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Ian Brunton-Smith, University of Surrey Patrick Sturgis, University of Southampton

Detecting and understanding interviewer effects on survey data using a cross-classified mixed-effects location scale model. Ian Brunton-Smith, University of Surrey Patrick Sturgis, University of Southampton George Leckie, University of Bristol 7 th ESRC Methods Festival, 5 th July, Bath, UK.

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Ian Brunton-Smith, University of Surrey Patrick Sturgis, University of Southampton

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  1. Detecting and understanding interviewer effects on survey data using a cross-classified mixed-effects location scale model Ian Brunton-Smith, University of Surrey Patrick Sturgis, University of Southampton George Leckie, University of Bristol 7th ESRC Methods Festival, 5th July, Bath, UK

  2. Motivation • Interviewers can substantially inflate variance of estimators • idiosyncrasies in administration of questions • interaction of personal characteristics with those of respondents • Induce within-interviewer dependency • Akin to clustering • Standard approach: mixed effects model with random intercept

  3. Another source of interviewer error?

  4. Mixed effects approach to interviewer effects • Two-level (e.g. individuals nested in interviewers) random intercept model: where , • Enables estimation of interviewer ICC

  5. Location-scale model • Extend the standard model by modelling the level-1 variance as a log-linear function of covariates and a further random effect (Hedeker et al., 2008) Mean function: Level-1 variance function: where ,

  6. Location-scale model • Extend the standard model by modelling the level-1 variance as a log-linear function of covariates and a further random effect (Hedeker et al., 2008) Mean function: Level-1 variance function: where , , • Extended to the cross-classified case to adjust for area confounding (e.g. Vassallo et al., 2016; Durrant et al., 2010)

  7. Data and measures • Wave 3 of UKHLS general population sample (2011-12) n = 17,471 • Respondent age and gender • matched to ‘understanding society interviewer survey’ n = 303 • Interviewer gender, age, experience, beliefs about the value of surveys, and personality (big 5) • Adjusted for area clustering (MSOA) n = 3,473 • Ethnic diversity, socio-economic disadvantage, urbanicity, population mobility, age/housing structure (census)

  8. Results “People in this neighbourhood generally don’t get along with each other” (5-point likert scale)

  9. Identifying differences between interviewers • Design effect between 2.5 and 4.9 across middle 95%

  10. Quantifying the effect • Interviewer scoring 1SD below mean on extraversion, had worked on other surveys, but does not believe surveys conducted responsibly: DEFF = 3.2 • As above but 1SD above mean on extraversion: DEFF = 2.4

  11. Sense check: A self completion item “The friendships and associations I have with other people in my neighbourhood mean a lot to me”

  12. Setting up the model – a template for Stat-JR http://www.bristol.ac.uk/cmm/software/statjr/

  13. Discussion • Interviewer can have effect on variability of responses as well as mean • Location-scale model provides a flexible approach to identify these effects (implemented in Stat-JR) • Can be linked to specific interviewer characteristics to better understand effects Full paper available: Brunton-Smith, I., Sturgis, P., and Leckie, G. (online) ‘Detecting and understanding interviewer effects on survey data by using a cross-classified mixed effects location-scale model.’Journal of the Royal Statistical Society Series A. • http://onlinelibrary.wiley.com/doi/10.1111/rssa.12205/epdf

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