Indicators to examine quality of large scale survey data: an example through district level household and facility survey.

Background Large scale surveys are the main source of data pertaining to all the social and demographic indicators, hence its quality is also of great concern. In this paper, we discuss the indicators used to examine the quality of data. We focus on age misreporting, incompleteness and inconsistency of information; and skipping of questions on reproductive and sexual health related issues. In order to observe the practical consequences of errors in a survey; the District Level Household and Facility Survey (DLHS-3) is used as an example dataset. Methods Whipple's and Myer's indices are used to identify age misreporting. Age displacements are identified by estimating downward and upward transfers for women from bordering age groups of the eligible age range. Skipping pattern is examined by recording the responses to the questions which precede the sections on birth history, immunization, and reproductive and sexual health. Results The study observed errors in age reporting, in all the states, but the extent of misreporting differs by state and individual characteristics. Illiteracy, rural residence and poor economic condition are the major factors that lead to age misreporting. Female were excluded from the eligible age group, to reduce the duration of interview. The study further observed that respondents tend to skip questions on HIV/RTI and other questions which follow a set of questions. Conclusion The study concludes that age misreporting, inconsistency and incomplete response are three sources of error that need to be considered carefully before drawing conclusions from any survey. DLHS-3 also suffers from age misreporting, particularly for female in the reproductive ages. In view of the coverage of the survey, it may not be possible to control age misreporting completely, but some extra effort to probe a better answer may help in improving the quality of data in the survey.