The Demographic and Health Surveys (DHS) are a major source for many demographic and health indicators in developing countries. Although these indicators are well defined in the literature, using survey data to calculate some of these indicators has never been an easy task for data users. This paper presents the software, a user-friendly R package developed to calculate fertility indicators, such as the total fertility rate, general fertility rate, and age-specific fertility rates, and childhood mortality indicators, such as the neonatal mortality rate, post-neonatal mortality rate, infant mortality rate, child mortality rate, and under-5 mortality rate, from the DHS data. The package allows for national and subnational indicators. In addition, the package calculates sampling error indicators such as standard error, design effect, relative standard error, and confidence interval for each demographic indicator. The package can also be used to calculate the same indicators from other population surveys such as the Multiple Indicator Cluster Survey (MICS).

We found no evidence to support a larger noncoverage bias for Hispanics than non-Hispanic Whites in landline telephone surveys. This finding should be considered in the design and interpretation of telephone surveys.

Data on women’s living conditions and socio-economic development are important for understanding and addressing the pronounced challenges and inequalities faced by women worldwide. While such information is increasingly available at the national level, comparable data at the sub-national level are missing. We here present the LivWell global longitudinal dataset, which includes a set of key indicators on women’s socio-economic status, health and well-being, access to basic services and demographic outcomes. It covers 447 regions in 52 countries and includes a total of 265 different indicators. The majority of these are based on 199 Demographic and Health Surveys (DHS) for the period 1990–2019 and are complemented by extensive information on socio-economic and climatic conditions in the respective regions. The resulting dataset offers various opportunities for policy-relevant research on gender inequality, inclusive development and demographic trends at the sub-national level.

The effects of incentives on response rates, web completion rates and demographic and socioeconomic characteristics were investigated in a randomized experiment for a general population economic attitude survey. The experiment was conducted in two groups: 1) fresh cases from Address based sampling (ABS) postal addresses, and 2) recontact cases that were interviewed six months ago. The prepaid cash incentive condition was crossed with a set of Web-Mail survey contact strategies. While the fresh cases were contacted by Web-Mail concurrent or web-intensive contact strategies, the recontact cases were contacted by mail, concurrent or web-intensive contact strategies. Overall findings showed that incentives increased average response propensity without changing the covariance component of the nonresponse bias given the comparisons on a set of demographic and socioeconomic characteristics by the incentive condition. While the prepaid cash incentives significantly increased the web completion rates in the web intensive contact strategy for the fresh sample, they had a reversed effect in the concurrent strategy for the recontact sample.

The increasing cost and decreasing coverage of Random Digit Dialing (RDD) landline telephone surveys motivated The Surveys of Consumer Attitudes (SCA) at the University of Michigan to conduct monthly experimental mail survey studies using address-based sampling (ABS). The primary objectives of the experimental studies were to evaluate the feasibility of transitioning the data collection operations from telephone to mail and to investigate differential survey errors between the two modes. Overall mail survey response rates were comparable to the RDD landline survey. Coverage improved using ABS, with more than 20% of the mail responses from non-landline telephone households not covered by the RDD landline telephone surveys. Mail survey respondents from households without landline telephones were more likely to be younger, have lower income, be renters and live in one-person households. There were no apparent measurement or reporting differences between the telephone and mail self-administered modes of data collection. Furthermore, inclusion of non-landline telephone households did not result in any substantial demographic or economic attitude differences between the two approaches.