N=1 versus N>1 analyses
This is the landing page for topics related to analyzing single cases (N=1) versus modeling multiple cases at once (N>1).
Using N=1 analyses allows you to tailor a model to an individual case, which can provide you with a detailed examination of that individual’s process. Using N>1 analyses may allow you to learn about a population of cases, as well as about individual cases. However, such analyses require careful thought about if and how those individual cases differ from each other, and how to account for this in your analyses. If this is not done carefully, such analyses might end up suffering from the [within/between problem], and result in the ecological fallacy.
Below we have specified multiple articles where you can read more about single or multiple case modeling.
Think more about N=1 versus N>1
Modeling repeated measures of a single case.
- [Time series analysis]
Bottom-up analyses start by modeling single single cases and continue by finding similarities among the cases, and then obtaining group-level results.
- [Cluster analyses]
- [GIMME]
Top-down analyses start with a model that specifies how cases are related, and given these constraints obtain results for single cases.
- [Multilevel modeling]
- [Dynamic structural equation modeling]