This document discusses dyadic data analysis. Dyadic data refers to data that involves two related individuals, such as perceptions between two people or levels of self-disclosure between two interacting people. There are several approaches to analyzing dyadic data, including repeated measures analysis, multi-level modeling, and structural equation modeling. These approaches allow researchers to assess dependencies between individuals and account for the covariance between observations from the same dyad. The key considerations in selecting an analytic approach include whether the dyad members can be distinguished and whether they are exchangeable.