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Structural equation model
Contributed by: Grainger
  • 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 with indirect effect only
B) Variable not predicted by other variables in the model
C) Variable with direct causal effect
D) Variable affected by measurement errors
  • 2. What is the purpose of confirmatory factor analysis in SEM?
A) Assess reliability and validity of measurement instruments
B) Predict future outcomes
C) Analyze non-linear relationships
D) Study causal relationships between variables
  • 3. Which statistical analysis is commonly used to evaluate the goodness-of-fit of an SEM model?
A) ANOVA
B) Chi-square test
C) T-test
D) Pearson correlation
  • 4. What does the 'loading' of an indicator on a factor represent in SEM?
A) Repeatability of the measurement
B) Magnitude of measurement error
C) Effect size of moderation
D) Strength of relationship between indicator and factor
  • 5. What is the purpose of specifying error terms in SEM?
A) Eliminate measurement biases
B) Account for unexplained variance in observed variables
C) Enhance model interpretability
D) Reduce model complexity
  • 6. In SEM, what is the general term for paths that indicate direct causal relationships between variables?
A) Error paths
B) Measurement paths
C) Structural paths
D) Factor 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) Estimate model complexity
D) Determine statistical power
  • 8. Which of the following is a disadvantage of SEM?
A) Fast computation times
B) Complexity in model specification and interpretation
C) Limited to linear relationships
D) Ease of handling missing data
  • 9. What does 'recursive' modeling imply in SEM?
A) Presence of non-linear paths only
B) No relationships between variables are assumed
C) All variables influence each other directly
D) Variables are arranged in a series of causal relationships without feedback loops
  • 10. What is the role of 'covariance matrix' in SEM model estimation?
A) Indicates model convergence
B) Calculates the effect sizes
C) Used for weight initialization
D) Contains information about the relationships between observed variables
  • 11. What does the term 'endogeneity' refer to in SEM?
A) Non-normal residual distribution
B) When an independent variable is correlated with the error term of another variable
C) Measurement error accumulation
D) Model overfitting
  • 12. What does 'model identification' in SEM refer to?
A) Parameter estimation process
B) Interpretation of fit indices
C) Optimization algorithm selection
D) Ensuring the unique estimation of model parameters with the given data
  • 13. What software is commonly used for SEM analysis?
A) Minitab
B) LISREL
C) Excel
D) SPSS
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