Household surveys are one of the most important sources of social and demographic statistics in the U. While housing censuses are also a key source of such statistics, they are only conducted every 10 to 15 years.
This underlines the need to prioritize high quality data over cost and time complications by planning and budgeting accordingly from the outset substantially increase the time and cost of data. Several important lessons can inform future implementation of household surveys in urban populations. First, the use of GIS technology to develop sampling frames is an innovative and cost-effective method in resource-constrained settings like urban Karachi.
Second, having a dedicated data management team to monitor electronic data collection in real time facilitates efficient detection of errors and inconsistences, and greatly enhances data quality. Third, the strategy of interviewing women immediately after listing households in each cluster makes it easier to re-locate selected respondents and reduces loss-to-follow up.
Fourth, understanding local norms and developing culturally appropriate strategies to engage participants is essential to build trust with communities and may significantly reduce refusals. Training female enumerators preferably from the same locality is ideal since they are can easily build rapport and approach reproductive health topics with sensitivity and respect.
Lastly, ensuring privacy in joint family households may be achieved by rescheduling interviews for another time when most family members are not at home. Findings of this study will help to improve the quality and efficiency of future household surveys in urban settings. Sustainable developmental goals.
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We would also like to acknowledge colleagues at the Harvard T. Maximize the use of household survey data for SDG reporting. Reference documents Terms of reference for working group on household surveys.
This working group will support the development of household survey-based indicators, maintain definitions of indicators calculated from survey data, contribute to the harmonization of survey data used by different organizations, and prepare guidelines for producers and users of survey data. Source: Analysis carried out by Development Initiatives as part of the Joined-up Data Standards project — see the discussion paper: Household surveys: do competing standards serve country needs?
However, data users needing to pull this data together for comparative analysis still face challenges. Although questions are duplicated across surveys, each survey follows its own coding standards for variables meaning duplicated questions cannot easily be matched. As a result, merging or comparing across datasets from different surveys to make a continuous data trend is both labour intensive and costly.
Additionally, not only is the data coded differently between DHS and MICS, but data structures are substantially different between versions and rounds of the same survey. There is considerable variation in the size of surveys. However, sample size can also vary within a country. The sample size of a survey is determined by two dimensions.
First, the number of subnational domains required for disaggregated reporting. Second, a calculation that works out the number of households required in each domain sample to reach the target population. What appears at first to be counterintuitive is that the sample size required for any country is not related to the total population of the country but rather to the number of domains subnational divisions chosen. The cost of a survey depends on many factors such as the sample size of surveyed population; size of the survey number of modules ; population density; income level; and level of technical assistance required, which often depends on the statistical capacity of a given country.
To adequately sample the population, these surveys require accurate, current census information about the underlying population. Many countries do not have current census data and rely on biased information for sampling. Sampling also limits the extent to which data can be disaggregated. Most DHS and MICS surveys are only designed to represent a few subnational areas, limiting the ability of data users to provide rich disaggregated data beyond those levels.
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