Several developing countries are currently implementing phone surveys in response to immediate data needs to monitor the socioeconomic impact of COVID-19. However, phone surveys are often subject to coverage and non-response bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey. Using data from high-frequency phone surveys in Ethiopia, Malawi, Nigeria, and Uganda, this study investigates the magnitude and source of biases present in these four surveys and explores the effectiveness of techniques applied to reduce bias. Varying levels of coverage and non-response bias are found in all four countries. The successfully contacted samples in these four countries were biased toward wealthier households with higher living standards. Left unaddressed, this bias would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the country. However, phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative survey from which the sample is drawn. Applying these methods to the four surveys resulted in a substantial reduction in bias, although the bias was not fully eradicated. This highlights one of the potential advantages of drawing phone survey samples from existing face-to-face, representative surveys over random digit dialing or using lists from telecom providers where such adjustment methods can be more limited.
Information
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Auteur
Ambel,Alemayehu A., Mcgee,Kevin Robert, Tsegay,Asmelash Haile
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Date du document
2021/05/26
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Type de document
Document de travail de recherche sur les politiques
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Numéro du rapport
WPS9676
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Volume
1
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Total Volume(s)
1
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Pays
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Région
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Date de publication
2021/05/26
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Disclosure Status
Disclosed
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Titre du rapport
Reducing Bias in Phone Survey Samples : Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames in Four African Countries
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Mots clé
development research group; Demographic and Health Survey; survey sample; phone number; improved water source; income from remittance; Access to Electricity; household head; transportation of good; per capita consumption; representative household surveys; risk of transmission; household budget survey; extent of coverage; household consumption expenditure; terms of consumption; census enumeration area; living standard measurement; representative survey; response rate; nonfarm enterprise; sample household; financial account; marital status; telephone surveys; dependency ratio; average household; female head; mean characteristic; rural area; high frequency; random digit; sampling approaches; reference person; telecom provider; education level; crop farm; adjustment factor; sample selection; work status; sample units; calibration approach; representative sample; socioeconomic impact; urban sample; external information; distinct advantage; wage employment; population census; logistical costs; employment status; national survey; random sample; Cash Transfer; consumption quintile; wealth quintile; correction factor; general population; individual characteristic; information availability; coverage rate; welfare measure; aggregate measure; interview time; high share; ownership share; minimal risk; demographic composition; network provider; rural level; mobile network; common feature; standard definition; logistic regression; health effect; confidence interval; urban household; wealth index; toilet facility; Public Services; saharan africa; weighting method; survey sampling; poor household; data needs; comparative analysis; several sources; Research Support; development policy; network availability; use Telephone; phone penetration; coping strategy; open access; survey methodology; household characteristic; recent years
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