Allow complex relationships between one or more

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allow complex relationships between one or more independent variables and one or more dependent variables. Though there are many ways to describe SEM, it is most commonly thought of as a hybrid between some form of analysis of variance (ANOVA)/regression and some form of factor analysis. In general, it can be remarked that SEM allows one to perform some type of multilevel regression/ANOVA on factors. You should therefore be quite familiar with univariate and multivariate regression/ANOVA as well as the basics of factor analysis to implement SEM for your data. Some preliminary terminology will also be useful. The following definitions regarding the types of variables that occur in SEM allow for a more clear explanation of the procedure:
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234 1) Variables that are not influenced by another other variables in a model are called exogenous variables. As an example, suppose we have two factors that cause changes in GPA, hours studying per week and IQ. Suppose there is no causal relationship between hours studying and IQ. Then both IQ and hours studying would be exogenous variables in the model. 2) Variables that are influenced by other variables in a model are called endogenous variables. GPA would be a endogenous variable in the previous example in (a). 3) A variable that is directly observed and measured is called a manifest variable (it is also called an indicator variable in some circles). In the example in (a), all variables can be directly observed and thus qualify as manifest variables. There is a special name for a structural equation model which examines only manifest variables, called path analysis. 4) A variable that is not directly measured is a latent variable. The “factors” in a factor analysis are latent variables. For example, suppose we were additionally interested in the impact of motivation on GPA. Motivation, as it is an internal, non- observable state, is indirectly assessed by a student’s response on a questionnaire, and thus it is a latent variable. Latent variables increase the complexity of a structural equation model because one needs to take into account all of the questionnaire items and measured responses that are used to quantify the “factor” or latent variable. In this instance, each item on the questionnaire would be a single variable that would either be significantly or insignificantly involved in a linear combination of variables that influence the variation in the latent factor of motivation 5) For the purposes of SEM, specifically, moderation refers to a situation that includes three or more variables, such that the presence of one of those variables changes the relationship between the other two. In other words, moderation exists when the association between two variables is not the same at all levels of a third variable. One way to think of moderation is when you observe an interaction between two variables in an ANOVA. For example, stress and psychological adjustment may differ at different levels of social support (i.e., this is the definition of an interaction). In other words, stress may
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