
Multilevel Modeling in Plain Language

Remember that the residuals are the difference between our predicted reading score based on these characteristics and the actual reading score we see in the data.
Karen Robson • Multilevel Modeling in Plain Language
If you violate it, you get incorrect estimations of the standard errors.
Karen Robson • Multilevel Modeling in Plain Language
The assumption of independence means that cases in our data should be independent of one another,
Karen Robson • Multilevel Modeling in Plain Language
independent variables are measured without error.
Karen Robson • Multilevel Modeling in Plain Language
the sample used in the analysis is representative of its population.
Karen Robson • Multilevel Modeling in Plain Language
relationship between the independent and dependent variable is linear.
Karen Robson • Multilevel Modeling in Plain Language
Multilevel modeling is an extension of OLS.
Karen Robson • Multilevel Modeling in Plain Language
Additionally, we can look at how Level 1 predictors interact with each other and how Level 2 predictors interact with each other.
Karen Robson • Multilevel Modeling in Plain Language
Perhaps most importantly, we are also able to do cross-level interactions so that we can explain how Level 1 predictors affect our dependent variable according to different contexts (Level 2 predictors).