I'm still struggling with my understanding of multilevel analysis, wondering if it applies or not to my problem. I'v read here the following (where author gives an example of a multilevel model with classrooms):
Classrooms pertain to a level (rather than a predictor variable), since (a) classrooms were randomly sampled from a population of units (classrooms around the world are potentially infinite and you have sampled some of them), and (b) classrooms have no intrinsic meaning per se (classrooms are interchangeable units without theoretical content).
Next, author says the following:
On the contrary, socioeconomic status would for instance pertain to a predictor variable (rather than a level) since its categories are both non-random and theoretically meaningful (e.g. lower, middle, and upper class are not “atheoretical” random units).
So far so good. Where I'm confused is when the author adds:
With (...the classroom as) a data structure, you cannot run a standard logistic regression analysis. The reason is that this violates one of the most important assumptions in the linear model, namely the assumption of independence (or lack of correlation) of the residuals (...). Observations are interdependent: Participants nested in the same cluster are more likely to function in the same way than participants nested in different clusters.
Isn't the same for observations in the different socioeconomic groups? Aren't the observations also interdependent of each other in a specific socioeconomic group? If yes, does it mean that we also should treat socioeconomic status in a multilevel setting?
EDIT: to clarify about comment below, suppose you want to do a linear regression based on socioeconomic status. Is there a fit for multilevel analysis. I would be inclined to say no since the categories are not random. On the other side, observations in one category could be interdependent. What can I do?