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March 2019 Modeling within-household associations in household panel studies
Fiona Steele, Paul S. Clarke, Jouni Kuha
Ann. Appl. Stat. 13(1): 367-392 (March 2019). DOI: 10.1214/18-AOAS1189

Abstract

Household panel data provide valuable information about the extent of similarity in coresidents’ attitudes and behaviours. However, existing analysis approaches do not allow for the complex association structures that arise due to changes in household composition over time. We propose a flexible marginal modeling approach where the changing correlation structure between individuals is modeled directly and the parameters estimated using second-order generalized estimating equations (GEE2). A key component of our correlation model specification is the “superhousehold”, a form of social network in which pairs of observations from different individuals are connected (directly or indirectly) by coresidence. These superhouseholds partition observations into clusters with nonstandard and highly variable correlation structures. We thus conduct a simulation study to evaluate the accuracy and stability of GEE2 for these models. Our approach is then applied in an analysis of individuals’ attitudes towards gender roles using British Household Panel Survey data. We find strong evidence of between-individual correlation before, during and after coresidence, with large differences among spouses, parent–child, other family, and unrelated pairs. Our results suggest that these dependencies are due to a combination of nonrandom sorting and causal effects of coresidence.

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Fiona Steele. Paul S. Clarke. Jouni Kuha. "Modeling within-household associations in household panel studies." Ann. Appl. Stat. 13 (1) 367 - 392, March 2019. https://doi.org/10.1214/18-AOAS1189

Information

Received: 1 September 2017; Revised: 1 April 2018; Published: March 2019
First available in Project Euclid: 10 April 2019

zbMATH: 07057432
MathSciNet: MR3937433
Digital Object Identifier: 10.1214/18-AOAS1189

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 1 • March 2019
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