them into the maximum possible that could be obtained. Sometimes the scores are averaged for each person measured, and/or for each item or question on the test, assessment, or survey. These scores and percentages are then almost universally fed directly into decision processes or statistical analyses with no further consideration.

The reproducible pattern of evidence on which decisions are based is presumed to exist between the measures, not within them. In other words, the focus is on the group or population statistics, not on the individual measures. Attention is typically focused on the tip of the iceberg, the score or percentage, not on the much larger, but hidden, mass of information beneath it. Evidence is presumed to be sufficient to the task when the differences between groups of scores are of a consistent size or magnitude, but is this sufficient?

Going Past Assumptions to Testable Hypotheses

In other words, does not science require that evidence be explained by theory, and embodied in instrumentation that provides a shared medium of observation? As shown in the blue lines in the Figure below,

  • theory, whether or not it is explicitly articulated, inevitably influences both what counts as valid data and the configuration of the medium of its representation, the instrument;
  • data, whether or not it is systematically gathered and evaluated, inevitably influences both the medium of its representation, the instrument, and the implicit or explicit theory that explains its properties and justifies its applications; and
  • instruments, whether or not they are actually calibrated from a mapping of symbols and substantive amounts, inevitably influence data gathering and the image of the object explained by theory.

The rhetoric of evidence-based decision making skips over the roles of theory and instrumentation, drawing a direct line from data to decision. In leaving theory laxly formulated, we allow any story that makes a bit of sense and is communicated by someone with a bit of charm or power to carry the day. In not requiring calibrated instrumentation, we allow any data that cross the threshold into our awareness to serve as an acceptable basis for decisions.

What we want, however, is to require meaningful measures that really provide the evidence needed for instruments that exhibit invariant calibrations and for theories that provide predictive explanatory control over the variable. As shown in the Figure, we want data that push theory away from the instrument, theory that separates the data and instrument, and instruments that get in between the theory and data.

We all know to distrust too close a correspondence between theory and data, but we too rarely understand or capitalize on the role of the instrument in mediating the theory-data relation. Similarly, when the questions used as a medium for making observations are obviously biased to produce responses conforming overly closely with a predetermined result, we see that the theory and the instrument are too close for the data to serve as an effective mediator.

Finally, the situation predominating in the social sciences is one in which both construct and measurement theories are nearly nonexistent, which leaves data completely dependent on the instrument it came from. In other words, because counts of correct answers or sums of ratings are mistakenly treated as measures, instruments fully determine and restrict the range of measurement to that defined by the numbers of items and rating categories. Once the instrument is put in play, changes to it would make new data incommensurable with old, so, to retain at least the appearance of comparability, the data structure then fully determines and restricts the instrument.

What we want, though, is a situation in which construct and measurement theories work together to make the data autonomous of the particular instrument it came from. We want a theory that explains what is measured well enough for us to be able to modify existing instruments, or create entirely new ones, that give the same measures for the same amounts as the old instruments. We want to be able to predict item calibrations from the properties of the items, we want to obtain the same item calibrations across data sets, and we want to be able to predict measures on the basis of the observed responses (data) no matter which items or instrument was used to produce them.

Most importantly, we want a theory and practice of measurement that allows us to take missing data into account by providing us with the structural invariances we need as media for predicting the future from the past. As Ben Wright (1997, p. 34) said, any data analysis method that requires complete data to produce results disqualifies itself automatically as a viable basis for inference because we never have complete data—any practical system of measurement has to be positioned so as to be ready to receive, process, and incorporate all of the data we have yet to gather. This goal is accomplished to varying degrees in Rasch measurement (Rasch, 1960; Burdick, Stone, & Stenner, 2006; Dawson, 2004). Stenner and colleagues (Stenner, Burdick, Sanford, & Burdick, 2006) provide a trajectory of increasing degrees to which predictive theory is employed in contemporary measurement practice.

The explanatory and predictive power of theory is embodied in instruments that focus attention on recording observations of salient phenomena. These observations become data that inform the calibration of instruments, which then are used to gather further data that can be used in practical applications and in checks on the calibrations and the theory.

