ESRA 2017 Programme

Tuesday 18th July      Wednesday 19th July      Thursday 20th July      Friday 21th July     

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Tuesday 18th July, 14:00 - 15:30 Room: N AUD5

It’s the Interviewers! New developments in interviewer effects research 3

Chair Dr Salima Douhou (City University of London, CCSS )
Coordinator 1Professor Gabriele Durrant (University of Southampton)
Coordinator 2Dr Olga Maslovskaya (University of Southampton)
Coordinator 3Dr Kathrin Thomas (City University of London, CCSS)
Coordinator 4Mr Joel Williams (TNS BMRB)

Session Details

To what extent do interviewers affect data collection and how can we better monitor and limit their impact?

Any deviation from the standardised protocol of the data collection process has the potential to induce bias to the data. Interviewer effects, defined as the distortions of survey responses in surveys with interviewer presence, may have a severe impact on data quality. These effects result from potential reactions to the social style and personality of interviewers, but also to their presentation of questions.

Analysis based on data that are biased by interviewer intervention and the conclusions drawn on the basis of this are likely to be incorrect. Hence, survey methodologists have improved the way in which interviewers are trained and briefed in order to limit the interviewers' influence. Yet, it remains open why even in surveys with exceptional efforts to train and monitor interviewers, interviewer effects occur.

Interviewers make (initial) contact with the prospective respondents and attempt to convince them to participate in the survey. The doorstep interaction between prospective respondents and interviewers is rarely documented, but an increasing number of studies indicates that some interviewers are more successful than others in convincing the prospective respondents to participate in a survey and to avoid non-response.

Once door-step interaction has been successful, interviewers may further affect the way in which respondents answer the survey questions on the questionnaire. Variation in survey responses may be due to the attitudes, interpersonal skills and personality of interviewers, but also relate to how the interviewers present particular questions and how strictly they follow the instructions. Any deviation from the standardised protocol provided by the core research team of the survey project decreases the comparability of the survey responses.

This session welcomes papers on new developments in the area of interviewer effects. Topics may include but are not restricted to:
• methodological developments in measuring and modelling interviewer effects,
• interviewer effects on measurement error,
• interviewer effects on nonresponse rates and nonresponse bias,
• interviewer influences on response latencies (timings),
• influence of personality traits, behaviour, attitudes, experience, and other characteristics of interviewers on survey estimates,
• implications for interviewer recruitment and training strategies,
• monitoring and evaluation of fieldwork efforts by interviewers,
• collection of GPS data or audio-visual material of door-step interactions.

Papers that discuss these issues from a comparative perspective are also welcome. We invite academic and non-academic researchers and survey practitioners to contribute to our session.

Paper Details

1. Interviewer Effects in Large-Scale Assessments of Competence: Using posterior distributions to identify interviewers with effects on the assessment
Miss Theresa Rohm (Research Assistant)
Dr Timo Gnambs (Postdoctoral Researcher)
Miss Luise Fischer (Research Assistant)
Professor Claus H. Carstensen (Head of Psychology and methods of educaitonal research)

In order to guarantee highly standardized settings in large-scale educational assessments, test administrators are intensively trained. However, in the field some test administrators do not precisely adhere to these testing protocols and deviate from the standardized practice. As a result, different interviewer behaviour can introduce a systematic bias in large-scale assessments of competences. Therefore, it was tested if systematic test administrator effects could be identified for the measurement of mathematic abilities in the adult cohort of the German National Educational Panel Study. Furthermore, the variance in ability estimates introduced by different test administrators was disentangled from the variance attributable to different geographical areas. Due to the use of sampling points, used in absence of a nation-wide population register, greater homogeneity in ability estimates can exist within the regional clusters as compared to homogeneity between clusters.

The sample consists of 5,220 respondents in the age of 24 to 67 years that lived in 466 area clusters and were interviewed by 207 different interviewers. On average, each interviewer administered the competence test to 25.2 test takers and 11.2 persons lived in each cluster. The 21 Items for mathematic competence were administered to respondents in year 2010 and 2011 by paper and pencil mode. The competence tests took place before the computer assisted personal interviews and both were normally held at the respondent’s home.

Because interviewer effects were expected to be statistically confounded with effects at the area level, cross-classified multilevel analyses with the use of Markov Chain Monte Carlo (MCMC) procedures were conducted to disentangle both sources of variance. It was found for the estimation of adult mathematic achievement, that 9.5 percent of the observed variance in mathematical competence is attributable to interviewers and 0.5 percent to geographical areas. Even though the variance in competence measures traceable to interviewer presence was rather high, none of the investigated interviewer characteristics (gender, age, education and work experience) was found to be significantly related to ability measures of respondents. Thus, socio-demographic differences were unable to identify interviewers with aberrant test administration behaviours.

Therefore, Bayes predictions of second level errors were used to identify outlying interviewers. As the considered variables adjust for prior differences among test takers and contexts, the interviewer level residual variances are a measure of bias introduced to competence measurement by the test administrator. Unobserved factors at the interviewer level which affect respondent achievement were investigated this way. Our Bayesian prediction of the second level random effects for mathematic achievement identified interviewers that exhibited significant effects on the respondent’s competence measurement. Out of the 207 interviewer clusters, 6 clusters have an interval below and 9 clusters have an interval above zero. Hence, their estimated competence intercept deviates from the population mean. As the model adjusts for differences between respondent characteristics and contextual factors these significant deviations in predicted intercept values can be interpreted as interviewer bias in competence measurement.


2. Minimising interviewer effects on the Crime Survey for England and Wales
Mr Luke Taylor (Kantar Public)
Mr Adam Green (Kantar Public)

Interviewer effects in face-to-face studies can not only impact on the precision of survey estimates but also impact on the accuracy of analysis conducted at a local level. The Crime Survey for England and Wales (CSEW) is a continuous face-to-face cross sectional study conducted by Kantar Public on behalf of the Office for National Statistics. The study is designed to allow for analysis to be conducted separately for each Police Force Area (of which there are 43 in England and Wales).The geographical clustering of sample points, and the fact that interviewers tend to work near to where they live, can mean that an individual interviewer works a reasonably high proportion of a PFA over the course of a year. There is therefore a risk that interviewer effects may lead to bias in PFA level estimates.

A quantitative approach to identify interviewers who administer the survey instrument in an atypical fashion has been developed for this study. Demographic variables (deemed to be largely unaffected by interviewer effects) are used to model respondent level responses at a set of key questions. This model is then used to predict the distribution of responses that we expect each interviewer to record at these variables, based on the set of respondents they have interviewed. The expected and observed survey estimates are used to calculate T-scores for each interviewer at each variable. Where large discrepancies have been found, interviewers have been contacted to try and identify the root cause of the issue and in order to retrain them.

In this paper we will explain the approach we have used to identify interviewers who are administering questions incorrectly, and report on the impact which our interventions have had by tracking the progress of the interviewers since they were retrained.