Challenges and measures to improve interviewers' bias in large-scale demographic surveys in India: Some suggestions based on analysis of NFHS-4 data - Tarun Kumar Roy; Rajib Acharya (Elsevier, 2022)
With increasing demand for more data at local level, the health surveys have expanded both their coverage and areas of inquiry. To cater to this demand, the sample size in National Family Health Surveys (NFHS) increased significantly and thereby raised concerns regarding quality. The present paper attempts to investigate the presence of interviewers' bias in the birth history data in 4th round of NFHS in four states -Haryana, Odisha, Tamil Nadu and Maharashtra. The paper suggests a practical procedure that can be used to promote judicious supervision to minimize the non-sampling errors in future rounds of NFHS or other large-scale demographic surveys. Findings show that the outlier-based approach adopted in the paper helps in detecting the presence of interviewers' bias in the enumeration of total children ever born as well as those born during 5 years prior to the survey - two critical variables in demographic surveys. Among the four study states, the extent of the bias was highest in Tamil Nadu. In fact, in Haryana, the data was found to be free of any bias in the recording of the occurrence of births in 5 years preceding the survey. It is suggested that it should be feasible to employ the outlier-based approach early when fieldwork is in progress, along with usual practice of generating field check tables. This approach would have the potential to not only streamline the supervision but also help salvage the data from any biasing effects. The biasing effects, if any and found early during fieldwork can be rectified by suitably arranging the necessary revisits to the respondents.