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Regression Analysis versus Structural Equation Modeling (SEM):  What are the consequences if regression analysis is used instead of SEM when the variables are latent?

 

Regression analysis and Structural Equation Modeling (SEM) are both statistical techniques used to analyze relationships between variables. However, they differ in the types of variables they can analyze.

Regression analysis is suitable for analyzing relationships between observed variables, while SEM is appropriate for analyzing relationships between both observed and latent variables.

If regression analysis is used instead of SEM when the variables are latent, the consequences can be significant. Some of the possible consequences include:

1.  Misspecification of the model: Regression analysis assumes that all variables are observed, which means that latent variables are not accounted for. This can result in misspecification of the model, leading to biased and unreliable results.

2.  Failure to account for measurement error: Latent variables are often measured indirectly through observed indicators. If regression analysis is used, measurement error in the indicators may not be accounted for, leading to biased estimates of the relationships between variables.

3.  Inability to estimate indirect effects: SEM allows for the estimation of indirect effects, which are effects that operate through intermediate variables. Regression analysis cannot estimate indirect effects, leading to incomplete understanding of the relationships between variables.

4.  Failure to account for complex relationships: SEM can model complex relationships between variables, such as mediating and moderating effects. Regression analysis cannot account for these complex relationships, leading to a simplistic understanding of the relationships between variables.

In summary, the consequences of using regression analysis instead of SEM when the variables are latent can include misspecification of the model, failure to account for measurement error, inability to estimate indirect effects, and failure to account for complex relationships. It is important to choose the appropriate statistical technique based on the type of variables being analyzed.

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