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Some Cautions Concerning The Application Of Causal Modeling Methods

Some Cautions Concerning The Application Of Causal Modeling Methods Literal acceptance of the results of fitting "causal" models to correlational data can lead to conclusions that are of questionable value. The long-established principles of scientific inference must still be applied. In particular, the possible influence of variables that are not observed must be considered; the well-known difference between correlation and causation is still relevant, even when variables are separated in time; the distinction between measured variables and their theoretical counterparts still exists; and ex post facto analyses are not tests of models. There seems to be some danger of overlooking these principles when complex computer programs are used to analyze. correlational data, even though these new methods provide great increases in the rigor with which correlational data can be analyzed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multivariate Behavioral Research Taylor & Francis

Some Cautions Concerning The Application Of Causal Modeling Methods

Multivariate Behavioral Research , Volume 18 (1): 12 – Jan 1, 1983

Some Cautions Concerning The Application Of Causal Modeling Methods

Multivariate Behavioral Research , Volume 18 (1): 12 – Jan 1, 1983

Abstract

Literal acceptance of the results of fitting "causal" models to correlational data can lead to conclusions that are of questionable value. The long-established principles of scientific inference must still be applied. In particular, the possible influence of variables that are not observed must be considered; the well-known difference between correlation and causation is still relevant, even when variables are separated in time; the distinction between measured variables and their theoretical counterparts still exists; and ex post facto analyses are not tests of models. There seems to be some danger of overlooking these principles when complex computer programs are used to analyze. correlational data, even though these new methods provide great increases in the rigor with which correlational data can be analyzed.

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Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-7906
eISSN
0027-3171
DOI
10.1207/s15327906mbr1801_7
Publisher site
See Article on Publisher Site

Abstract

Literal acceptance of the results of fitting "causal" models to correlational data can lead to conclusions that are of questionable value. The long-established principles of scientific inference must still be applied. In particular, the possible influence of variables that are not observed must be considered; the well-known difference between correlation and causation is still relevant, even when variables are separated in time; the distinction between measured variables and their theoretical counterparts still exists; and ex post facto analyses are not tests of models. There seems to be some danger of overlooking these principles when complex computer programs are used to analyze. correlational data, even though these new methods provide great increases in the rigor with which correlational data can be analyzed.

Journal

Multivariate Behavioral ResearchTaylor & Francis

Published: Jan 1, 1983

There are no references for this article.