Foundations of Measurement Across the Sciences

13:00 – 14:30 CET ?

Norms for Successful Measurement

Alistair Isaac ??(1.5 KB)

• Using examples from physics I defend two norms for successful measurement: precision and convergence. A diachronic trajectory of measurements satisfies the norm of precision if it gives increasingly precise results (i.e. results with more significant figures or tighter error bars). A trajectory of measurements targeted at a single quantity converges if different methods, employing different background assumptions, produce the same outcome (within the bounds of error). I argue that measured values produced by methods satisfying precision and convergence are "successful" in the sense that we may confidently take them to veridically represent objective quantities in the world. I conclude by considering some challenges for satisfying these norms in consciousness science.

15:00 – 16:30 CET ?

Measurement, Uncertainty, and Precision

Michael Miller ??(1.7 KB)

• Quantum mechanics suggests that there are fundamental limits on the precision with which dynamical physical quantities can be instantiated, and hence measured. These limits are typically characterized in the form of uncertainty relations which are expressed as bounds on the variance of the quantity. In this talk I will argue that variance-based notions of precision are ill-suited to capturing what it means for a physical quantity to be instantiated with less than full precision. I will develop an alternative proposal which better captures how quantum mechanical quantities are instantiated. Along the way I will clarifiy the relationship between the precision and determinacy of a physical quantity. The analysis I develop is sufficiently general to apply to quantities in general, and not just quantum mechanical quantities in particular. 

17:30 – 19:00 CET ?

Theory-Based Measurement in Psychology

Matthias Borgstede ??(2.0 KB)

• Representational Measurement Theory (RMT) defines measurable attributes as classes of relational structures that allow for numerical representations. Although RMT provides a rigorous framework for the construction of measurement scales, it makes no assertions about the theoretical meaning of measurable attributes. I propose Theory-Based Measurement, which integrates RMT with a semantic theory conception, such that measurable attributes may be identified with theoretically meaningful concepts. I further apply the framework of Theory-Based Measurement to psychological attributes, which are hypothesized to be internally generated stimuli that result from selection of relational responses and allow for the effective prediction of the environmental consequences of behavior. First empirical results (N=9) support this hypothesis, revealing that relational responding can indeed be selected according to a quantitative structure. Whereas subjects were not able to name the stimulus features they responded to, subjects responded according to the selected quantitative structure and used quantitative vocabulary to describe their subjective experience.

19:30 – 21:00 CET ?

How Can We Justify the Epistemic Prestige of Measurement?

Luca Mari ??(1.3 KB)

• The talk discusses the conditions that justify the epistemic prestige attributed to measurement, in the perspective of the current trends aiming at expanding the scope of measurement to psychosocial properties, to non-quantitative properties, and in the context of the digital transformation. The thesis is maintained that measurement is an empirical property evaluation characterized by the public and transparent statement of the trustworthiness of the information it produces, stated in terms of its objectivity and intersubjectivity.

21:30 – 22:30 CET ?

Differences in Measurement Across the Sciences

Panel Discussion ??(808.0 B)

13:00 – 14:30 CET ?

In What Sense does RTM Provide 'Foundations' of Measurement?

Jo Wolff ??(1.5 KB)

• The Representational Theory of Measurement (RTM) is best known through the three volumes of "Foundations of Measurement" by Krantz, Luce, Suppes and Tversky, published between 1971 and 1990. As the title of this work suggests, RTM is here conceived of as foundational for measurement, a claim that has met with much criticism. My talk asks in what sense RTM provides foundations for measurement. I argue that RTM is best understood as providing semantic foundations for numerical representation. This interpretation of the foundational ambition of RTM explains many of its features and provides a response to some of the criticisms often raised against RTM.

15:00 – 16:30 CET ?

Item Response Models and Psychological Measurement

Leah Feuerstahler ??(1.6 KB)

• In psychology and other social sciences, latent variable models are often used to infer measurements of variables that cannot be directly observed. Latent variable models such as item response theory (IRT) aggregate several related observations of behavior, attitudes, etc. into an estimate of a latent variable based on a set of assumptions. In this talk, I will provide an overview of the assumptions and statistical indeterminacies associated with IRT models, focusing on the nature of the underlying latent trait continuum. I will also address various misconceptions about the IRT latent trait scale and present some emerging research on asymmetric IRT models that may provide a more valid representation of the latent trait.

17:30 – 19:00 CET ?

Lessons for Human Science Measurement from the Quantification of Earthquake Size

Miguel Ohnesorge ??(2.1 KB)

• There are longstanding debates about whether the human sciences can quantify the attributes they study. We identify a basic problem within these debates: success standards and expectations are modeled almost exclusively on experimental physics. As a result, researchers in measurement theory, psychology, and philosophy, have (i) misidentified experimental control as a necessary condition for quantification and (ii) overlooked central methodological lessons on how quantification without experimental control might succeed. To remedy this situation, we present novel historical research on how twentieth-century seismologists quantified “earthquake size.” The study serves to (i) refute the idea that experimental control is a necessary condition for quantification and (ii) provide an alternative methodology for quantifying without high degrees of experimental control. We contrast this methodology to an ongoing effort at quantifying reading comprehension. The contrast helps to better understand both the achievements and persistent problems of quantification in the human sciences and demonstrates the need for more diverse historical models of successful quantification.

19:30 – 21:00 CET ?

Quantifying the Human?

Cristian Larroulet Philippi ??(2.4 KB)

• Quantitative measurement in the human sciences remains both widespread and controversial. Are depression scales, intelligence tests, etc. valid measurement instruments? Do they deliver quantitative or merely ordinal information? I discuss two approaches for understanding practices of quantitative measurement of theoretical attributes in the early stages of research. One uses causal notions to characterize dispositional attributes and to understand how they relate to measurement indications. It aims at standard epistemic desiderata in science (discovery, explanation, prediction) and offers good answers to traditional worries about human attributes (namely, are they quantitative?) and about their measurement instruments (namely, are they valid?). A second approach uses the notion of value (as sketched in Dan Hausman's 2015 Valuing Health) to make sense of quantification practices. This approach does not resemble what scientists think of their measurement practices: it is not designed for the testing of tentative concepts but rather to standardize political decision making. Yet, I argue, this approach is the most plausible candidate for making sense of some human sciences’ measurement practices as quantifying anything. Such is the case for measurements that (i) combine distinct dimensions of the phenomena at stake and (ii) for which we don’t observe serious efforts aiming at embedding such measurements in predictive and explanatory networks. I illustrate with two examples: depression severity (HAMD) and the Human Development Index (HDI).

21:30 – 22:30 CET ?

Challenges and Lessons for Measurement in Consciousness Science

Panel Discussion ??(846.0 B)