|Abstract||A general principle for good pedagogic strategy is this: other things equal, make the essential principles of the subject explicit rather than tacit. We think that this principle is routinely violated in conventional instruction in statistics. Even though most of the early history of probability theory has been driven by causal considerations, the terms “cause” and “causation” have practically disappeared from statistics textbooks. Statistics curricula guide students away from the concept of causality, into remembering perhaps the cliche disclaimer “correlation does not mean causation,” but rarely thinking about what correlation does mean. The treatment of causality is a serious handicap to later studies of such topics as experimental design, where often the main goal is to establish (or disprove) causation. Much of the technical vocabulary of the language used in research design textbooks consists in euphemisms for speciﬁc causal relations, e.g, “latent variable,” “intervening variable,” “confounding factor,” etc. The multiplicity of terms used to refer to causation results in confusion and, in eﬀect, may hinder understanding of the basic principles of research design.|
|Keywords||No keywords specified (fix it)|
|Categories||No categories specified (fix it)|
|Through your library||Only published papers are available at libraries|
Similar books and articles
James M. Joyce (2010). Causal Reasoning and Backtracking. Philosophical Studies 147 (1).
Greg Bamford (2003). Research, Knowledge and Design. In Clare Newton, Sandra Kaj-O'Grady & Simon Wollan (eds.), Design + Research: Project Based Research in Architecture. Second International Conference of the Association of Australasian Schools of Architecture, Melbourne 28 – 30 September, 2003. Association of Architecture Schools of Australasia.
Gurol Irzik (1986). Causal Modeling and the Statistical Analysis of Causation. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:12 - 23.
Paul Thagard (1998). Explaining Disease: Correlations, Causes, and Mechanisms. Minds and Machines 8 (1):61-78.
David L. DeMets (1999). Statistics and Ethics in Medical Research. Science and Engineering Ethics 5 (1).
Tuomas K. Pernu (2013). The Principle of Causal Exclusion Does Not Make Sense. Philosophical Forum 44 (1):89-95.
Sieghard Beller & Gregory Kuhnm (2007). What Causal Conditional Reasoning Tells Us About People's Understanding of Causality. Thinking and Reasoning 13 (4):426 – 460.
Mary L. Cummings (2006). Integrating Ethics in Design Through the Value-Sensitive Design Approach. Science and Engineering Ethics 12 (4).
Lei Zhong (2011). Can Counterfactuals Solve the Exclusion Problem? Philosophy and Phenomenological Research 83 (1):129-147.
Frederick S. Ellett Jr & David P. Ericson (1983). The Logic of Causal Methods in Social Science. Synthese 57 (1):67 - 82.
Added to index2010-12-22
Total downloads3 ( #202,056 of 549,196 )
Recent downloads (6 months)1 ( #63,397 of 549,196 )
How can I increase my downloads?