The Nonprofit Incubator

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effective was it? Were there unintended side-effects? Who benefitted most? Who was harmed? Why did it work or not work? What lessons can be applied to other contexts, or if the program was scaled up? How cost-effective was the program? How does it compare to other programs designed to accomplish similar goals?" After randomized evaluations have been conducted on test interventions and have answered these chief questions, the results can be utilized by program designers and program implementers. Based on the research and considerations of context, policymakers can decide which interventions are best to scale up. Evidence-based social interventions maximize positive social outcomes and ensure that policies and programs are effective rather than harmful to the target population. Foreign aid interventions illustrate the necessity of both of these goals. For generations, the debate on foreign aid has been wrought with controversy, mostly because evidence has not been developed or analyzed to assess impact. For example, Duflo notes that we do not know whether Africa would be better off now if we had given more aid or less aid since 1970. Without supporting evidence, implementation of policies and interventions can be a waste of resources or worse, exacerbate social problems. Evidence-based practice (EBP) is an interdisciplinary approach to clinical practic that has been gaining ground following its formal introduction in 1992. It started in medicine as evidence-based medicine (EBM) and spread to other fields such as dentistry, nursing, psychology, education, library and information science. Its basic principles are that 1) all practical decisions should be made based upon research studies and 2) that these research studies are to be selected and interpreted according to some specific norms characteristic for EBP. Typically such norms disregard both theoretical and qualitative studies and consider quantitative studies according to a narrow set of criteria of what counts as evidence. If such a narrow set of methodological criteria are not applied, it is considered better just to speak instead of research based practice. Evidence-based behavioral practice (EBBP) "entails making decisions about how to promote health or provide care by integrating the best available evidence with practitioner expertise and other resources, and with the characteristics, state, needs, values and preferences of those who will be affected. This is done in a manner that is compatible with the environmental and organizational context. Evidence is research findings derived from the systematic collection of data through observation and experiment and the formulation of questions and testing of hypotheses". Empirically supported treatments (ESTs) in some clinical settings are defined as "clearly specified psychological treatments shown to be efficacious in controlled research with a delineated population" Social Policy <strong>The</strong>re are increasing demands for the whole range of social policy and other decisions and programs run by government and the NGO sector to be based on sound evidence as to their effectiveness. This has seen an increased emphasis on the use of a wide Page 40 of 89
ange of Evaluation approaches directed at obtaining evidence about social programs of all types. A research collaboration called the Campbell Collaboration has been set up in the social policy area to provide evidence for evidence-based social policy decisionmaking. This collaboration follows the approach pioneered by the Cochrane Collaboration in the health sciences. Using an evidence-based approach to social policy has a number of advantages because it has the potential to decrease the tendency to run programs which are socially acceptable (e.g. drug education in schools) but which often prove to be ineffective when evaluated. Data-Driven Resource Allocation Data-Informed Decision-Making (DIDM) DDDM refers to the collection and analysis of data to guide decisions that improve success. DIDM is used in education communities (where data is used with the goal of helping students) but is also applicable to (and thus also used in) other fields in which data is used to inform decisions. While data-driven decision-making is a more common term, data-informed decision-making is a preferable term since decisions should not be based solely on quantitative data. Most educators have access to a data system for the purpose of analyzing student data. <strong>The</strong>se data systems present data to educators in an over-thecounter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators’ data-informed decisionmaking. Decision making has long been a subject of study and given the explosive growth of Big Data over the past decade, it’s not surprising that data-driven decision making is one of the most promising applications in the emerging discipline of data science. In a recently published article, “Data Science and its Relationship to Big Data and Data- Driven Decision Making,” Foster Provost and Tom Fawcett define data-driven decision making as “the practice of basing decisions on the analysis of data rather than purely on intuition.” Equally succinctly, they view data science “as the connective tissue between data-processing technologies (including those for big data) and data-driven decision making.” Page 41 of 89
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