Investigating the performance of multilevel cross-classified an multiple membership logistic models: with applications to interviewer effects on nonresponse

Abstract

This thesis focuses on the modelling of interviewer effects on nonresponse using cross classified and multiple membership multilevel logistic models, and investigates the properties of such models under various survey conditions. The first paper reviews the use of cross-classified and multiple membership models to account for both interviewer and area effects and for various wave interviewers. An extension to incorporate both wave interviewer effects and area effects is presented. The mathematical details, assumptions and limitations of the models are considered. The different models conceptualised are then fitted to a dataset. This application extends the focus of the first paper from simply a methodological one to an applied study with substantive research questions. The study aims to identify interviewer characteristics that influence nonresponse behaviour, assess the relative importance of previous and current wave interviewers on current wave nonresponse, and explore whether respondents react favourably to interviewers with similar characteristics. The second and third papers investigate the properties of cross-classified and multiple membership multilevel models respectively under various survey conditions. The second study looks at the effects of different interviewer case assignment schemes, total sample sizes, group sizes (interviewer caseload), number of groups (number of interviewers), overall rates of response, and the variance partitioning coefficient on the properties of the estimators and the power of the Wald test. The study aims to provide practical recommendations for future study designs by identifying the smallest total sample size, interviewer pool, and the most geographically-restrictive and cost-effective interviewer case allocation required to adequately distinguish between area and interviewer effects. The third paper includes a sensitivity analysis which looks at how accurately the Deviance Information Criterion identifies the best weighting scheme for different true multiple membership weights, interview allocation profiles, and total sample sizes. This sensitivity analysis indicates how well the relative importance of the previous and current wave interviewers can be estimated in multiple membership models under different survey conditions. Moreover the quality of parameter estimates under models with correctly specified weights, models with incorrectly specified weights, and models with weights based on the Deviance Information Criterion are also investigated.

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Identifiers

ORCID for Gabriele B. Durrant: orcid.org/0009-0001-3436-1512

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