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Kish’s (1962) classical intra-interviewer correlation (ρint) provides survey researchers with an estimate of the effect of interviewers on variation in measurements of a survey variable of interest. This correlation is an undesirable product of the data collection process that can arise when answers from respondents interviewed by the same interviewer are more similar to each other than answers from other respondents, decreasing the precision of survey estimates. Estimation of this parameter, however, uses only respondent data. The potential contribution of variance in nonresponse errors between interviewers to the estimation of ρint has been largely ignored. Responses within interviewers may appear correlated because the interviewers successfully obtain cooperation from different pools of respondents, not because of systematic response deviations. This study takes a first step in filling this gap in the literature on interviewer effects by analyzing a unique survey data set, collected using computer-assisted telephone interviewing (CATI) from a sample of divorce records. This data set, which includes both true values and reported values for respondents and a CATI sample assignment that approximates interpenetrated assignment of subsamples to interviewers, enables the decomposition of interviewer variance in means of respondent reports into nonresponse error variance and measurement error variance across interviewers. We show that in cases where there is substantial interviewer variance in reported values, the interviewer variance may arise from nonresponse error variance across interviewers.