Multilevel modeling in egocentric network analysis: A practical guide with SAS and R

Area of Science:

• Social Network Analysis
• Statistical Modeling
• Health Sciences Research

Background:

• Egocentric network data possess a nested structure (alters within egos) and intra-ego dependencies.
• Traditional regression models assume independence, which is violated by egocentric network data.
• Accurate analysis requires methods that account for hierarchical data structures.

Purpose of the Study:

• To illustrate the application of multilevel modeling for egocentric network data.
• To address the violation of independence assumptions in traditional regression models.
• To provide a practical guide for researchers in social and health sciences.

Main Methods:

• Utilizing multilevel modeling to accommodate hierarchical data structures and intra-ego dependencies.
• Distinguishing effects at alter, ego, and dyadic levels on outcome variables.
• Describing model specifications including random intercepts, slopes, cross-level interactions, and residual structures.

Main Results:

• Demonstrated estimation of fixed and random effects for continuous and binary outcomes.
• Provided methods for assessing intraclass correlation and testing cross-level interactions.
• Offered step-by-step implementation guidance using SAS and R.

Conclusions:

• Multilevel modeling offers a rigorous approach for analyzing egocentric network data.
• This methodology enables deeper insights into individual, relational, and structural influences on health.
• The guide supports researchers in applying advanced statistical techniques to network data.