Thomas Burch

Thomas BurchThomas Burch is a Canadian-American social demographer, currently Adjunct Professor, University of Victoria [Canada], and Regional Associate, Center for Studies in Demography and Ecology, University of Washington [Seattle]. He is Professor Emeritus, Western University [London, Ontario], where he taught for 26 years, helping develop a Ph.D. program in social demography. Previously, he was Associate Director, Demographic Division, The Population Council, and Demographic Director, Center for Population Research, Georgetown University. While at Georgetown he served on the Vatican Commission on Population, Family and Birth. He was president of the Canadian Population Society [1992-94], and was awarded the CPS Lifetime Achievement Award [ex aequo] in 2013. His primary research focus was on fertility and on household and family demography and, late in his career, on the role of computer modeling as a tool for theoretical work in demography. This culminated in the publication of Model-Based Demography: Essays on Integrating Data, Technique and Theory [Springer Open, 2018], in the series Demographic Research Monographs, of the Max Planck Institute for Demographic Research, Rostock, Germany. The book argues for the fruitfulness of viewing demography – past, present and future – from the perspective of the semantic or ‘model-based’ school of philosophy of science.

Papers Published in World Economics:

Error in Demographic and Other Quantitative Data and Analyses
Author: Thomas Burch

Statistical data consumed, analysed and produced contain errors from more sources than is often recognised and the commercialisation of survey and other statistical research and ‘inventions’ such as ‘big data’ has led to naïve and faulty analysis and propaganda. Oskar Morgenstern has noted that, in contrast to physics, there is no estimate of statistical error within economics and the various sources of error that come into play in the social sciences suggest that the error in economic observations is substantial. It is important to recognise the phenomenon of the propagation of errors; errors in our results may be disproportionate to errors in our input data. Despite documented problems social scientists cannot give up on quantitative data since many of the most important questions in social science are matters of more or less, not either/or.

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