No means to attribute to control areas and use before intervention in absence of econometric analysis to identify intervention. comparator group. additional effect of the intervention on outcomes, controlling for other factors. Random allocation helps Number of comparators likely to ensure similar be modest treatment and Primary Capture data population Identify comparator areas with control Research and business surveys. characteristics to the programme characteristics. areas. to Estimate Aggregation of data likely to Outcomes be a challenge and sample Capture data on key variables sizes would need to be using population and business large. survey. No means to attribute to Sample size would need to be intervention in absence of large for aggregation. comparator group. Number of comparators likely to be modest so analytical power limited. Observe beneficiaries to official Match beneficiaries to official Outcomes in datasets and observe datasets and observe change. Official change. Replicate in control groups and Datasets use econometric analysis to look No means to attribute to at the additional effect of intervention in absence of intervention on outcomes, comparator group. controlling for other factors. Primary Use of surveys and carefully Replicate survey analysis in Research designed interviews to control areas and use of determine change and statistical analysis to look at Level to Estimate to intervention. additional effect on intervention Outcomes on outcomes, controlling for other factors. Beneficiary Source: Regeneris Consulting the at Change Measure In order to facilitate CIE methods, it is important that grant recipients gather the following information as a minimum: Information on the beneficiaries which can enable them to be identified in administrative datasets such as the IDBR (such as the company registration number in the case of businesses);
No means to attribute to intervention in absence of comparator group. control areas and use econometric analysis to identify additional effect of the intervention on outcomes, controlling for other factors. Number of comparators likely to be modest before intervention. Random allocation helps ensure similar treatment and control characteristics. Primary Research to Estimate Outcomes Capture data population and business surveys. Aggregation of data likely to be a challenge and sample sizes would need to be large. No means to attribute to intervention in absence of comparator group. Identify comparator areas with characteristics to the programme areas. Capture data on key variables using population and business survey. Sample size would need to be large for aggregation. Number of comparators likely to be modest so analytical power limited. Measure Change at the Beneficiary Level Observe Outcomes in Official Datasets beneficiaries to official datasets and observe change. No means to attribute to intervention in absence of comparator group. Match beneficiaries to official datasets and observe change. Replicate in control groups and use econometric analysis to look at the additional effect of intervention on outcomes, controlling for other factors. Primary Research to Estimate Outcomes Use of surveys and carefully designed interviews to determine change and to intervention. Replicate survey analysis in control areas and use of statistical analysis to look at additional effect on intervention on outcomes, controlling for other factors. Source: Regeneris Consulting In order to facilitate CIE methods, it is important that grant recipients gather the following information as a minimum: Information on the beneficiaries which can enable them to be identified in administrative datasets such as the IDBR (such as the company registration number in the case of businesses);
Appendix%201%20-%20ESIF-GN-1-034%20ERDF%20Summative%20Assessment%20Guidance%20Appendices.docx