Goldsmith J R
Int J Epidemiol. 1977 Dec;6(4):391-9. doi: 10.1093/ije/6.4.391.
Conventional regression analysis is based on assumptions of bidirectional associations between pairs of independent variables. In a number of circumstances these assumptions are not plausible. Structural representation in conventional regression is based on a set of parallel paths between independent and dependent variables; when the implausible assumptions are excluded, a different structrual relation between the independent and dependent variables is found. It permits a series associative path between independent variables. Two criteria for modification of conventional multivariate analysis are presented. They are when bilateral symmetry among independent variables is implausible on the basis of a priori information, and when there are significant differences between zero order and first order partial correlation coefficients. When these criteria are applied, there may result a series-parallel matrix of associations. For analysis of such a matrix, the procedures of path analysis are appropriate. The concepts are illustrated with environmental examples, and path analytical computations are worked out for a set of data on social and environmental factors which affected infant mortality in England and Wales between 1928-1938.
传统回归分析基于自变量对之间双向关联的假设。在许多情况下,这些假设并不合理。传统回归中的结构表示基于自变量和因变量之间的一组平行路径;当排除不合理的假设时,会发现自变量和因变量之间存在不同的结构关系。它允许自变量之间存在一系列关联路径。提出了修改传统多元分析的两个标准。它们是基于先验信息,自变量之间的双边对称性不合理时,以及零阶和一阶偏相关系数之间存在显著差异时。当应用这些标准时,可能会产生一个串并联关联矩阵。对于这种矩阵的分析,路径分析程序是合适的。文中用环境方面的例子对这些概念进行了说明,并针对一组关于1928年至1938年间影响英格兰和威尔士婴儿死亡率的社会和环境因素的数据进行了路径分析计算。