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Mode and Interviewer Effects in Egocentric Network Research.

ABSTRACT: Surveys of egocentric networks are especially vulnerable to methods effects. This study combines a true experiment-random assignment of respondents to receive essentially identical questions from either an in-person interviewer or an online survey--with audio recordings of the in-person interviews. We asked over 850 respondents from a general population several different name-eliciting questions. Face-to-face interviews yielded more cooperation and higher quality data but fewer names than did the web surveys. Exploring several explanations, we determine that interviewer differences account for the mode difference: Interviewers who consistently prompted respondents elicited as many alters as did the web survey and substantially more than did less active interviewers. Although both methods effects substantially influenced the volume of alters listed, they did not substantially modify associations of other variables with volume.

SUBMITTER: Fischer CS

PROVIDER: S-EPMC6748643 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Mode and Interviewer Effects in Egocentric Network Research.

Fischer Claude S CS Bayham Lindsay L

Field methods 20190714 3

Surveys of egocentric networks are especially vulnerable to methods effects. This study combines a true experiment-random assignment of respondents to receive essentially identical questions from either an in-person interviewer or an online survey--with audio recordings of the in-person interviews. We asked over 850 respondents from a general population several different name-eliciting questions. Face-to-face interviews yielded more cooperation and higher quality data but <i>fewer</i> names than ...[more]

PMID: 31530997

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Project description:BackgroundSelection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results.MethodsThe identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated.ResultsUsing nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% - 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive.ConclusionsOur approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

Dataset Information

Mode and Interviewer Effects in Egocentric Network Research.

Publications

Mode and Interviewer Effects in Egocentric Network Research.

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