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 with indirect effect only
B) Variable affected by measurement errors
C) Variable not predicted by other variables in the model
D) Variable with direct causal effect
  • 2. What is the purpose of confirmatory factor analysis in SEM?
A) Predict future outcomes
B) Study causal relationships between variables
C) Assess reliability and validity of measurement instruments
D) Analyze non-linear relationships
  • 3. Which statistical analysis is commonly used to evaluate the goodness-of-fit of an SEM model?
A) T-test
B) ANOVA
C) Chi-square test
D) Pearson correlation
  • 4. What does the 'loading' of an indicator on a factor represent in SEM?
A) Repeatability of the measurement
B) Strength of relationship between indicator and factor
C) Magnitude of measurement error
D) Effect size of moderation
  • 5. What is the purpose of specifying error terms in SEM?
A) Eliminate measurement biases
B) Enhance model interpretability
C) Reduce model complexity
D) Account for unexplained variance in observed variables
  • 6. In SEM, what is the general term for paths that indicate direct causal relationships between variables?
A) Measurement paths
B) Factor paths
C) Structural paths
D) Error paths
  • 7. What is 'modification index' used for in SEM analyses?
A) Calculate total effect size
B) Estimate model complexity
C) Identify potential areas of improvement in the model fit
D) Determine statistical power
  • 8. Which of the following is a disadvantage of SEM?
A) Ease of handling missing data
B) Complexity in model specification and interpretation
C) Limited to linear relationships
D) Fast computation times
  • 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) Calculates the effect sizes
B) Indicates model convergence
C) Contains information about the relationships between observed variables
D) Used for weight initialization
  • 11. What does the term 'endogeneity' refer to in SEM?
A) Model overfitting
B) Measurement error accumulation
C) Non-normal residual distribution
D) When an independent variable is correlated with the error term of another variable
  • 12. What does 'model identification' in SEM refer to?
A) Parameter estimation process
B) Optimization algorithm selection
C) Ensuring the unique estimation of model parameters with the given data
D) Interpretation of fit indices
  • 13. What software is commonly used for SEM analysis?
A) LISREL
B) Minitab
C) Excel
D) SPSS
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