Abstract
Abstract Assuming that a model consists of a system of equations and assumptions about the relationships among a set of variables, where variables can be observed, unobserved (latent), or disturbances (error), path analysis provides an algorithm to understand the direct, indirect, and total effect of one variable on another in a hypothesized model. It also allows a test of whether the hypothesized model is consistent with the covariances (correlations) of observed variables. It is not intended as a tool to discover causal relations. The primary components of path analysis: path diagram, estimation of path coefficients, and decomposition of effects are illustrated. The current form of path analysis is best shown through structural equation modeling.
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