Are time points nested in students or crossed in a longitudinal multi-level model I often hear that in a longitudinal multi-level analysis, time points (as a fixed factor) are "nested" within students (e.g., just search the word $nest$ in this paper).
However, this great answer very clearly indicates that time points can't be "nested" within a cluster (e.g., a student). Because, every cluster is planned to taste every time point in theory. So time points and students are crossed.
Question: Is the use of the phrase time points are "nested" within students just sloppy language? If it is, how could we correctly describe the relation of the time points to students?
 A: 
Question: Is the use of the phrase time points are "nested" within students just sloppy language? If it is, how could we correctly describe the relation of the time points to students?

No, I don't think it's sloppy, but I can see why you think it might be and why it can be confusing.  The main point is that nesting is a generic term, but in the context of crossed or nested random effects it refers specifically to grouping variables that are being treated as random. Time, on the other hand, in longitudinal studies, is not usually a grouping variable.
In the linked question, nesting is defined as a situation where one grouping variable "belongs" to only one upper level grouping variable. That doesn't apply in longitudinal studies for two reasons: the main reason is the one I have already given above: time isn't a grouping variable. Another way to look at it is that in longitudinal mixed models there is no requirement for subjects to be measured at the same time each. Indeed, we could argue that writing down the same time for each subject is just a convenience/simplification. If subjects each get their blood pressure measured on the same days, time may be recorded the same for each, but the actual measurement took place at a slightly different time for each. In that sense each measurement is unique to each subject and then still fits with the definition of nesting above. This is a somewhat philosophical point.
Due to the potential for confusion, I like to speak of "repeated measures" rather than "nesting" for longitudinal data.
