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

Rebecca Pillinger, Fiona Steele, George Leckie and Jennifer Jenkins

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: autoregressive model; cross-lagged effects; dynamic panel model; dyadic data; round-robin data; Methods for the Analysis of Longitudinal Dyadic Data with an Application to Inter-generational Exchanges of Family Support' (ref. ES/P000118/1). (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2024-04-01
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Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, 1, April, 2024, 187(2), pp. 338 - 357. ISSN: 0964-1998

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:119988

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