Structural Equation Modeling(SEM) Presented by: SABA KHAN ID 4640 Menu
What is SEM? Structural Equations Modeling is a family of statistical models that seek to explain the relationships among multiple variables. It examines the “structure” of interrelationships expressed in a series of equations, similar to a series of multiple regression equations. These equations depict all of the relationships among constructs (the dependent and independent variables) involved in the analysis. Constructs are unobservable or latent factors that are represented by multiple variables.2
Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables each of which is predicted to tap into the latent variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which in theory allows the structural relations between latent variables to be accurately estimated. Factor analysis and regression all represent special cases of SEM.3 SEM…
Its a graphical method with underlying equation execution. Estimation of Multiple and Interrelated Relationships. Represents unobserved (latent) concepts and corrects for measurement error. Defines a model that explains an entire set of relationships.4 What is different about SEM?
SEM may be used as a more powerful alternative to multiple regression, path analysis, factor analysis, time series analysis, and analysis of covariance. Its is a confirmatory test rather then a exploratory test.5 Why and when to use SEM?
Exogenous constructs are the latent, multi-item equivalent of independent variables. They use a variate (linear combination) of measures to represent the construct, which acts as an independent variable in the model.( Such variables which does not become dependent in a equation are called exogenous) Multiple measured variables (x) represent the exogenous constructs (ξ). Endogenous constructs are the latent, multi-item equivalent to dependent variables. These constructs are theoretically determined by factors within the model. (Such variables which are dependent in equation but are independent, are called endogenous) Multiple measured variables (y) represent the endogenous constructs (η).6 Latent Constructs and Abbreviations
High Multicollinearity Linearity. Outliers Sample size should be at least 200. Normality of data and using dichotomous or ordinal variables should be avoided. Use of dichotomous variables as endogenous variable while its exogenous variables are continuous.7 Assumptions
Terms in use. path direct effect of x1 on y2 coefficients 21 x1 y2 11 21 2 exogenous variable y1 1 endogenous variables indirect effect of x1 on y28 is 11 times 21 8