the former may lead to additional steps for “course correction” in the context of an ongoing survey, the latter would be useful for decisions regarding future surveys.

In the literature, the term “interviewer effect” has been used to reference interviewer contributions to variations in interview outcomes. Interviewers can contribute to the variability of respondents’ answers, non-response/survey participation, measurement error or bias, and interview length. West and Blom (2017) summarizes findings concerning interviewer effects, focusing on high-income countries. In low- and middle-income countries, interviewer characteristics such as gender (FloresMacias et al. 2008; Vollmer et al. 2021) and ethnicity (Adida et al. 2016 <mark>) have been found to</mark> affect responses. Research has also demonstrated that interviewer characteristics can affect answers to sensitive questions, including those on political preference in Uganda (Maio and Fiala 2020), domestic violence in India (Singh et al. 2022), and abortion (in the context of Demographic and Health Surveys) (Leone et al. 2021).

The paradata-driven analyses of interviewer effects on survey duration have been limited in lowand middle-income countries until now - in contrast with the numerous applications that have been focused on high-income contexts. The latter category of research has typically relied on multilevel models, which are typically used for analyzing data with a hierarchical structure or when the data is nested within a larger group (such as responses within a group of interviewers). These models have been used to decompose the contributions of interviewer effects, enumeration area effects, and respondent effects towards the variance in survey duration. In high-income countries, the effects of these variables have overall been quite modest (Couper and Kreuter, 2013).<sup>10</sup> With the increasing availability of CAPI/CATI paradata, there is now an opportunity to undertake similar analyses in low- and middle-income countries.

In what follows, we use the CAPI survey paradata at our disposal and obtain multi-level model estimations to discern interviewer effects on survey duration in Cambodia, Ethiopia and Tanzania. Our analysis differs from the previous paradata-powered research in the sense that we do not model question-level duration but rather focus on module duration and conduct separate analysis for each module. This allows us to contrast results across various modules, currently a significant gap in the literature.

Specifically, we estimate a range of multi-level models that are specific to each individual and household questionnaire module. The dependent variable is always the module duration in minutes. To model duration for individual questionnaire modules, we first estimate **Equation 1** as

> 10 Couper and Kreuter (2013), for example, found that the interviewer variable contributes to less than 2 percent of the variation in interview-length while the respondent variable contributes about 3.8 to 6.3 percent, depending on the model. Most of the variation is at the question-level - about 96 percent, again depending on the model. The authors noted that this result is consistent in the literature and thus one should expect that interviewer and respondent contribution to not impact survey time much when conducting fieldwork.

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