Interviewer effects in food acquisition surveys.
OBJECTIVE
To understand the effects of interviewers on the responses they collect for measures of food security, income and selected survey quality measures (i.e. discrepancy between reported Supplemental Nutrition Assistance Program (SNAP) status and administrative data, length of time between initial and final interview, and missing income data) in the US Department of Agriculture's National Household Food Acquisition and Purchase Survey (FoodAPS).


DESIGN
Using data from FoodAPS, multilevel models with random interviewer effects were fitted to estimate the variance in each outcome measure arising from effects of the interviewers. Covariates describing each household's socio-economic status, demographics and experience in taking the survey, and interviewer-level experience were included as fixed effects. The variance components in the outcomes due to interviewers were estimated. Outlier interviewers were profiled.


SETTING
Non-institutionalized households in the continental USA (April 2012-January 2013).


SUBJECTS
Individuals (n 14 317) in 4826 households who responded to FoodAPS.


RESULTS
There was a substantial amount of variability in the distributions of the outcomes examined (i.e. time between initial and final interview, reported values for food security, individual income, missing income) among the FoodAPS interviewers, even after accounting for the fixed effects of the household- and interviewer-level covariates and removing extreme outlier interviewers.


CONCLUSIONS
Interviewers may introduce error in food acquisition survey data when they are asked to interact with the respondents. Managers of future surveys with similarly complex data collection procedures could consider using multilevel models to adaptively identify and retrain interviewers who have extreme effects on data collection outcomes.
['Ong|Ai Rene|AR|http://orcid.org/0000-0002-3118-3458', 'Hu|Mengyao|M|', 'West|Brady T|BT|', 'Kirlin|John A|JA|']
Interviewer effects Food acquisition surveys FoodAPS Multilevel modeling Survey methodology