contribution analysis evaluation

Using specific examples from evaluation practice in a U.S. policy context over the past two years, this brief explores the suitability and application of contribution analysis for advocacy evaluation and offers guidance for evaluators considering this approach. If the theory that works for both then this is strong support for the theory (if it is the favored theory, or other), Among the FPs and FNs, the selected instance should be as similar as possible to the TP instance in respect to all other attributes other that those present in the theory conditions (FP), and theory outcome (FN) respectively, Only after careful selection of instances to be compared should effort then go into identifying and testing alternative theories. Yet applications of CA for this purpose are limited, and methods are needed to strengthen contribution claims and ensure CA is practical to implement. ANALYSIS. What conditions are needed to make this type of program succeed? Various ways of doing evaluation in ways that support democratic decision making, accountability and/or capacity. 2. Decide who will conduct the evaluation, 5. effect of bureaucratic administration on change X or y. One result of the widespread interest in theory-based approaches is that there is … Why has the result occurred? Confirming or revising a programme’s theory of change – including its logic model. such as realist evaluation ( Pawson & Tilley, 1997 ) and developmental evaluation (P atton, 2010), ca n be particularly eff ective in this regard (G overnment of Canada, 2012 ). The links in the theory of change also need to be assessed. It is useful to first use existing evidence such as from past related evaluations or research, and from prior monitoring, to test the theory of change. A research design that focuses on understanding a unit (person, site or project) in its context, which can use a combination of qualitative and quantitative data. These steps are: 1. More recently, contribution analysis (CA) has become widely known as a theory-based evaluation … A particular type of case study used  to create a narrative of how institutional arrangements have evolved over time and have created and contributed to more effective ways to achieve project or program goals. Contribution analysis helps to confirm or revise a theory of change; it is not intended to be used to surface or uncover and display a hitherto implicit or inexplicit theory of change. The theory of Contribution Analysis (CA) as a method of evaluating complex programs has been written about extensively and has evolved considerably since it was first introduced by John Mayne in 1999. Having identified where the contribution story is less credible, additional evidence is now gathered to augment the evidence in terms of what results have occurred, how reasonable the key assumptions are, and what has been the role of external influences and other contributing factors. Six steps are taken to produce a credible contribution story: Determine the specific questions being addressed. Develop the postulated theory of change and the risks to it, including rival (alternative) explanations. In Evaluating the Complex, R. Schwartz, K. Forss, and M. Marra (Eds. The theory of change must include the assumptions made in the results chain and the inherent risks as well as external influences such as donor pressure, influences of peers and resourcing levels. Contribution analysis is used in estimating how direct and variable costs of a product affect the net income of a company. The Success Case Method (SCM) involves identifying the most and least successful cases in a program and examining them in detail. Investigate possible alternative explanations, 1. Approach primarily intended to clarify differences in values among stakeholders by collecting and collectively analysing personal accounts of change. It offers a step-by-step approach designed to help managers, researchers, and policymakers arrive at conclusions about the contribution their program has made (or is currently making) to particular outcomes. A non-experimental impact evaluation method that holds promise is contribution analysis. To address this challenge, the National Institute for Occupational Safety and Health program evaluators used a modified version of contribution analysis (CA) to evaluate two research programs. 2. Where are the main weaknesses in the story? Combining qualitative and quantitative data, 1. With this information, you will be able to assemble your contribution story that expresses why it is reasonable to assume that the actions of the program have contributed (in some fashion, which you may want to try and characterize) to the observed outcomes. This theory of change should lead to a plausible association between the activities of the program and the outcomes sought. Develop planning documents for the evaluation or M&E system, 8. Review evaluation (do meta-evaluation), 2. A stakeholder involvement approach designed to provide groups with the tools and knowledge they need to monitor and evaluate their own performance and accomplish their goals. Define ethical and quality evaluation standards, Document management processes and agreements, Develop planning documents for the evaluation or M&E system, Develop programme theory / theory of change, activities, outcomes, impacts and context, Combine qualitative and quantitative data, Check the results are consistent with causal contribution, Investigate possible alternative explanations, Sustained and emerging impacts evaluation (SEIE), Technology and evaluation in insecure settings, Evaluation practice in Aboriginal and Torres Straight Islander settings, Addressing attribution through contribution analysis: using performance measures sensibly, Contribution Analysis: An approach to exploring cause and effect,, Closing the series on participation in evaluation, Still Hesitating? 'http':'https';if(!d.getElementById(id)){js=d.createElement(s);;js.src=p+"://";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); Select a content type to filter search results: The content of this field is kept private and will not be shown publicly. Risks and assumptions are labelled as [O] over which the intervention has no or very little influence, or [I], wh… But feel free to point out the error of my ways :-). (2008) Contribution Analysis: An approach to exploring cause and effect, ILAC methodological brief, available at, False Negatives, where the conditions described by the favored theory are not present, but the expected outcome is, False Positives, where the conditions described by the favored theory are  present, but the expected outcome is not, Additional True Positives, where conditions described by the favored theory are present, and the expected outcome is. Reducing uncertainty about the contribution the intervention is making to the observed results. 3. Contribution analysis is used in estimating how direct and variable costs of a product affect the net income of a company. Have a look at this blog posting of mine on the subject. What evidence (information from performance measures and evaluations) is currently available about the occurrence of these various results? I am tempted to answer my own question: the very fuzzy description of the sort of claims that would be an acceptable result of such analysis. Augmenting evidence can include the collection of additional, new data such as from surveys, field visits, administrative data, focus groups, national statistical data, etc. Reducing uncertainty about the contribution the intervention is making to the observed results. Has the program made an important contribution to the observed result? These often are not that useful because they treat the program as a black box and don’t get at the fact that there are usually many causes involved. Using a generative perspective on causality to infer that a program made an important contribution to an expected result that has been observed, contribution analysis argues that a reasonable contribution causal claim can be made if: Some issues might arise when taking this approach with regards to: Risks and assumptions are labelled as [O] over which the intervention has no or very little influence, or [I], where the intervention can (should) have an influence, direct or indirect, or [C] where the intervention should be able to directly control.

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