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- W2037667990 abstract "A multivariate causal law is one in which the expected value of a dependent variable Y is expressed as a function of several independent variables X,, X2,..., Xn. In the behavioral sciences, such laws are usually constructed by least-squares techniques, particularly by multiple-regression techniques. When variables are measured on scales which are at least interval, the best-known work in the behavioral sciences on constructing such laws in the linear case is by Blalock (1964); a brief description and further references are given by Darlington (1968, pp. 166-168). The nonlinear case is more complex; two important but difficult papers are by Box and Hunter (1957) and Draper and Hunter (1967). Once a causal law is constructed by these techniques, the problem often arises of stating the importance of each X variable in the law. This paper attempts to present a general solution to this problem, which applies to both linear and nonlinear causal laws. When applied to the linear case, the solution is equivalent to that described by Blalock and others, which is associated with the term path analysis. Thus the principal contribution of the new solution is in its application to the nonlinear case." @default.
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- W2037667990 date "1972-01-01" @default.
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- W2037667990 title "Assessing the Importance of Independent Variables in Nonlinear Causal Laws" @default.
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- W2037667990 doi "https://doi.org/10.3102/00028312009003449" @default.
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