Science Outside the Laboratory: Measurement in Field Science and Economics by Marcel Boumans (PDF)

Description

The conduct of most of social science occurs outside the laboratory. Such studies in field science explore phenomena that cannot for practical, technical, or ethical reasons be explored under controlled conditions. These phenomena cannot be fully isolated from their environment or investigated by manipulation or intervention. Yet measurement, including rigorous or clinical measurement, does provide analysts with a sound basis for discerning what occurs under field conditions, and why.In Science Outside the Laboratory, Marcel Boumans explores the state of measurement theory, its reliability, and the role expert judgment plays in field investigations from the perspective of the philosophy of science. Its discussion of the problems of passive observation, the calculus of observation, the two-model problem, and model-based consensus uses illustrations drawn primarily from economics.Rich in research and discussion, the volume clarifies the extent to which measurement provides valid information about objects and events in field sciences, but also has implications for measurement in the laboratory. Scholars in the fields of philosophy of science, social science, and economics will find Science Outside the Laboratory a compelling and informative read.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This book is certainly not the usual “Introduction to measurement and statistics” for students and researchers in economics and some other (applied) social or behavioral research disciplines. It takes a fresh look from different perspectives on the measurement problems with which both theoretical and practical oriented scientists are confronted when they have to rely on observations and data from the field. Such data may be less controlled, less reliable and less valid than typical laboratory data, leading to all kinds of systematic or unsystematic errors, but in typical field sciences, one has no choice.In this book, the author works out a measurement philosophy and methodology for this kind of science and research, not by replacing the superb achievements of the representational (model) measurement approach, but by judiciously and carefully explaining what has to be added to it in order to get a full-blown blueprint for such an extended measurement approach for the field sciences.In a nutshell, the author recommends a well-balanced mixture of objective (Type A: mechanistic) and subjective (Type B: expert judgment) knowledge in order to regain what will be lost by leaving the cleanroom of laboratory research. This is all well-documented, pointing out the diverse origins of its ideas. Especially interesting are the many illustrations from the long history of economic research, but also other research fields, e.g. medical decision-making, are discussed at length, which makes the book equally interesting and relevant for researchers outside the main field of economics and econometrics.Probably because of the broad perspective taken by this book, I missed here and there a deeper discussion and explanation of certain concepts or methods which are part of the overall proposal, e.g. the distinction between white-box, gray-box and black-box models and tests (presumably borrowed from software and system engineering modelling and testing) is not made clear enough for those, who may have never heard of it or read about it before. All-in-all: highly recommend reading, fitting well in the steadily emerging pluralistic philosophy of science.

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