A dynamic social relations model for clustered longitudinal dyadic data with continuous or ordinal responses

Pillinger, Rebecca, Steele, Fiona ORCID: 0000-0001-6417-7444, Leckie, George and Jenkins, Jennifer (2024) A dynamic social relations model for clustered longitudinal dyadic data with continuous or ordinal responses. Journal of the Royal Statistical Society. Series A: Statistics in Society, 187 (2). 338 - 357. ISSN 0964-1998

Text (Steele_dynamic-social-relations-model--published) - Published Version Available under License Creative Commons Attribution. Download (483kB)

• Author
• Scopus publication

Abstract

Social relations models allow the identification of cluster, actor, partner, and relationship effects when analysing clustered dyadic data on interactions between individuals or other units of analysis. We propose an extension of this model which handles longitudinal data and incorporates dynamic structure, where the response may be continuous, binary, or ordinal. This allows the disentangling of the relationship effects from temporal fluctuation and measurement error and the investigation of whether individuals respond to their partner’s behaviour at the previous observation. We motivate and illustrate the model with an application to Canadian data on pairs of individuals within families observed working together on a conflict discussion task.

Item Type:	Article
Official URL:	https://academic.oup.com/jrsssa
Additional Information:	© 2023 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	15 Aug 2023 15:51
Last Modified:	15 Nov 2024 01:27
URI:	http://eprints.lse.ac.uk/id/eprint/119988

Actions (login required)

View Item