SEM integrates factor analysis with path analysis and regression and focuses on testing hypotheses about relations between observed and latent variables. Relative to alternative statistical procedures, structural equation modeling has several weaknesses: It requires a relatively large sample size (N of 150 or greater).
Structural Equation Modeling offers a nontechnical presentation of SEM with an emphasis on applications in social and personality psychology. These theorized relationships are formalized into a set of equations that include the variables in question. It requires much more formal training in statistics to be able to effectively use SEM software programs. Structural Equation Models (SEM) encourages confirmatory rather than exploratory modeling; thus, it is suited to theory testing rather than theory development. According to this approach, a researcher begins with a model that specifies how multiple variables are related to each other. This course introduces Structural Equation Modeling (SEM) as a general statistical method for data analysis in the social and behavioural sciences. It usually starts with a hypothesis, represents it as a model, operationalises the constructs of interest with a measurement instrument, and tests the model. Structural equation modeling (SEM) is a particular form of data analysis.
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