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Impact evaluation - Wikipedia, the free encyclopedia. Impact evaluation assesses the changes that can be attributed to a particular intervention, such as a project, program or policy, both the intended ones, as well as ideally the unintended ones.? This involves counterfactual analysis, that is, . In other words, they look for the changes in outcome that are directly attributable to a program.? It has received increasing attention in policy making in recent years in both Western and developing country contexts. The key challenge in impact evaluation is that the counterfactual cannot be directly observed and must be approximated with reference to a comparison group. There are a range of accepted approaches to determining an appropriate comparison group for counterfactual analysis, using either prospective (ex ante) or retrospective (ex post) evaluation design. Prospective evaluations begin during the design phase of the intervention, involving collection of baseline and end- line data from intervention beneficiaries (the . Retrospective evaluations are usually conducted after the implementation phase and may exploit existing survey data, although the best evaluations will collect data as close to baseline as possible, to ensure comparability of intervention and comparison groups. There are five key principles relating to internal validity (study design) and external validity (generalizability) which rigorous impact evaluations should address: confounding factors, selection bias, spillover effects, contamination, and impact heterogeneity. Confounding factors are therefore alternate explanations for an observed (possibly spurious) relationship between intervention and outcome. Selection bias, a special case of confounding, occurs where intervention participants are non- randomly drawn from the beneficiary population, and the criteria determining selection are correlated with outcomes. Unobserved factors, which are associated with access to or participation in the intervention, and are causally related to the outcome of interest, may lead to a spurious relationship between intervention and outcome if unaccounted for. Self- selection occurs where, for example, more able or organized individuals or communities, who are more likely to have better outcomes of interest, are also more likely to participate in the intervention. Endogenous program selection occurs where individuals or communities are chosen to participate because they are seen to be more likely to benefit from the intervention. Ignoring confounding factors can lead to a problem of omitted variable bias. In the special case of selection bias, the endogeneity of the selection variables can cause simultaneity bias. Spillover (referred to as contagion in the case of experimental evaluations) occurs when members of the comparison (control) group are affected by the intervention. Contamination occurs when members of treatment and/or comparison groups have access to another intervention which also affects the outcome of interest. Impact heterogeneity refers to differences in impact due by beneficiary type and context. High quality impact evaluations will assess the extent to which different groups (e. The degree that results are generalizable will determine the applicability of lessons learned for interventions in other contexts. Impact evaluation designs are identified by the type of methods used to generate the counterfactual and can be broadly classified into three categories . 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This paper describes the conceptual model that underlies the International Tobacco Control Policy Evaluation Project (ITC Project), whose mission is to measure the psychosocial and behavioural impact of key policies of the Framework Convention on Tobacco. These evaluation designs are referred to as randomized control trials (RCTs). In experimental evaluations the comparison group is called a control group. When randomization is implemented over a sufficiently large sample with no contagion by the intervention, the only difference between treatment and control groups on average is that the latter does not receive the intervention. Random sample surveys, in which the sample for the evaluation is chosen randomly, should not be confused with experimental evaluation designs, which require the random assignment of the treatment. The experimental approach is often held up as the . It is the only evaluation design which can conclusively account for selection bias in demonstrating a causal relationship between intervention and outcomes. Randomization and isolation from interventions might not be practicable in the realm of social policy and may be ethically difficult to defend. Bamberger and White (2. Methodological critiques have been made by Scriven (2. Other problems include the often heterogeneous and changing contexts of interventions, logistical and practical challenges, difficulties with monitoring service delivery, access to the intervention by the comparison group and changes in selection criteria and/or intervention over time. The Vermont Department of Environmental Conservation’s mission is to preserve, enhance, restore and conserve Vermont’s natural resources and protect human health for the benefit of this and future generations. The DEC, along with the Department of Fish and.Thus, it is estimated that RCTs are only applicable to 5 percent of development finance. Quasi- experimental methods include matching, differencing, instrumental variables and the pipeline approach; they are usually carried out by multivariate regression analysis. If selection characteristics are known and observed, they can be controlled for to remove the bias. Matching involves comparing program participants with non- participants based on observed selection characteristics. Propensity score matching (PSM) uses a statistical model to calculate