Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.
Explanatory Item Response Models for Dyadic Data from Multiple Groups
