Leveraging its in-house technical expertise, IHfRA supports clients in the full life-cycle of research projects, from design, through to data collection and analysis.
Typical research projects include: program evaluations, household surveys, poverty analysis, prevalence studies and studies on perception, knowledge, attitudes, behavior and practice (KABP), price sensitivity, etc.
IHfRA works in close collaboration with clients to design effective research tools and instruments. When developing research instruments our teams follow a number of principles:
never re-invent the wheel, study the literature;
always think about the local context; and
test the research instruments, don’t assume they make sense.
Research instruments are translated to the local language(s), back translated into the original language, piloted in live conditions. This ensures that the research instruments are consistent with the research objectives and that questions are well understood and interpreted by the target population.
To ensure data accuracy and quality, IHfRA checks a number of quality criteria including the flow of questions, the wording of questions, the precision of the translation and the degree to which the research instruments capture the local context.
IHfRA’s research team helps clients select the most appropriate weighting strategy for their sample. Our considerations for weighting are to:
help clients estimate population totals (using weights reflecting the inverse probability of selection);
adjust samples so that they better match the characteristics of the population (using post-stratification weights);
correct for attrition bias (if there is a known source of attrition); or
balance the treatment and control group (weighting on the inverse of the propensity score).
IHfRA’s research team helps clients make sense of data using econometric analysis techniques and statistical software tools.
Our teams work with a number of principles:
structure the analysis following a clear set of priors/hypothesis;
work collaboratively, as that is the best way to generate additional ideas and hypotheses;
start simple, focusing on uni-variate and bi-variate analysis to understand variables and patterns in the data;
automate analysis wherever possible to accelerate delivery to the client and not focus too much energy on trivial calculations;
estimate coefficients using several techniques/models to test the robustness of results;
think twice about how standard errors and test-statistics are estimated, and
convey findings graphically.
We have experience working with all sorts of datasets, including household datasets, child datasets, agriculture-focused datasets, export datasets, national tax databases, etc.
IHfRA is constantly looking for partners interested in applying network analysis or complexity methods to development issues. The type of techniques we routinely adapt to our project work, include:
studying the relationship between variables not only in the framework of regression analysis but also through the lense of a network of variables, which has proven to lead to very interesting insights;
applying measures of complexity to calculate socio-economic indexes, such as wealth;
generating similarity networks, to identify and visualize clusters of individuals or clusters; and
using network visualizations to convey messages/findings.
IHfRA helps clients design the most optimal sampling strategies, given the research objectives and budget constraints. IHfRA also works with clients to operationalize these strategies, while minimizing the risks of compromising the validity of the sample and staying within budget parameters.
Sampling effectively is key to the success of research projects; it determines:
which research questions can be addressed;
the validity of the results;
the analytic options available;
the precision of results; and
the required budget, as sample size is one of the biggest cost-drivers in research projects.
Sampling always comes with uncertainty, risks and limitations. IHfRA helps clients understand some of the analytic and financial trade-offs involved when opting for different sampling strategies as well as the sensitivity of statistical power to various known/unknown parameters using established formulas.
IHfRA supports clients in the design of relevant and testable research questions using its proprietary design-thinking approach.
This iterative and collaborative method, allows us to design research projects in a structured way, starting from policy assumptions or what we refer to as policy levers, through to identifying testable priors/hypotheses, symptoms that we would expect to observe if these priors were true, and associated metrics. The process can involve semi-structured interviews and focus groups.
The aim of the design-thinking-approach is to:
think strategically and in a structured way about how a research project can inform policy and program design/implementation;
to generate ideas on the best hypothesis to test;
to engage with the client through a structured thinking process; and
to ensure research projects are as relevant as possible.
IHfRA has the in-house capability to conduct simple GIS mapping for clients, including graduated symbol maps, and point maps, that account for the vast majority of our clients’ needs.
IHfRA also puts a large emphasis on data visualization and conveying statistics in a graphical and simple way. Data visualization plays a double role:
makes data easier to understand and read; but
also helps our research teams generate new insights about patterns and correlations.
IHfRA is continuously expanding its capabilities in the GIS and Data Visualization space and experimenting with new ways to convey information, in particular when it comes to network visualizations.
IHfRA’s core team brings strong individual and collective technical expertise in delivering research projects, from start to finish. Our research team, trained in some of the world’s best universities, specializes in econometric analysis, program evaluation, and sampling techniques.
IHfRA takes a very structured approach to research using a proprietary method. This iterative and collaborative method, allows our teams to design research projects in a structured way, starting from policy assumptions, through to identifying testable priors/hypotheses, symptoms that we would expect to observe if these priors were true, and associated metrics.
IHfRA strives to produce analysis of the highest quality. We achieve this by taking a structured approach, testing hypotheses both quantitatively and qualitatively and systematically stress-testing our results and methods. This also means being forthcoming about the limitations of the research undertaken and the assumptions under which results hold.
We have worked on many different topics, with clients in both public and private sectors, IHfRA has an excellent standing of the local context. As a company, we invest in always improving our local knowledge and integrating the specifics of the local context into our project design and project execution.
At IHfRA, we try to innovate with every new opportunity, integrating the latest available research methods, testing approaches borrowed from other sciences (in particular, complex networks) and applying recently acquired ideas from previous projects. Through our work and the IHfRA Lab, we aim to make a significant contribution to development economics.