the probability of participating on the basis of a set of observable characteristics and matches participants and non- participants with similar probability scores. Regression discontinuity design exploits a decision rule as to who does and does not get the intervention to compare outcomes for those just either side of this cut- off. Difference- in- differences or double differences, which use data collected at baseline and end- line for intervention and comparison groups, can be used to account for selection bias under the assumption that unobservable factors determining selection are fixed over time (time invariant). Instrumental variables estimation accounts for selection bias by modelling participation using factors (. The assumption is that as they have been selected to receive the intervention in the future they are similar to the treatment group, and therefore comparable in terms of outcome variables of interest. However, in practice, it cannot be guaranteed that treatment and comparison groups are comparable and some method of matching will need to be applied to verify comparability. Non- experimental design. The method used in non- experimental evaluation is to compare intervention groups before and after implementation of the intervention. Intervention interrupted time- series (ITS) evaluations require multiple data points on treated individuals before and after the intervention, while before versus after (or pre- test post- test) designs simply require a single data point before and after. Post- test analyses include data after the intervention from the intervention group only. Non- experimental designs are the weakest evaluation design, because to show a causal relationship between intervention and outcomes convincingly, the evaluation must demonstrate that any likely alternate explanations for the outcomes are irrelevant. However, there remain applications to which this design is relevant, for example, in calculating time- savings from an intervention which improves access to amenities. In July 2015, EPA published the 2015 underground storage tank regulation and the 2015 state program approval regulation. Read more about the revised regulations. Access plain language publications about the revised regulations. EPA has developed resources to help UST owners and operators prepare for. In addition, there may be cases where non- experimental designs are the only feasible impact evaluation design, such as universally implemented programmes or national policy reforms in which no isolated comparison groups are likely to exist. Biases in estimating programme effects. This particular research design is said to generally be the design of choice when it is feasible as it allows for a fair and accurate estimate of the program. The main problem though is that regardless of which design an evaluator chooses, they are prone to a common problem: Regardless of how well thought through or well implemented the design is, each design is subject to yielding biased estimates of the program effects. These biases play the role of exaggerating or diminishing program effects. Not only that, but the direction the bias may take cannot usually be known in advance (Rossi et al., 2. These biases affect the interest of the stakeholder. Furthermore, it is possible that program participants are disadvantaged if the bias is in such a way that it contributes to making an ineffective or harmful program seem effective. There is also the possibility that a bias can make an effective program seem ineffective or even as far as harmful. This could possibly make the accomplishments of program seem small or even insignificant therefore forcing the personnel and even cause the program. Not only are the stakeholders mostly concerned, but those taking part in the program or those the program is intended to positively affect will be affected by the design chosen and the outcome rendered by that chosen design. Therefore, the evaluator. Unfortunately, not all forms of bias that may compromise impact assessment are obvious (Rossi et al., 2. The most common form of impact assessment design is comparing two groups of individuals or other units, an intervention group that receives the program and a control group that does not. The estimate of program effect is then based on the difference between the groups on a suitable outcome measure (Rossi et al., 2. The random assignment of individuals to program and control groups allows for making the assumption of continuing equivalence. Group comparisons that have not been formed through randomization are known as non- equivalent comparison designs (Rossi et al., 2. Selection bias. This is known as selection bias (Rossi et al., 2. ICES reports offer a general comparability of the years and levels of study necessary to complete a program. In all cases, ICES recommendations are set by considering several factors including: The minimum academic credential one must hold in order to be admitted to the program. The level and duration of the program. The program of study to which the credential provides access in the country of origin. The recognition of the institution and program. The Scope and Limits of an ICES Report. ICES follows the standard methodology used in evaluation services throughout Canada and the United States and we apply this methodology consistently to all of our clients. ICES does not evaluate course content or make a judgment on the quality of individual credentials. ICES is not in a position to evaluate the merits of each individual's performance or knowledge (other than on the basis of grades). Instead, ICES offers a service which authenticates documents, conducts research based on well established methodologies, and then issues an evaluation report that provides a general recommendation of the comparability of education earned outside of Canada to the Canadian education system. 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