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
A) Predict future outcomes B) Study causal relationships between variables C) Assess reliability and validity of measurement instruments D) Analyze non-linear relationships
A) T-test B) ANOVA C) Chi-square test D) Pearson correlation
A) Repeatability of the measurement B) Strength of relationship between indicator and factor C) Magnitude of measurement error D) Effect size of moderation
A) Eliminate measurement biases B) Enhance model interpretability C) Reduce model complexity D) Account for unexplained variance in observed variables
A) Measurement paths B) Factor paths C) Structural paths D) Error paths
A) Calculate total effect size B) Estimate model complexity C) Identify potential areas of improvement in the model fit D) Determine statistical power
A) Ease of handling missing data B) Complexity in model specification and interpretation C) Limited to linear relationships D) Fast computation times
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
A) Calculates the effect sizes B) Indicates model convergence C) Contains information about the relationships between observed variables D) Used for weight initialization
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
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
A) LISREL B) Minitab C) Excel D) SPSS |