Blackwell Reference Online

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Structural equation modeling (SEM) is a statistical technique that allows tests of complex relationships between large numbers of variables. The term “structural equation modeling” is closely related to terms like covariance structure analysis, causal modeling, or path analysis. According to Kline (2005) , the main characteristics of SEM are as follows. First of all, there has to be a model that shows how variables are related; it has to be specified prior to statistical analysis. The idea of the model may come from theory, from prior research, or from researchers’ specified knowledge and experience. The procedure is mainly confirmatory, and generally researchers decide which are dependent and independent variables and how they are linked by causal relations. When specifying a model one must distinguish between observed and latent variables, or at least explicate at which level a variable is located. Most standard procedures do not allow differentiation between observed and latent variables. As an example, if you want to measure how political orientation influences newspaper usage , you have two abstract constructs that cannot be measured directly. These constructs – or latent variables – have to be operationalized through indicators, or observed variables. So political orientation may be measured through statements like “I want to know what is going on in the world” or “I want ... log in or subscribe to read full text