文档介绍：Garson - Path analysis 1 Garson - Path analysis 1 David Garson Professor of Public Administration, North Carolina State University, Raleigh, North Carolina. Path Analysis Lecture Notes, 2008 Contents Key concepts and terms Path coefficients Path multiplication rule Effect position Path analysis with structural equati on modeling Assumptions Frequently asked questions Bibliography Overview Path analysis is an extension of the regression model, used to test the fit of the correlation m atrix against two or more causal models which are pared by the researcher. The model is usually depicted in a circle- and- arrow figure in which single- headed arrows indicate causation. A regression is done for each variable in the model as a depend ent on others which the model indicates are causes. The regression weights predicted by the model pared with the observed correlation matrix for the variables, and a goodness- of- fit statistic is calculated. The best- fitting of two or more models is selected by the researcher as the best model for advancement of theory. Path analysis requires the usual assumptions of regression. It is particularly sensitive to model specification because failure to include relevant causal variables or inclusion of ex traneous variables often substantially affects the path coefficients, which are used to assess the relative importance of various direct and indirect causal paths to the dependent variable. Such interpretations should be undertaken in the context par ing alternative models, after assessing their goodness of fit discussed in the section on structural equation modeling (SEM packages monly used today for path analysis in lieu o f stand- alone path analysis programs). When the variables in the model are latent variables measured by multiple observed indicators, path analysis is termed structural equation modeling , treated separately. We follow the conventional terminology by which path analysis refers to modeling single