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Separating interviewer and area effects using a cross-classified multilevel logistic model: implications for survey designs

Separating interviewer and area effects using a cross-classified multilevel logistic model: implications for survey designs
Separating interviewer and area effects using a cross-classified multilevel logistic model: implications for survey designs
Cross-classified multilevel models deal with data pertaining to two different non-hierarchical classifications. It is unclear how much interpenetration is needed for a cross-classified multilevel model to work well and to reliably estimate the two higher-level effects. The paper investigates this question and the properties of cross-classified multilevel logistic models under various survey conditions. The effects of different membership allocation schemes, total sample sizes, group sizes, number of groups, overall rates of response, and the variance partitioning coefficient on the properties of the estimators and the power of the Wald test are considered. The work is motivated by an application to separate area and interviewer effects on survey nonresponse which are often confounded. The results indicate that limited interviewer dispersion (around 3 areas per interviewer) provides sufficient interpenetration for good estimator properties. Further dispersion yields only very small or negligible gains in the properties. Interviewer dispersion also acts as a moderating factor on the effect of the other simulation factors (sample size, the ratio of interviewers to areas, the overall probability, and the variance values) on the properties of the estimators and test statistics. The results also indicate that a higher number of interviewers for a set number of areas and a set total sample size improves these properties.
cross-classification, interpenetration, interviewer effects, area effects, multilevel models
0964-1998
1-32
Vassallo, Rebecca
8751b529-b4b7-4cbd-a040-dfe42d1cbd91
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Vassallo, Rebecca
8751b529-b4b7-4cbd-a040-dfe42d1cbd91
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Vassallo, Rebecca, Durrant, Gabriele and Smith, Peter W.F. (2016) Separating interviewer and area effects using a cross-classified multilevel logistic model: implications for survey designs. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1-32. (doi:10.1111/rssa.12206).

Record type: Article

Abstract

Cross-classified multilevel models deal with data pertaining to two different non-hierarchical classifications. It is unclear how much interpenetration is needed for a cross-classified multilevel model to work well and to reliably estimate the two higher-level effects. The paper investigates this question and the properties of cross-classified multilevel logistic models under various survey conditions. The effects of different membership allocation schemes, total sample sizes, group sizes, number of groups, overall rates of response, and the variance partitioning coefficient on the properties of the estimators and the power of the Wald test are considered. The work is motivated by an application to separate area and interviewer effects on survey nonresponse which are often confounded. The results indicate that limited interviewer dispersion (around 3 areas per interviewer) provides sufficient interpenetration for good estimator properties. Further dispersion yields only very small or negligible gains in the properties. Interviewer dispersion also acts as a moderating factor on the effect of the other simulation factors (sample size, the ratio of interviewers to areas, the overall probability, and the variance values) on the properties of the estimators and test statistics. The results also indicate that a higher number of interviewers for a set number of areas and a set total sample size improves these properties.

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More information

Accepted/In Press date: 12 April 2016
e-pub ahead of print date: 8 June 2016
Published date: 8 June 2016
Keywords: cross-classification, interpenetration, interviewer effects, area effects, multilevel models
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 382014
URI: https://eprints.soton.ac.uk/id/eprint/382014
ISSN: 0964-1998
PURE UUID: aa65a578-8b8a-4e1f-9194-3e5a463c4fde
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 05 Oct 2015 15:25
Last modified: 15 Aug 2019 05:36

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