A) Variable not predicted by other variables in the model B) Variable with indirect effect only C) Variable with direct causal effect D) Variable affected by measurement errors
A) Assess reliability and validity of measurement instruments B) Analyze non-linear relationships C) Predict future outcomes D) Study causal relationships between variables
A) Chi-square test B) Pearson correlation C) ANOVA D) T-test
A) Magnitude of measurement error B) Strength of relationship between indicator and factor C) Repeatability of the measurement D) Effect size of moderation
A) Eliminate measurement biases B) Account for unexplained variance in observed variables C) Enhance model interpretability D) Reduce model complexity
A) Measurement paths B) Error paths C) Factor paths D) Structural paths
A) Estimate model complexity B) Calculate total effect size C) Determine statistical power D) Identify potential areas of improvement in the model fit
A) Limited to linear relationships B) Ease of handling missing data C) Fast computation times D) Complexity in model specification and interpretation
A) Presence of non-linear paths only B) All variables influence each other directly C) No relationships between variables are assumed D) Variables are arranged in a series of causal relationships without feedback loops
A) Contains information about the relationships between observed variables B) Indicates model convergence C) Calculates the effect sizes D) Used for weight initialization
A) Model overfitting B) Non-normal residual distribution C) When an independent variable is correlated with the error term of another variable D) Measurement error accumulation
A) Ensuring the unique estimation of model parameters with the given data B) Interpretation of fit indices C) Optimization algorithm selection D) Parameter estimation process
A) SPSS B) LISREL C) Excel D) Minitab |