path analysis

A form of multiple regression , in which standardized regression coefficients (path coefficients) are computed by stipulating the structure of hypothesized relationships between variables , in an explicitly formulated causal model . The causal connections are conceived as unidirectional and presented in a path diagram. In essence, therefore, the technique is merely a diagrammatic representation of a set of regression equations for which the variables are assumed to have a temporal ordering (see, Causal Modelling, 1983).

A hypothetical example is provided in the diagram below, which indicates the causal connections between the four variables of father's occupation, father's education, respondent's education, and respondent's occupation. In this model, social origins are placed before respondent's educational achievement, which is in turn placed before his or her occupational attainment. This technique attaches quantitative estimates to the causal connections in question, although it does not actually establish causality, since the pattern of relationships between the variables is entirely dependent on the researcher's own judgements about the likely causality among the variables. Where it is impossible to specify directionality, variables are deemed to be correlates (as in the case of social background and parental education above), and the link between them is conventionally described by a curved arrow having two heads.

The principal advantage of path analysis is that it allows the researcher to estimate the relative influence of variables within a causal network. The obvious disadvantage is that the model depends upon the researcher's own conception of the likely causal sequences involved, and since this cannot be validated or invalidated by the analysis, misleading path diagrams are sometimes produced. See also multivariate analysis.

FIGURE 6: An example path analysis of the causal connections between a respondent's and his/her father's education and occupation