Year: 2019
Type: preprint
Abstract: This tutorial demonstrates the estimation and interpretation of the Multilevel Social Relations Model for dyadic data. The Social Relations Model is appropriate for data structures in which individuals appear multiple times as both the source and recipient of dyadic outcomes. Estimated using Stat-JR statistical software, the models are fitted to multiple outcome types: continuous, count, and binary outcomes. In addition, models are demonstrated for dyadic data from a single group and from multiple groups. The modeling approaches are illustrated via a series of case studies, and the data and software to replicate these analyses are available as supplemental files.
Source: arXiv (Cornell University)
Institution: University of Cincinnati
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2966058153