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Quantitative Research - Using Structural Equation Modeling

Cross-Sectional Statistical Modeling


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If it seems like a long way to go, just take it one step at a time.

Courtesy Petr Kovar, Photographer. © August 5, 2007 Stock.xchng

When Would I Choose SEM?

Structural Equation Modeling (SEM)is quantitative research technique. SEM is used to show the causal relationships between variables. The relationships shown in SEM represent the hypotheses of the researchers. Typically, these relationships can't be statistically tested for directionality . Most often, SEM is used for research that is designed for confirmation rather than to explore or explain a phenomenon. SEM produces data in a visual display -- and this is part of its appeal. When using SEM, the researcher gets a tidy visual display that is easy to interpret, even if the statistics behind the data are quite complex.

What Is Meant by Cross-Sectional Variation?

SEM is designed to look at complex relationships between variables, and to reduce the relationships to visual representations. The structural and measurement relationships that are implied by hypotheses in a research design can be modeled using SEM. The relationships that are displayed in SEM modeling are determined by data arranged in a matrix. SEM uses cross-sectional variation to do the modeling that yields the conclusions. By cross-sectional variation, we mean variation across the respondents who are part of a research study.

Where Did the Idea of Path Analysis Come From?

SEM is a cross-sectional statistical modeling technique that has its origins in econometric analysis. SEM is a combination of factor analysis and multiple regression. The terms factor and variable refer to the same concept in statistics. Path Analysis is a variation of SEM, which is essentially a type of multivariate procedure that allows an examination of independent variables and dependent variables. Variables can be continuous or discrete. SEM works with measured variables and latent variables. Path Analysis uses measured values only. Measured variables can be observed and are measurable. Latent variables cannot be observed directly, but their values can be implied by their relationships to observed variables. Two or more measured variables are necessary to determine a value for a latent variable.

What is the Difference Between Measurement and Structural Models?

SEM has two basic parts: A measurement model and a structural model. The relationships between the variables (both measured and latent) are shown in the measurement model. Only the relationships between the latent variables are shown in the structural model. One important benefit of using latent variables is that they are free of random error. The error associated with the latent variables is statistically estimated and removed in the SEM analysis. Only a common variance remains. Tidy.

How Is a SEM Analysis Performed?

A SEM is constructed through five discrete steps. They are as follows:

When first learning about Structured Equation Modeling, it is helpful to consider each of these steps individually. Not independently, but just one at a time.

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