Structural equation model

1. A structural equation model (SEM) is a statistical technique used to test and validate complex relationships between variables. It is a powerful tool commonly used in social sciences, psychology, economics, and other fields to study causal relationships among factors. SEM allows researchers to model both observed and unobserved variables, known as latent variables, and to quantify the direct and indirect effects of one variable on another. By specifying multiple interrelated equations, SEM helps researchers understand the underlying mechanisms and pathways through which variables influence each other. This method provides valuable insights into complex systems and can help inform theoretical models, make predictions, and guide decision-making in various research domains.

In SEM, what does the term 'exogenous variable' refer to?

A) Variable not predicted by other variables in the model
B) Variable affected by measurement errors
C) Variable with direct causal effect
D) Variable with indirect effect only

2. What is the purpose of confirmatory factor analysis in SEM?

A) Study causal relationships between variables
B) Analyze non-linear relationships
C) Predict future outcomes
D) Assess reliability and validity of measurement instruments

3. Which statistical analysis is commonly used to evaluate the goodness-of-fit of an SEM model?

A) ANOVA
B) Chi-square test
C) Pearson correlation
D) T-test

4. What does the 'loading' of an indicator on a factor represent in SEM?

A) Magnitude of measurement error
B) Strength of relationship between indicator and factor
C) Repeatability of the measurement
D) Effect size of moderation

5. What is the purpose of specifying error terms in SEM?

A) Reduce model complexity
B) Eliminate measurement biases
C) Account for unexplained variance in observed variables
D) Enhance model interpretability

6. In SEM, what is the general term for paths that indicate direct causal relationships between variables?

A) Structural paths
B) Error paths
C) Factor paths
D) Measurement paths

7. What is 'modification index' used for in SEM analyses?

A) Calculate total effect size
B) Identify potential areas of improvement in the model fit
C) Determine statistical power
D) Estimate model complexity

8. Which of the following is a disadvantage of SEM?

A) Fast computation times
B) Limited to linear relationships
C) Complexity in model specification and interpretation
D) Ease of handling missing data

9. What does 'recursive' modeling imply in SEM?

A) Variables are arranged in a series of causal relationships without feedback loops
B) All variables influence each other directly
C) No relationships between variables are assumed
D) Presence of non-linear paths only

10. What is the role of 'covariance matrix' in SEM model estimation?

A) Used for weight initialization
B) Indicates model convergence
C) Calculates the effect sizes
D) Contains information about the relationships between observed variables

11. What does the term 'endogeneity' refer to in SEM?

A) Measurement error accumulation
B) Non-normal residual distribution
C) Model overfitting
D) When an independent variable is correlated with the error term of another variable

12. What does 'model identification' in SEM refer to?

A) Optimization algorithm selection
B) Ensuring the unique estimation of model parameters with the given data
C) Interpretation of fit indices
D) Parameter estimation process

13. What software is commonly used for SEM analysis?

A) LISREL
B) SPSS
C) Excel
D) Minitab

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