Statisticism: the erroneous notion that computing is doing research

Notes on Social Measurement: Historical and Critical is a major and insightful book by a distinguished American sociologist Otis Dudley Duncan (1921-2004) published in 1984. Duncan has introduced many statistical techniques to sociology, and studied mainly intergenerational occupational mobility. In a paper “Otis Dudley Duncan, quantitative sociologist par excellence: Path analysis, loglinear methods, and Rasch models“, a statistician Leo Goodman referred to Duncan as “the most important quantitative sociologist in the world in the latter half of the 20th century” (2007: 131).
In a concluding chapter of the book, which is an erudite history and critical account of measurement and numerical methods in science and policy making, Duncun elaborates the term of statisticism. The more I mull over this concept, the more I realize the necessity of its inculcation and substantive absorption into nowadays social sciences in general, and in economics in particular. Why? I respectfully leave the floor to Professor Duncan:

“Coupled with downright incompetence in statistics, we often find the syndrome that I have come to call statisticism: the notion that computing is synonymous with doing research, the naïve faith that statistics is a complete or sufficient basis for scientific methodology, the superstition that statistical formulas exist for evaluating such things as the relative merits of different substantive theories or the “importance” of the causes of a “dependent variable”; and the delusion that decomposing the covariations of some arbitrary and haphazardly assembled collection of variables can somehow justify not only a “causal model” but also, praise a mark, a “measurement model.” There would be no point in deploring such caricatures of the scientific enterprise if there were a clearly identifiable sector of social science research wherein such fallacies were clearly recognized and emphatically out of bounds.” (Duncan 1984: 226).

statistics

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