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Audio computer-assisted self-interviewing (ACASI) has been widely used to collect sensitive information from respondents in face-to-face interviews. Interviewers ask questions that are not sensitive or only moderately sensitive and then allow respondents to self-administer more sensitive questions, listening to audio recordings of the questions and typically entering their responses directly into the same device that the interviewer has used. According to the conventional thinking, ACASI is taken as independent of the face-to-face interaction that almost always precedes it. Presumably as a result of this presumed independence, the respondents’ prior interaction with the interviewer is rarely considered when assessing the quality of ACASI responses. There is no body of existing research that has experimentally investigated how the preceding interviewer–respondent interaction may create sufficient social presence to affect responses in the subsequent ACASI module. The study reported here, a laboratory experiment with eight professional interviewers and 125 respondents, explores the carryover effects of preceding interactions between interviewer and respondent on responses in the subsequent ACASI. We evaluated the impact of the similarity of the live and recorded interviewer’s voice for each respondent as well as respondents’ rapport with interviewers in the preceding interview. We did not find significant main effects of vocal similarity on disclosure in ACASI. However, we found significant interaction effects between vocal similarity and respondents’ rapport ratings in the preceding interview on disclosure in ACASI. When the ACASI voice was similar to the interviewer’s voice in the preceding interaction, respondent-rated rapport led to more disclosure but, when the ACASI voice is clearly different from the interviewer’s voice, respondent-rated rapport in the prior interaction did not affect disclosure.

Interviewer-respondent rapport is generally considered to be beneficial for the quality of the data collected in survey interviews; however, the relationship between rapport and data quality has rarely been directly investigated. We conducted a laboratory experiment in which eight professional interviewers interviewed 125 respondents to see how the rapport between interviewers and respondents is associated with the quality of data—primarily disclosure of sensitive information—collected in these interviews. It is possible that increased rapport between interviewers and respondents might motivate respondents to be more conscientious, increasing disclosure; alternatively, increased rapport might inhibit disclosure because presenting oneself unfavorably is more aversive if respondents have a positive relationship with the interviewer. More specifically, we examined three issues: (1) what the relationship is between rapport and the disclosure of information of varying levels of sensitivity, (2) how rapport is associated with item nonresponse, and (3) whether rapport can be similarly established in video-mediated and computer-assisted personal interviews (CAPIs). We found that (1) increased respondents’ sense of rapport increased disclosure for questions that are highly sensitive compared with questions about topics of moderate sensitivity; (2) increased respondents’ sense of rapport is not associated with a higher level of item nonresponse; and (3) there was no significant difference in respondents’ rapport ratings between video-mediated and CAPI, suggesting that rapport is just as well established in video-mediated interviews as it is in CAPI.

This article examines the influence of interviewers on the estimation of regression coefficients from survey data. First, we present theoretical considerations with a focus on measurement errors and nonresponse errors due to interviewers. Then, we show via simulation which of several nonresponse and measurement error scenarios has the biggest impact on the estimate of a slope parameter from a simple linear regression model. When response propensity depends on the dependent variable in a linear regression model, bias in the estimated slope parameter is introduced. We find no evidence that interviewer effects on the response propensity have a large impact on the estimated regression parameters. We do find, however, that interviewer effects on the predictor variable of interest explain a large portion of the bias in the estimated regression parameter. Simulation studies suggest that standard measurement error adjustments using the reliability ratio (i.e., the ratio of the measurement-error-free variance to the observed variance with measurement error) can correct most of the bias introduced by these interviewer effects in a variety of complex settings, suggesting that more routine adjustment for such effects should be considered in regression analysis using survey data.

Recent methodological studies have attempted to decompose the interviewer variance introduced in interviewer-administered surveys into its potential sources, using the Total Survey Error framework. These studies have informed the literature on interviewer effects by acknowledging interviewers’ dual roles as recruiters and data collectors, thus examining the relative contributions of nonresponse error variance and measurement error variance among interviewers to total interviewer variance. However, this breakdown may depend on the interviewing technique: some techniques emphasize behaviors designed to reduce variation in the answers collected by interviewers more so than other techniques. The question of whether the contributions of these error sources to total interviewer variance change for different interviewing techniques remains unanswered. Addressing this gap in knowledge has important implications for interviewing practice because the technique used could alter the relative contributions of variance in these error sources to total interviewer variance. This article presents results from an experimental study mounted in Germany that was designed to answer this question about two specific interviewing techniques. A national sample of employed individuals was first selected from a database of official administrative records, then randomly assigned to interviewers who themselves were randomized to conduct either conversational interviewing (CI) or standardized interviewing (SI), and finally measured face-to-face on a variety of cognitively challenging survey questions with official values also available for verifying the accuracy of responses. We find that although nonresponse error variance does exist among interviewers for selected measures (especially respondent age in the CI group), measurement error variance tends to be the more important source of total interviewer variance, regardless of whether interviewers are using CI or SI.

Responsive survey designs may attempt to tailor contact attempts to selected units based on available information about the units. Cross-sectional surveys often lack sufficient information for such tailoring, especially at the beginning of the field period. In panel surveys, on the other hand, information from prior waves exists, and such information can be used to refine attempts to make contact or elicit cooperation. However, existing findings regarding the effectiveness of such information are confounded with interviewers' and respondents' behaviors. This research note reports results from a randomized field experiment that tested the use of prior wave contact information to increase fieldwork efficiency in a face-to-face survey. A random subset of respondents to the Medical Expenditure Panel Survey Household Component was informed via postcard about a prespecified interview appointment. The date and time of the appointment were based on information from the prior wave interview date. Such date assignment significantly reduced the number of actions needed to achieve an interview in the treatment group compared to the control group, reducing the number of hours worked in the field by 4 percent.

Although interviewer observations have potential as auxiliary sources of information on key survey variables, questions about their quality temper enthusiasm for their use in survey estimation and responsive survey design. This study considers the utility of two interviewer observations (household income and household receipt of unemployment benefits) collected in a panel survey: the German Labor Market and Social Security (PASS) study. We find that the ability of the interviewer observations to accurately identify these household features is not as high as that of prior-wave survey reports on these features, but the observations do tend to capture accurate information for households with changing socioeconomic status over time (where prior-wave reports may be inconsistent with current-wave reports). The observations add modest predictive power to models for key survey variables that also include survey reports on related variables in prior waves, but this predictive power may be limited by relatively high error rates and variance in observation quality among interviewers. Finally, estimates based on panel households only improve slightly when including the observations in nonresponse adjustments, which is likely due to the inability of the observations to predict response propensity (given a relatively low attrition rate for the panel households). Implications for practice and directions for future research in this area are discussed in conclusion.