2. 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.
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3. 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.
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SEM…
4. 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.
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What is different about SEM?
5. 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.
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Why and when to use SEM?
6. 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).
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Latent Constructs and Abbreviations
7. 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.
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Assumptions
8. 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 y2
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is 11 times 21
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