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Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis

Kuha, Jouni, Katsikatsou, Myrsini and Moustaki, Irini (2018) Latent variable modelling with non-ignorable item nonresponse: multigroup response propensity models for cross-national analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181 (4). pp. 1169-1192. ISSN 0964-1998

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Identification Number: 10.1111/rssa.12350

Abstract

When missing data are produced by a non-ignorable nonresponse mechanism, analysis of the observed data should include a model for the probabilities of responding. In this paper we propose such models for nonresponse in survey questions which are treated as measures of latent constructs and analysed using latent variable models. The nonresponse models that we describe include additional latent variables (latent response propensities) which determine the response probabilities. We argue that this model should be specified as exibly as possible, and propose models where the response propensity is a categorical variable (a latent response class). This can be combined with any latent variable model for the survey items, and an association between the latent variables measured by the items and the latent response propensities then implies a model with non-ignorable nonresponse. We consider in particular such models for the analysis of data from cross-national surveys, where the nonresponse model may also vary across the countries. The models are applied to data on welfare attitudes in 29 countries in the European Social Survey.

Item Type: Article
Official URL: http://rss.onlinelibrary.wiley.com/hub/journal/10....
Additional Information: © 2017 The Authors
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 29 Nov 2017 15:12
Last Modified: 20 Jun 2019 00:05
Projects: Methodological Innovation Projects
Funders: National Centre for Research Methods,
URI: http://eprints.lse.ac.uk/id/eprint/85875

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