Non-sampling errors in questionnaire surveys: findings from a National Fertility Survey

Abstract

Non-sampling errors can generally be divided into three types: sampling frame errors, non-response errors and measurement errors. Missing target units in the sampling frame, improper handling of non-responses, and misreporting or underreporting of key variables in the questionnaire can all cause deviations in a survey’s results. The widespread application of Computer-Assisted Personal Interviewing (CAPI) systems and the inclusion of administrative records from government sources in surveys has strengthened the ability to control non-sampling errors. Taking a national fertility sampling survey as an example, this study summarizes the sources of various non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey. The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors. The findings indicate that non-sampling errors were the main source of total error in the survey, and that the errors found came mainly from sampling frame errors; non-response errors and measurement errors were controlled and had little impact on the survey results.