A Comparison of Different Approaches to Examining Whether Interviewer Effects Tend to Vary Across Different Subgroups of Respondents

Modeling Group-Specific Interviewer Effects on Survey Participation Using Separate Coding for Random Slopes in Multilevel Models

“…Unfortunately, this parameterization is not satisfactory because the interpretation of the variance components is not intuitive and potentially even misleading: The obtained random slope variance for the dummies only indicates to what extent the difference in response rates between sample groups varies across interviewers (see also West and Elliott 2014). Beullens et al (2019) and Loosveldt and Wuyts (2020) also aim to identify differential interviewer effects in surveys, albeit in terms of interviewer measurement variances during the interview. In a two-step procedure, they extend the basic multilevel model with a conditional random interviewer effect model to estimate the effect of respondent characteristics on the variability of intraclass correlations.…”

“…However, to account for differences in the sample composition of the interviewers' assignments, we control for the sample units' age, gender, household size, level of education, employment status, frequency of Internet use, frequency of media consumption, and whether they voted in the last general election (for an English translation of the survey questionnaires see appendix B in the supplementary data online). 4 In addition, we account for the nonrandom allocation of interviewers by stepwise introducing the interviewer characteristics to the sample unit characteristics for which the areas could have a differential composition (see model 0 and 1 in the appendix A, tables A.3 and A.4 in the supplementary data online and for further information; Hox 1994;Blom et al 2011, p. 367;Steele and Durrant 2011;Loosveldt and Wuyts 2020).…”