Big data represents a significant challenge for the social sciences. From a philosophy-of-science perspective, it is important to reflect on related theories and processes for developing them. In this paper, we start by examining different views on the role of theories in big data-related social research. Then, we try to show how big data is related to standards for evaluating theories. We also outline how big data affects theory- and data-based research approaches and the process of theory building. Discussions include (...) a summary of lines of reasoning, limitations, and a proposal for future developmental steps. (shrink)

In this paper we motivate and develop the analytic theory of measurement, in which autonomously specified algebras of quantities (together with the resources of mathematical analysis) are used as a unified mathematical framework for modeling (a) the time-dependent behavior of natural systems, (b) interactions between natural systems and measuring instruments, (c) error and uncertainty in measurement, and (d) the formal propositional language for describing and reasoning about measurement results. We also discuss how a celebrated theorem in analysis, known as Gelfand (...) representation, guarantees that autonomously specified algebras of quantities can be interpreted as algebras of observables on a suitable state space. Such an interpretation is then used to support (i) a realist conception of quantities as objective characteristics of natural systems, and (ii) a realist conception of measurement results (evaluations of quantities) as determined by and descriptive of the states of a target natural system. As a way of motivating the analytic approach to measurement, we begin with a discussion of some serious philosophical and theoretical problems facing the well-known representational theory of measurement. We then explain why we consider the analytic approach, which avoids all these problems, to be far more attractive on both philosophical and theoretical grounds. (shrink)

We do indeed “live and work in a time when the issues facing education, many of which have been with us for a considerable period, are being approached primarilythrough measurement – classroom assessment, research methods, standardized testing, international comparisons”. It is also true that “we do not often stop to consider what counts – and alternatively, what doesn’t count – in a climate where measuring up to a standard is the name of the game. At a deeper level, we rarely (...) raise questions about measurement itself.” Heidegger argued that what is “most thought provoking [in this ‘thought provoking age’] is that we are still not thinking,” in What is called thinking? . This somewhat startling assertion deserves careful attention especially in relation to the quote above . Heidegger’s assertion is pertinent for a number of reasons: he associated this “not thinking” with a “critical moment in history”, with a “call”, and with a “miscalculation”. I will argue that it is important to reflect on a number of questions: what is thinking, especially in relation to measurement? Was Heidegger correct in arguing that we have “miscalculated” in so far as we have sought “the safety of the mere drive for calculation” ? And how does the desire for a higher form of “representational thinking” in these contexts serve and promote a number of aims in higher education, such as learning and even “flourishing”? I will attempt to provide answers to a number of these questions by reflecting on the broad but fundamentally important question of measurement and its limits. (shrink)

Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a new account of (...) both concrete and mathematical models, with special emphasis on the intentions of theorists, which are necessary for evaluating the model-world relationship during the practice of modeling. Although mathematical models form the basis of most of contemporary modeling, my discussion begins with more traditional, concrete models such as the San Francisco Bay model. (shrink)