Detecting and understanding interviewer effects on survey data using a cross-classified mixed-effects location scale model

Ian Brunton-Smith, Patrick Sturgis, George Leckie

Research output: Contribution to journalArticle (Academic Journal)

12 Citations (Scopus)
361 Downloads (Pure)

Abstract

We propose a cross-classified mixed-effects location scale model for the analysis of interviewer effects in survey data. The model extends the standard two-way cross-classified random-intercept model (respondents nested in interviewers crossed with areas) by specifying the residual variance to be a function of covariates and an additional interviewer random effect. This extension provides a way to study interviewers’ effects on not just the ‘location’ (mean) of respondents’ responses, but additionally on their ‘scale’ (variability). It therefore allows researchers to address new questions such as: Do interviewers influence the variability of their respondents’ responses in addition to their average, and if so why? In doing so, the model facilitates a more complete and flexible assessment of the factors associated with interviewer error. We illustrate this model using data from wave 3 of the UK Household Longitudinal Survey (UKHLS), which we link to a range of interviewer characteristics measured in an independent survey of interviewers. By identifying both interviewer characteristics in general, but also specific interviewers who are associated with unusually high or low or homogeneous or heterogeneous responses, the model provides a way to inform improvements to survey quality.
Original languageEnglish
Pages (from-to)551-568
Number of pages18
JournalJournal of the Royal Statistical Society: Series A
Volume180
Issue number2
Early online date13 May 2016
DOIs
Publication statusPublished - Feb 2017

Keywords

  • Interviewer effect
  • Measurement error
  • Mixed effects location–scale model
  • Stat-JR software
  • Understanding society

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