“Nothing is so practical as a good theory” (Lewin, 1951, p. 169). Good theory makes it possible to create symbolic representations of things that are easy to think with. To facilitate clear thinking, our words, numbers, and instruments must be transparent. We have to be able to look right through them at the thing itself, with no concern as to distortions introduced by the instrument, the sample, the observer, the time, the place, etc. This happens only when the structure of the instrument corresponds with invariant features of the world. And where words effect this transparency to an extent, it is realized most completely when we can measure in ways that repeatedly give the same results for the same amounts in the same conditions no matter which instrument, sample, operator, etc. is involved.

Where Might Full Mathematization Lead?

The attainment of mathematical transparency in measurement is remarkable for the way it focuses attention and constrains the imagination. It is essential to appreciate the context in which this focusing occurs, as popular opinion is at odds with historical research in this regard. Over the last 60 years, historians of science have come to vigorously challenge the widespread assumption that technology is a product of experimentation and/or theory (Kuhn, 1961/1977; Latour, 1987, 2005; Maas, 2001; Mendelsohn, 1992; Rabkin, 1992; Schaffer, 1992; Heilbron, 1993; Hankins & Silverman, 1999; Baird, 2002). Neither theory nor experiment typically advances until a key technology is widely available to end users in applied and/or research contexts. Rabkin (1992) documents multiple roles played by instruments in the professionalization of scientific fields. Thus, “it is not just a clever historical aphorism, but a general truth, that ‘thermodynamics owes much more to the steam engine than ever the steam engine owed to thermodynamics’” (Price, 1986, p. 240).

The prior existence of the relevant technology comes to bear on theory and experiment again in the common, but mistaken, assumption that measures are made and experimentally compared in order to discover scientific laws. History shows that measures are rarely made until the relevant law is effectively embodied in an instrument (Kuhn, 1961/1977, pp. 218-9): “…historically the arrow of causality is largely from the technology to the science” (Price, 1986, p. 240). Instruments do not provide just measures; rather they produce the phenomenon itself in a way that can be controlled, varied, played with, and learned from (Heilbron, 1993, p. 3; Hankins & Silverman, 1999; Rabkin, 1992). The term “technoscience” has emerged as an expression denoting recognition of this priority of the instrument (Baird, 1997; Ihde & Selinger, 2003; Latour, 1987).

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

In this context, the relatively recent introduction of measurement models requiring additive, invariant parameterizations (Rasch, 1960) provokes speculation as to the effect on the human sciences that might be wrought by the widespread availability of consistently reproducible effects expressed in common quantitative languages. Paraphrasing Price’s comment on steam engines and thermodynamics, might it one day be said that as yet unforeseeable advances in reading theory will owe far more to the Lexile analyzer (Stenner, et al., 2006) than ever the Lexile analyzer owed reading theory?

Kuhn (1961/1977) speculated that the second scientific revolution of the early- to mid-nineteenth century followed in large part from the full mathematization of physics, i.e., the emergence of metrology as a professional discipline focused on providing universally accessible, theoretically predictable, and evidence-supported uniform units of measurement (Roche, 1998). Kuhn (1961/1977, p. 220) specifically suggests that a number of vitally important developments converged about 1840 (also see Hacking, 1983, p. 234). This was the year in which the metric system was formally instituted in France after 50 years of development (it had already been obligatory in other nations for 20 years at that point), and metrology emerged as a professional discipline (Alder, 2002, p. 328, 330; Heilbron, 1993, p. 274; Kula, 1986, p. 263). Daston (1992) independently suggests that the concept of objectivity came of age in the period from 1821 to 1856, and gives examples illustrating the way in which the emergence of strong theory, shared metric standards, and experimental data converged in a context of particular social mores to winnow out unsubstantiated and unsupportable ideas and contentions.

Might a similar revolution and new advances in the human sciences follow from the introduction of evidence-based, theoretically predictive, instrumentally mediated, and mathematical uniform measures? We won’t know until we try.

