This paper examines the design and the process used to carry out the China Fertility Survey 2017, a national representative survey that collected data on fertility desire, childbearing behavior, the use of childbearing services, and the determinants of childbearing behavior. The sampling method adopted was three-stage stratified probabilities proportional to size (PPS), and survey implementation made use of Computer Assisted Personal Interviewing (CAPI). CAPI played a significant role in survey design, last-stage sampling, interviewer training, face-to-face interviews, and questionnaire review and quality control. The survey results were compared with relevant data in the Integrated Management Information System for Population and Family Planning to check consistency. Ex post facto weighting was applied to correct sample structure bias. The process used to acquire accurate personal information is summarized. Suggestions based on consideration of sampling frame distortion by population mobility and other factors are put forward in the hope of improving similar sampling surveys in the future.

This paper reviews the background of China’s decision to launch the “China Migrants Dynamic Survey” (CMDS), and examines the ideas and characteristics of CMDSs carried out annually from 2009 through 2018. The paper considers such aspects as project design, questionnaire design, sampling design, and survey implementation, and summarizes why it is necessary to carry out CMDS and the survey’s performance. Based on the need for information that drives migration surveys and research in the new era, and taking into account the experience gained from previous surveys, this paper attempts to set forth several issues that need to be taken into consideration in the design and implementation of future surveys of migrants.

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.

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