David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 68 (1):13 - 36 (1986)
The fact that causal laws in the social sciences are most realistically expressed as both multivariate and stochastic has a number of very important implications for indirect measurement and generalizability. It becomes difficult to link theoretical definitions of general constructs in a one-to-one relationship to research operations, with the result that there is conceptual slippage in both experimental and nonexperimental research. It is argued that problems of this nature can be approached by developing specific multivariate causal models that incorporate sources of measurement bias, along with the theoretical variables of interest. Many general concepts are defined in such a way that causal assumptions are built into the definitions themselves. Additionally, in any given piece of research it is necessary to omit many variables from consideration, and this is often done without careful consideration of the assumptions required to justify such omissions. Finally, generalization to more inclusive populations or a diversity of settings ordinarily requires one to replace "constants" by variables. It is concluded that the criteria of parsimony, generalizability, and precision are incompatible, given the multivariate nature of social causation, and the author expresses his own preference for sacrificing parsimony in favor of the objectives of achieving increased precision and generalizability of social science laws.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Hubert M. Blalock (1986). Multiple Causation, Indirect Measurement and Generalizability in the Social Sciences. Synthese 68 (1):13-36.
Harold Kincaid (1990). Defending Laws in the Social Sciences. Philosophy of the Social Sciences 20 (1):56?83.
Julian Reiss (2009). Causation in the Social Sciences: Evidence, Inference, and Purpose. Philosophy of the Social Sciences 39 (1):20-40.
A. Ruzzene (2012). Drawing Lessons From Case Studies by Enhancing Comparability. Philosophy of the Social Sciences 42 (1):99-120.
R. Keith Sawyer (2003). Nonreductive Individualism Part II—Social Causation. Philosophy of the Social Sciences 33 (2):203-224.
Veronika A. Andorfer & Ulf Liebe (2012). Research on Fair Trade Consumption—A Review. Journal of Business Ethics 106 (4):415-435.
Holger Andreas (2008). Ontological Aspects of Measurement. Axiomathes 18 (3):379-394.
Frederick S. Ellett Jr & David P. Ericson (1986). An Analysis of Probabilistic Causation in Dichotomous Structures. Synthese 67 (2):175 - 193.
Ludwik Finkelstein (2003). Widely, Strongly and Weakly Defined Measurement. Measurement 34 (1):39-48.
Charles Weijer, Characterizing the Population in Clinical Trials: Barriers, Comparability, and Implications for Review.
Frederick S. Ellett Jr & David P. Ericson (1983). The Logic of Causal Methods in Social Science. Synthese 57 (1):67 - 82.
Frederick S. Ellett & David P. Ericson (1983). The Logic of Causal Methods in Social Science. Synthese 57 (1):67-82.
Ludwik Finkelstein (2009). Widely-Defined Measurement. An Analysis of Challenges. Measurement 42 (9):1270–1277.
Added to index2011-05-29
Total downloads6 ( #213,996 of 1,101,879 )
Recent downloads (6 months)3 ( #128,836 of 1,101,879 )
How can I increase my downloads?