N=1 versus N>1
This is the landing page for topics related to single case versus many case models.
Using N=1 models allows for a detailed examination of individual-specific processes and dynamics that may be obscured in group-level analyses. In contrast, multilevel models are valuable for understanding both within- and between-person variability, but they rely on assumptions about group-level structures that might not hold for all individuals. Choosing between them depends on whether the research goal is to generalize across individuals or to deeply understand the unique patterns within a single person. Below we have specified multiple articles where you can read more about single versus many case modeling.
Think more about N=1 versus N>1
A single case can be a single person, but it could also be a dyad or school, for example.
- [Cases]
Modeling repeated measures of a single case.
- [Time series analysis]
Analyzing repeated measures of multiple cases in a single model.
- [Multilevel modeling]
- [Dynamic structural equation modeling]
- [GIMME]