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ObjectivesThis study was designed to assess patterns of objectively measured physical activity (PA) and sedentary behaviour in a sample of adults in a rural setting from a low-income Sub-Saharan African country (Malawi). The patterns of PA and sedentary behaviour in Malawi were compared with US data collected and analysed using the same methodology.MethodsThe Malawi PA data were collected as part of a survey experiment on the measurement of agricultural labor conducted under the World Bank Living Standards Measurement Study program. ActiGraph accelerometers (model GT3X) were worn on the right hip in a household-based sample of 414 working-age adults (15-85 years).ResultsMean total and 95% CIs for PA by category in min/day for Malawi adults were: sedentary 387.6 (377.4-397.8), low-light 222.1 (214.7-229.5), high-light 136.3 (132.7-139.9), moderate 71.6 (68.8-74.5), vigorous 1.1 (0.5-1.8) and moderate-to-vigorous physical activity (MVPA) 72.8 (69.7-75.9). Mean of PA and sedentary behavior (min/day) summed across age and sex groups are compared between Malawi and US samples: sedentary behaviour, 387.6 vs 525.8 (p

Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys. [ABSTRACT FROM AUTHOR]

Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys. [ABSTRACT FROM AUTHOR]

Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing rapidly, but we argue the full potential of data to improve development outcomes has not been realized yet. We outline 12 features needed for data to generate greater value for development and present case studies substantiating these features. We argue that a key reason why the transformational value of data has not yet been realized is that suboptimal data—data not satisfying these 12 features—are being supplied. The features are that the data should be of adequate spatial and temporal coverage (complete, frequent, and timely), should be of high quality (accurate, comparable, and granular), should be easy to use (accessible, understandable, and interoperable), and should be safe to use (impartial, confidential, and appropriate). [ABSTRACT FROM AUTHOR]