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WorksheetsStructural Equation Modeling Using AMOS
Total questions: 10
Worksheet time: 5mins
AMOS allows for model modification indices (MMI), which are used to
Assess the overall fit of the model
Identify specific areas of the model that can be improved
Calculate standardized path coefficients
Test for measurement invariance
The concept of "exogenous" variables in SEM refers to variables that are:
Measured using multiple indicators
Caused by other variables in the model
Predicted by other variables in the model
Not influenced by any other variables in the model
When estimating a model in AMOS, the Maximum Likelihood (ML) estimation method is commonly used for:
Categorical data
Ordinal data
Continuous data
Missing data
In SEM, a mediation effect occurs when:
Two variables have a direct relationship
A third variable explains the relationship between two other variables
The model does not fit the data well
The path coefficients are equal to zero
Which of the following fit indices in AMOS indicates a good fit between the model and the data?
Chi-square test
RMSEA (Root Mean Square Error of Approximation)
p-value
Standardized residuals
The term "factor loading" in SEM refers to:
The correlation between latent variables
The regression coefficient in a path model
The relationship between an observed variable and its latent variable
The standardized effect size
A measurement model in SEM is used to:
Determine the relationships between latent variables
Specify the indicators for each latent variable
Test for mediation effects
Calculate goodness-of-fit indices
The purpose of using AMOS in SEM is to: a. Estimate path coefficients b. c. d.
Estimate path coefficients
Perform confirmatory factor analysis
Assess model fit
All of the above
In SEM, the latent variables are also known as:
Independent variables
Dependent variables
Observed variables
Manifest variables
Structural equation modeling is a statistical technique used to:
Estimate population parameters
Test causal relationships between variables
Calculate effect sizes
Perform factor analysis
