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Path Analysis in Genetic Epidemiology and Alternatives

David Freedman's critique "As Others See Us: A Case Study in Path Analysis" raises a variety of issues and problems inherent to the application of linear structural models to social data. We concur with many of the concerns he raises as to the unqualified application of path analysis to sociometric, psychometric, and other complex data sets. We have discussed these and other methodological problems of path analysis in the context of genetic epidemiology (Karlin, Cameron, & Chakraborty, 1983). Moreover, we have developed an essentially model-free approach, structured exploratory data analysis (SEDA), for analyzing familial trait variables or other structured data emphasizing empirical and nonparametric methods of data analysis while recognizing the value of qualitative insights derived from a variety of pertinent models. These include the concepts of association arrays presented as an alternative to simple correlational analyses for studying the stochastic dependence between variables (Karlin, 1983; Karlin, Carmelli, et al., 1982; Karlin, Williams, et al., 1982), and SEDA-functionals that provide a method for assessing familial similarity of trait values in terms of standardized distance measures (Karlin, Cameron, & Williams, 1983; Karlin & Carmelli, 1985; Karlin & Williams, 1984; Karlin, Williams, & Carmelli, 1981). In the present discussion these ideas are illustrated with data examples for the variables of obesity in spouses and cognitive ability in male and female offspring of Caucasoid and Japanese descent.