Contents
What is a cross-classified model?
In cross-classified data, lower level units do not belong to one and only one higher. level unit. Rather, lower level units belong to pairs or combinations of higher level. units formed by crossing two or more higher level classifications with one another.
What is cross-classified multilevel model?
Cross-classified multilevel modelling is an extension of standard multilevel modelling for non-hierarchical data that have cross-classified structures. With hierarchical data structures, there is an exact nesting of each lower level unit in one and only one higher level unit.
What is cross classification data?
Cross classification usually means that the data points are classified according to multiple criteria (usually coded as factors, that is, categorical variables) at the same time, giving rise to a contingency table.
How are crossed random effects used in multilevel models?
The fact that you have level 1 and 2 indicates the random effects are nested. For example: students nested within teachers because each student has only one teacher. Crossed random effects means that your random factors themselves are crossed, not nested. So not each student having one teacher.
Which is an example of a nested and crossed effect?
Nested and crossed effects. A categorical variable, say L2, is said to be nested with another categorical variable, say, L3, if each level of L2 occurs only within a single level of L3. variables are crossed if the levels of of one random variable, say R1, occur within multiple levels of a second random variable, say R2.
Is there a third level of crossed random factors?
A third level is possible as well. This would happen if each doctor sees all their patients at one of four hospitals or each field has only one of 5 species. In one kind of 2-level model, there is not one random factor at Level 2, but two crossed factors. Each observation at Level 1 is nested in the combination of these two random factors.
Are there two random factors at Level 2?
In one kind of 2-level model, there is not one random factor at Level 2, but two crossed factors. Each observation at Level 1 is nested in the combination of these two random factors. These models need to be specified correctly to capture the effects of both random factors at Level 2.