  Many complex systems often contain interactions between more than two nodes,
known as higher-order interactions, which can change the structure of these
systems in significant ways. Researchers often assume that all interactions
paint a consistent picture of a higher-order dataset's structure. In contrast,
the connection patterns of individuals or entities in empirical systems are
often stratified by interaction size. Ignoring this fact can aggregate
connection patterns that exist only at certain scales of interaction. To
isolate these scale-dependent patterns, we present an approach for analyzing
higher-order datasets by filtering interactions by their size. We apply this
framework to several empirical datasets from three domains to demonstrate that
data practitioners can gain valuable information from this approach.
