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Path Analysis
Path analysis is a method used in statistics that predicts more than one dependent variable. It is an extension of a multiple regression model that is use to predict a single dependent variable and a multivariate technique that specifies relationships between observed measure variables. Path analysis is use to analyze models that are more complex and realistic than multiple regression. The predicting order for variables in multiple regression models are X causes Y but in path analysis the predicting order for variables is X causes Y, Y causes Z (Grimm & Yarnold, 2000). Path analysis tests a hypothesized causal model among a set of variables based on findings of previous research. Causal modeling in path analysis refers to*…show more content…*

The model is presented both in words and in a path diagram. The causal flow of the diagram is from left to right. There is a direct and indirect effect of the variables in the path diagram. Arrows show assumptions of causal relations among the variables. The single-headed arrow points cause to effect. The double-headed curved arrow shows no causal relationships except that the variables correlate. Independent variables (IV) are called exogenous and dependent variables (DV) are called endogenous (Green & Salkind, 2014). Exogenous variables are explained by the model and endogenous variables are not explained by the model. Path models have a combination of exogenous and endogenous variables. Total effect is the sum of both direct and indirect effects. Path analysis is use to show the linear relationship between the variables based on the researchers’ hypothesized idea. Path coefficients represent the magnitude of the direct effect of one variable on another. Path coefficients are standardized because it predicts one variable from another and they come from multiple regressions. Unmeasured variables on endogenous variables are the residual path coefficients. The path models can be recursive or non-recursive. In a recursive model, all of the effects are unidirectional, and it always fit the observed data perfectly. A recursive model does not have reciprocal causation among variables or loops. A model is non-recursive if one or more of the links were

The model is presented both in words and in a path diagram. The causal flow of the diagram is from left to right. There is a direct and indirect effect of the variables in the path diagram. Arrows show assumptions of causal relations among the variables. The single-headed arrow points cause to effect. The double-headed curved arrow shows no causal relationships except that the variables correlate. Independent variables (IV) are called exogenous and dependent variables (DV) are called endogenous (Green & Salkind, 2014). Exogenous variables are explained by the model and endogenous variables are not explained by the model. Path models have a combination of exogenous and endogenous variables. Total effect is the sum of both direct and indirect effects. Path analysis is use to show the linear relationship between the variables based on the researchers’ hypothesized idea. Path coefficients represent the magnitude of the direct effect of one variable on another. Path coefficients are standardized because it predicts one variable from another and they come from multiple regressions. Unmeasured variables on endogenous variables are the residual path coefficients. The path models can be recursive or non-recursive. In a recursive model, all of the effects are unidirectional, and it always fit the observed data perfectly. A recursive model does not have reciprocal causation among variables or loops. A model is non-recursive if one or more of the links were

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