Figure. The Dialectical Interactions and Mutual Mediations of Theory, Data, and Instruments

Figure. The Dialectical Interactions and Mutual Mediations of Theory, Data, and Instruments

Acknowledgment. These ideas have been drawn in part from long consideration of many works in the history and philosophy of science, primarily Ackermann (1985), Ihde (1991), and various works of Martin Heidegger, as well as key works in measurement theory and practice. A few obvious points of departure are listed in the references.


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

Alder, K. (2002). The measure of all things: The seven-year odyssey and hidden error that transformed the world. New York: The Free Press.

Aldrich, J. (1989). Autonomy. Oxford Economic Papers, 41, 15-34.

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

Baird, D. (1997, Spring-Summer). Scientific instrument making, epistemology, and the conflict between gift and commodity economics. Techné: Journal of the Society for Philosophy and Technology, 3-4, 25-46. Retrieved 08/28/2009, from

Baird, D. (2002, Winter). Thing knowledge – function and truth. Techné: Journal of the Society for Philosophy and Technology, 6(2). Retrieved 19/08/2003, from

Burdick, D. S., Stone, M. H., & Stenner, A. J. (2006). The Combined Gas Law and a Rasch Reading Law. Rasch Measurement Transactions, 20(2), 1059-60 [].

Carroll-Burke, P. (2001). Tools, instruments and engines: Getting a handle on the specificity of engine science. Social Studies of Science, 31(4), 593-625.

Daston, L. (1992). Baconian facts, academic civility, and the prehistory of objectivity. Annals of Scholarship, 8, 337-363. (Rpt. in L. Daston, (Ed.). (1994). Rethinking objectivity (pp. 37-64). Durham, North Carolina: Duke University Press.)

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

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

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

Hacking, I. (1983). Representing and intervening: Introductory topics in the philosophy of natural science. Cambridge: Cambridge University Press.

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

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

Heelan, P. A. (1998, June). The scope of hermeneutics in natural science. Studies in History and Philosophy of Science Part A, 29(2), 273-98.

Heidegger, M. (1977). Modern science, metaphysics, and mathematics. In D. F. Krell (Ed.), Basic writings [reprinted from M. Heidegger, What is a thing? South Bend, Regnery, 1967, pp. 66-108] (pp. 243-282). New York: Harper & Row.

Heidegger, M. (1977). The question concerning technology. In D. F. Krell (Ed.), Basic writings (pp. 283-317). New York: Harper & Row.

Heilbron, J. L. (1993). Weighing imponderables and other quantitative science around 1800. Historical studies in the physical and biological sciences), 24(Supplement), Part I, pp. 1-337.

Hessenbruch, A. (2000). Calibration and work in the X-ray economy, 1896-1928. Social Studies of Science, 30(3), 397-420.

Ihde, D. (1983). The historical and ontological priority of technology over science. In D. Ihde, Existential technics (pp. 25-46). Albany, New York: State University of New York Press.

Ihde, D. (1991). Instrumental realism: The interface between philosophy of science and philosophy of technology. (The Indiana Series in the Philosophy of Technology). Bloomington, Indiana: Indiana University Press.

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

Ihde, D., & Selinger, E. (Eds.). (2003). Chasing technoscience: Matrix for materiality. (Indiana Series in Philosophy of Technology). Bloomington, Indiana: Indiana University Press.

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

Kula, W. (1986). Measures and men (R. Screter, Trans.). Princeton, New Jersey: Princeton University Press (Original work published 1970).

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

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

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

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

Maas, H. (2001). An instrument can make a science: Jevons’s balancing acts in economics. In M. S. Morgan & J. Klein (Eds.), The age of economic measurement (pp. 277-302). Durham, North Carolina: Duke University Press.

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

Mendelsohn, E. (1992). The social locus of scientific instruments. In R. Bud & S. E. Cozzens (Eds.), Invisible connections: Instruments, institutions, and science (pp. 5-22). Bellingham, WA: SPIE Optical Engineering Press.

Polanyi, M. (1964/1946). Science, faith and society. Chicago: University of Chicago Press.

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

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

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

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

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

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

Thurstone, L. L. (1959). The measurement of values. Chicago: University of Chicago Press, Midway Reprint Series.

Wright, B. D. (1997, Winter). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45, 52 [].

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