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  1. Develop programme theory / theory of change

    Logic model, Program logic, Programme logic, Causal model, Results chain, Intervention logic, ToC

    A programme theory explains how an intervention (a project, a programme, a policy, a strategy) is understood to contribute to a chain of results that produce the intended or actual impacts. 

    It can include positive impacts (which are beneficial) and negative impacts (which are detrimental). It can also show the other factors which contribute to producing impacts, such as context and other projects and programmes.

    Different types of diagrams can be used to represent a programme theory.  These are often referred to as logic models, as they show the overall logic of how the intervention is understood to work.

  2. Check the results are consistent with causal contribution


    One of the tasks involved in understanding causes is to check whether the observed results are consistent with a cause-effect relationship between the intervention and the observed impacts.

  3. Identify potential unintended results


    Many evaluations and logic models only focus on intended outcomes and impacts - but positive or negative unintended results can be important too.

    Use these options before a program is implemented to identify possible unintended outcomes and impacts, especially negative impacts (that make things worse not better) that should also be investigated and tracked.

    Make sure your data collection remains open to unintended results that you have not anticipated by including some open-ended questions in interviews and questionnaires, and by encouraging reporting of unexpected results.

  4. Compare results to the counterfactual


    One of the three tasks involved in understanding causes is to compare the observed results to those you would expect if the intervention had not been implemented - this is known as the 'counterfactual'.

  5. Investigate possible alternative explanations


    All impact evaluations should include some attention to identifying and (if possible) ruling out alternative explanations for the impacts that have been observed.

  6. Report and support use


    From the first step of the evaluation process, even though it may be one of the last evaluation tasks, explicitly discuss the content, sharing, and use of reports during the initial planning of the evaluation and return to the discussion thereafter. Most importantly, identify who your primary intended users are. Use of the evaluation often depends on how well the report meets the needs and learning gaps of the primary intended users.

  7. Extrapolate findings


    An evaluation usually involves some level of generalising of the findings to other times, places or groups of people. 

  8. Probability Sampling


    How will you sample?

  9. Understand Causes


    Most evaluations require ways of addressing questions about cause and effect – not only documenting what has changed but understanding why.   

    Impact evaluation, which focuses on understanding the long-term results from interventions (projects, programs, policies, networks and organisations), always includes attention to understanding causes.  

    Understanding causes can also be important in other types of evaluations.  For example in a process evaluation, there often needs to be some explanation of why implementation is good or bad in order to be able to suggest ways it might be improved or sustained. 

    In recent years there has been considerable development of methods for understanding causes in evaluations, and also considerable discussion and disagreement about which options are suitable in which situations. 

  10. Manage data


    Good data management includes developing effective processes for consistently collecting and recording data, storing data securely, backing up data, cleaning data, and modifying data so it can be transferred between different types of software for analysis.