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