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Assessment of multiple membership multilevel models: an application to interviewer effects on nonresponse

Assessment of multiple membership multilevel models: an application to interviewer effects on nonresponse
Assessment of multiple membership multilevel models: an application to interviewer effects on nonresponse
Multilevel multiple membership models account for situations where lower level units are nested within multiple higher-level units from the same classification. Not accounting correctly for such multiple membership structures leads to biased results. The use of a multiple membership model requires selection of weights reflecting the hypothesized contribution of each level two unit and their relationship to the level one outcome. The Deviance Information Criterion (DIC) has been proposed to identify such weights. For the case of logistic regression, this study assesses, through simulation, the model identification rates of the DIC to detect the correct multiple membership weights, and the properties of model variance estimators for different weight specifications across a range of scenarios. The study is motivated by analyzing interviewer effects across waves in a longitudinal study. Interviewers can substantially influence the behavior of sample survey respondents, including their decision to participate in the survey. In the case of a longitudinal survey several interviewers may contact sample members to participate across different waves. Multilevel multiple membership models are suitable to account for the inclusion of higher-level random effects for interviewers at various waves, and to assess, for example, the relative importance of previous and current wave interviewers on current wave nonresponse. To illustrate the application, multiple membership models are applied to the UK Family and Children Survey to identify interviewer effects in a longitudinal study. The paper takes a critical view on the substantive interpretation of the model weights and provides practical guidance to statistical modelers. The main recommendation is that it is best to specify the weights in a multiple membership model by exploring different weight specifications based on the DIC, rather than prespecifying the weights.
deviance information criterion, interviewer effects, multilevel multiple membership models, survey nonresponse
1-17
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Vassallo, Rebecca
655a8946-fd08-41b2-8c05-ea07172cb965
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Vassallo, Rebecca
655a8946-fd08-41b2-8c05-ea07172cb965
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Durrant, Gabriele B., Vassallo, Rebecca and Smith, Peter W.F. (2018) Assessment of multiple membership multilevel models: an application to interviewer effects on nonresponse. Multivariate Behavioral Research, 1-17. (doi:10.1080/00273171.2018.1465809).

Record type: Article

Abstract

Multilevel multiple membership models account for situations where lower level units are nested within multiple higher-level units from the same classification. Not accounting correctly for such multiple membership structures leads to biased results. The use of a multiple membership model requires selection of weights reflecting the hypothesized contribution of each level two unit and their relationship to the level one outcome. The Deviance Information Criterion (DIC) has been proposed to identify such weights. For the case of logistic regression, this study assesses, through simulation, the model identification rates of the DIC to detect the correct multiple membership weights, and the properties of model variance estimators for different weight specifications across a range of scenarios. The study is motivated by analyzing interviewer effects across waves in a longitudinal study. Interviewers can substantially influence the behavior of sample survey respondents, including their decision to participate in the survey. In the case of a longitudinal survey several interviewers may contact sample members to participate across different waves. Multilevel multiple membership models are suitable to account for the inclusion of higher-level random effects for interviewers at various waves, and to assess, for example, the relative importance of previous and current wave interviewers on current wave nonresponse. To illustrate the application, multiple membership models are applied to the UK Family and Children Survey to identify interviewer effects in a longitudinal study. The paper takes a critical view on the substantive interpretation of the model weights and provides practical guidance to statistical modelers. The main recommendation is that it is best to specify the weights in a multiple membership model by exploring different weight specifications based on the DIC, rather than prespecifying the weights.

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More information

Accepted/In Press date: 8 April 2018
e-pub ahead of print date: 17 May 2018
Published date: 2018
Keywords: deviance information criterion, interviewer effects, multilevel multiple membership models, survey nonresponse

Identifiers

Local EPrints ID: 420181
URI: http://eprints.soton.ac.uk/id/eprint/420181
PURE UUID: 9ce95c9d-81e5-486c-85f0-97896dc8d5d0
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 01 May 2018 16:30
Last modified: 07 Jan 2020 05:01

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