RE: How to evaluate science, technology and innovation in a R4D context? New guidelines offer some solutions | Eval Forward

Do you think the Guidelines respond to the challenges of evaluating quality of science and research in process and performance evaluations?

As an international evaluation expert, I am so fortunate to evaluate a large range of projects and programs covering research (applied and non-experimental), development and humanitarian interventions. Over past decade, I got opportunities to employ various frameworks and guidelines to evaluate CGIAR projects and program proposals especially with the World Agroforestry Center (ICRAF) and the International Institute for Tropical Agriculture (IITA) in Central Africa (Cameroon & Congo). For example, when leading the final evaluation of the Sustainable Tree Crops Programme, Phase 2 (PAP2CP) managed by the IITA-Cameroon, together with the team, we revised the OECD-DAC framework and criteria to include a science criterion to address the research dimensions such as inclusion and exclusion research criteria.

When designing high-quality research protocols for a science evaluation, establishing inclusion and exclusion criteria for study participants is a standard and required practice. For example, inclusion criteria define as the key features of the target population that the evaluators will use to answer their research question (eg. demographic, and geographic characteristics of the targeted location in the two regions of Cameroon) should be considered. These are important criteria to understand the area of research and to get a better knowledge of the study population. Reversely, exclusion criteria cover features of the potential study participants who meet the inclusion criteria but present with additional characteristics that could interfere with the success of the evaluation or increase their risk for an unfavorable outcome (eg. characteristics of eligible individuals that make them highly likely to be lost to follow-up, miss scheduled appointments to collect data, provide inaccurate data, have comorbidities that could bias the results of the study, or increase their risk for adverse events). These criteria can be also considered to some extent as part of the cross-cutting themes, but still are not covered by the OECD-DAC evaluation criteria and framework, therefore can be become a challenge for evaluating quality of a science/research and performance evaluation.

Are four dimensions clear and useful to break down during evaluative inquiry (Research Design, Inputs, Processes, and Outputs)? 

A thorough review of the four dimensions shows that these are clear and useful especially when dealing with mixed methods approach involving both quantitative and qualitative methods and adequate indicators. Given that however context and rationale are always the best drivers of objectivity for the research design, research processes including collection of reliable and valid data/evidence to support decision-making process, it is very important that evaluators not only define the appropriate inclusion and exclusion criteria when designing a science research but also evaluate how those decisions will impact the external validity of the expected results. Therefore, on the basis of these inclusion and exclusion criteria, we can make a judgment regarding their impact on the external validity of the expected results. Making those judgments requires in-depth knowledge of the area of research (context and rationale), as well as of in what direction each criterion could affect the external validity of the study (in addition to the four dimensions).

Serge Eric