Path Analysis And Its Application In Medical Studies

Path analysis is an extension of the regression model, used to research andanalyze complicate causality of variables and effects of them in the model insocial investigation, psychological investigation and medical research.Particularly, latent variable path analysis is used to research the causalitybetween latent variables and manifest variables, to solve the problem that latentvariables affect the others in the social and psychological fields. Path analysis is a useful statistical tool to analyze the causality of variables.Conventional regression is done for each variable in the model as a dependenton others, which indicates causes. Traditional regression model does not showmultiple causal structures. Although the regression model includes independentvariables, which are parallel, partial regression coefficients indicate net effect ofeach independent variable on dependent variable controlling the others. Pathcoefficients may be used to decompose correlations in the model into direct andindirect effects. The indirect effects measure the effect of the interveningvariables. The researcher may dig a large amount of information out of the datain order to solve the social and psychological problems. This paper firstly focuses on the theory of path analysis including basictheory, the matrix solutions on path analysis, data example, the step of analysis,and the way to explain the result of the modeling. The article not only givesrational statistical and professional interpretations of path analysis modeling bythe way of path diagram and linear structural equations, but also systematicallyanalyzes the direct and indirect effect of the variables. Then using an example of factors of the sensitive tointerpersonal relationship in the college students and combiningpsychological knowledge, I create a path diagram. Applying theCALIS procedure of SAS 8.0 software, we give a rationalinterpretation. We draw a conclusion that latent variable pathanalysis modeling provides more objective information than theordinary multiple regression modeling.