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Interviewer effects in food acquisition surveys.

Ai Rene Ong1, Mengyao Hu1, Brady T West1

  • 1Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48104, USA.

Public Health Nutrition
|February 23, 2018
PubMed
Summary

Interviewers significantly impact survey data accuracy for food security and income. Multilevel models can identify and retrain interviewers with extreme effects on data collection outcomes.

Keywords:
Food acquisition surveysFoodAPSInterviewer effectsMultilevel modelingSurvey methodology

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Area of Science:

  • Survey methodology
  • Statistical modeling
  • Food security research

Background:

  • Interviewer effects can introduce variability in survey responses.
  • Accurate data collection is crucial for food security and income assessments.
  • The National Household Food Acquisition and Purchase Survey (FoodAPS) collects complex data.

Purpose of the Study:

  • To quantify interviewer effects on food security, income, and survey quality measures in FoodAPS.
  • To identify variability in responses attributable to interviewers.
  • To profile interviewers with extreme impacts on data collection.

Main Methods:

  • Utilized multilevel models with random interviewer effects on FoodAPS data.
  • Included household socio-economic status, demographics, and interviewer experience as fixed effects.
  • Estimated variance components and identified outlier interviewers.

Main Results:

  • Substantial interviewer variability was observed in food security, income, and interview duration.
  • This variability persisted after controlling for covariates and removing outliers.
  • Interviewers demonstrated significant effects on reported food security, income, and SNAP status discrepancies.

Conclusions:

  • Interviewers can introduce errors in food acquisition survey data.
  • Multilevel models are recommended for identifying and retraining interviewers with extreme effects.
  • This approach can improve the quality of future complex data collection efforts.