A cross-level interaction is just an interaction where one of the predictors is restricted in its variability to units at level 2. If the model makes sense then go ahead. It is however not uncommon for a random slope to be an equally good explanation (in terms of model fit) to a model with the cross-level interaction.

Why you should always include a random slope for the lower level variable involved in a cross-level interaction?

Introducing a random slope term on the lower-level variable involved in a cross-level interaction, reduces the absolute t-ratio by 31% or more in three quarters of cases, with an average reduction of 42%. Many practitioners seem to be unaware of these issues.

What is a two way interaction?

in a two-way analysis of variance, the joint effect of both independent variables, a and b, on a dependent variable.

What is multilevel modeling in statistics?

Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level.

What is cross level?

: to level (as a surveyor’s transit) at right angles to the principal line of sight.

What is random slope?

The random slopes model Well, unlike a random intercept model, a random slope model allows each group line to have a different slope and that means that the random slope model allows the explanatory variable to have a different effect for each group.

What are the five types of interaction?

There are five types of interactions between different species as listed below:

• Competition & Predation.
• Commensalism.
• Parasitism.
• Mutualism.
• Amensalism.

  Do I need a multilevel model?

  When the structure of your data is naturally hierarchical or nested, multilevel modeling is a good candidate. More generally, it’s one method to model interactions. A natural example is when your data is from an organized structure such as country, state, districts, where you want to examine effects at those levels.

  Why do we use multilevel modeling?

  Multilevel models recognise the existence of such data hierarchies by allowing for residual components at each level in the hierarchy. Multilevel models can also be fitted to non-hierarchical structures. For instance, children might be nested within a cross-classification of neighbourhoods of residence and schools.