Open Access
June 2023 Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support
Jouni Kuha, Siliang Zhang, Fiona Steele
Author Affiliations +
Ann. Appl. Stat. 17(2): 1521-1542 (June 2023). DOI: 10.1214/22-AOAS1680


Understanding the help and support that is exchanged between family members of different generations is of increasing importance, with research questions in sociology and social policy focusing on both predictors of the levels of help given and received, and on reciprocity between them. We propose general latent variable models for analysing such data, when helping tendencies in each direction are measured by multiple binary indicators of specific types of help. The model combines two continuous latent variables, which represent the helping tendencies, with two binary latent class variables which allow for high proportions of responses where no help of any kind is given or received. This defines a multivariate version of a zero-inflation model. The main part of the models is estimated using MCMC methods, with a bespoke data augmentation algorithm. We apply the models to analyse exchanges of help between adult individuals and their noncoresident parents, using survey data from the UK Household Longitudinal Study.

Funding Statement

This research was supported by a grant for the project “Methods for the Analysis of Longitudinal Dyadic Data with an Application to Inter-generational Exchanges of Family Support” cofunded by the UK Economic and Social Research Council (ESRC) and Engineering and Physical Sciences Research Council (EPSRC), ref. ES/P000118/1.
Additional funding for Siliang Zhang was provided by Shanghai Science and Technology Committee Rising-Star Program (22YF1411100).


Understanding Society (UKHLS) is an initiative funded by the Economic and Social Research Council and various government departments, with scientific leadership by the Institute for Social and Economic Research, University of Essex, and survey delivery by NatCen Social Research and Kantar Public. The research data are distributed by the UK Data Service.


Download Citation

Jouni Kuha. Siliang Zhang. Fiona Steele. "Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support." Ann. Appl. Stat. 17 (2) 1521 - 1542, June 2023.


Received: 1 June 2021; Revised: 1 May 2022; Published: June 2023
First available in Project Euclid: 1 May 2023

MathSciNet: MR4582723
zbMATH: 07692393
Digital Object Identifier: 10.1214/22-AOAS1680

Keywords: Item response theory models , latent class analysis , Mixture models , nonequivalence of measurement , two-step estimation

Rights: Copyright © 2023 Institute of Mathematical Statistics

Vol.17 • No. 2 • June 2023
Back to Top