# Rule 2 lines with an arrow in one direction show a

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Rule 2. Lines with an arrow in one direction show a hypothesized direct relationship between the two variables. It should originate at the causal variable and point to the variable that is caused. Absence of a line indicates there is no causal relationship between the variables. Rule 3. Lines with an arrow in both directions should be curved and this demonstrates a bi-directional relationship (i.e., a covariance). Rule 3a. Covariance arrows should only be allowed for exogenous variables. Rule 4. For every endogenous variable, a residual term should be added in the model. Generally, a residual term is a circle with the letter E written in it, which stands for error.

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239 Rule 4a . For latent variables that are also endogenous, a residual term is not called error in the lingo of SEM. It is called a disturbance, and therefore the “error term” here would be a circle with a D written in it, standing for disturbance. 23.4. REVISION POINTS 1) Structural equation modelling is a series of statistical methods that allow complex relationships between one or more independent variables and one or more dependent variables. 2) A variables that is caused by one or more variable in the model. 3) A variable is not caused by another variable in the model. 4) A measure of the amount of change in the effect variable expected given a one unit change in the casual variable and no change in any other variable. 5) The set of unspecified causes of the effect variable. 6) A diagram that pictorially represents a structural model. 23.5. INTEXT QUESTIONS 1) Describe the structural equation modelling with illustration. 2) Explain the model hypothesis and model specification in structural equation modelling. 3) What are the advantages and disadvantages of structural equation modelling. 23.6. SUMMARY The structural equation model can be decomposed into the structural model and the measurement model. The structural model assesses the relationships between the latent variables. The measurement model represents the degree to which the indicator variables capture the essence of the latent factor. It is basically confirmatory factor analysis for each latent variable. We call it a measurement model because indicator variables are measured variables used to give us some access to or indication of the intangible unmeasured latent factor. The model as a whole is evaluated by a variety of goodness of fit. 23.7. TERMINAL EXERCISES 1) What is meant by structural equation model? 2) Distinguish between endogenous and exogenous variable. 3) Define latent variable. 4) What is measurement model. 5) Mention the uses of structural equation modelling. 23.8. SUPPLEMENTARY EXERCISE 1) 2) 3) 4) 23.9. ASSIGNMENTS 1) Explain the use of multiple indicators in structural equation modelling.
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• Spring '12
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