What Are the Key Assumptions of Multilevel Analysis?
Like any statistical method, multilevel analysis has several key assumptions:
1. Random Effects: The random effects are normally distributed with a mean of zero. 2. Independence: The residuals at each level are independent and identically distributed. 3. Linearity: The relationship between the predictors and the outcome is linear for continuous outcomes. 4. Homogeneity of Variance: The variance of the residuals is constant across levels.
Violations of these assumptions can lead to biased estimates and incorrect inferences. Therefore, it is crucial to check these assumptions before interpreting the results.
