A log-linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships

R. Admiraal; M.S. Handcock

doi:10.1111/rssa.12067

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A log-linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships

Journal article

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Peer reviewed

A log-linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships

R. Admiraal and M.S. Handcock

Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol.178(2), pp.363-382

2015

DOI: https://doi.org/10.1111/rssa.12067

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Abstract

We propose a log-linear model to assess the consistency of ego reports of dyadic outcomes. We do so specifically in the context where males and females report on shared events, and we demonstrate how inconsistencies can be assessed by using a log-linear model that estimates separate mixing totals for each set of reports. This modelling approach immediately allows us to determine where inconsistencies in reports occur. To demonstrate how our method can be easily implemented for survey data, we apply it to both the 1992 National Health and Social Life Survey and the 2002 National Survey of Family Growth. Our analysis identifies inconsistencies in male and female reports of concurrent partnerships and the number of biological children.

Details

Title: A log-linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships
Authors/Creators: R. Admiraal (Author/Creator) - Murdoch University
M.S. Handcock (Author/Creator) - Zhejiang University
Publication Details: Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol.178(2), pp.363-382
Publisher: Blackwell Publishing
Identifiers: 991005545295807891
Murdoch Affiliation: School of Engineering and Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.66 HIV; 1.66.11 HIV/AIDS Prevention
Web Of Science research areas: Social Sciences, Mathematical Methods; Statistics & Probability
ESI research areas: Social Sciences, general