The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.

Strengths and limitations of computer assisted telephone interviews (CATI) for nutrition data collection in rural Kenya

Despite progress in fighting undernutrition, Africa has the highest rates of undernutrition globally, exacerbated by drought and conflict. Mobile phones are emerging as a tool for rapid, cost effective data collection at scale in Africa, as mobile phone subscriptions and phone ownership increase at the highest rates globally. To assess the feasibility and biases of collecting nutrition data via computer assisted telephone interviews (CATI) to mobile phones, we measured Minimum Dietary Diversity for Women (MDD-W) and Minimum Acceptable Diet for Infants and Young Children (MAD) using a one-week test-retest study on 1,821 households in Kenya. Accuracy and bias were assessed by comparing individual scores and population prevalence of undernutrition collected via CATI with data collected via traditional face-to-face (F2F) surveys. We were able to reach 75% (n = 1366) of study participants via CATI. Women’s reported nutrition scores did not change with mode for MDD-W, but children’s nutrition scores were significantly higher when measured via CATI for both the dietary diversity (mean increase of 0.45 food groups, 95% confidence interval 0.34–0.56) and meal frequency (mean increase of 0.75 meals per day, 95% confidence interval 0.53–0.96) components of MAD. This resulted in a 17% higher inferred prevalence of adequate diets for infants and young children via CATI. Women without mobile-phone access were younger and had fewer assets than women with access, but only marginally lower dietary diversity, resulting in a small non-coverage bias of 1–7% due to exclusion of participants without mobile phones. Thus, collecting nutrition data from rural women in Africa with mobile phones may result in 0% (no change) to as much as 25% higher nutrition estimates than collecting that information in face-to-face interviews.

Systematic Review of Methods in Low-Consensus Fields: Supporting Commensuration through `Construct-Centered Methods Aggregation' in the Case of Climate Change Vulnerability Research

There is increasing interest in using systematic review to synthesize evidence on the social and environmental effects of and adaptations to climate change. Use of systematic review for evidence in this field is complicated by the heterogeneity of methods used and by uneven reporting. In order to facilitate synthesis of results and design of subsequent research a method, construct-centered methods aggregation, was designed to 1) provide a transparent, valid and reliable description of research methods, 2) support comparability of primary studies and 3) contribute to a shared empirical basis for improving research practice. Rather than taking research reports at face value, research designs are reviewed through inductive analysis. This involves bottom-up identification of constructs, definitions and operationalizations; assessment of concepts' commensurability through comparison of definitions; identification of theoretical frameworks through patterns of construct use; and integration of transparently reported and valid operationalizations into ideal-type research frameworks. Through the integration of reliable bottom-up inductive coding from operationalizations and top-down coding driven from stated theory with expert interpretation, construct-centered methods aggregation enabled both resolution of heterogeneity within identically named constructs and merging of differently labeled but identical constructs. These two processes allowed transparent, rigorous and contextually sensitive synthesis of the research presented in an uneven set of reports undertaken in a heterogenous field. If adopted more broadly, construct-centered methods aggregation may contribute to the emergence of a valid, empirically-grounded description of methods used in primary research. These descriptions may function as a set of expectations that improves the transparency of reporting and as an evolving comprehensive framework that supports both interpretation of existing and design of future research.