How to Use Theory of Change for Adaptive Monitoring, Evaluation and Learning

In my previous post on designing and implementing adaptive MEL frameworks, I discussed the importance of a program’s Theory of Change (TOC) in facilitating understanding among your team and your stakeholders on what change looks like. The biggest challenge facing most programs, however, is that TOC is often mistaken for another form or version of a results chain/log frame/results framework. While they are not mutually exclusive, they are not the same.

The important distinction is that the TOC needs to iterate the theory behind the change you are seeking to enable, not just the process. The process is iterated in a results chain that specifies the tangible results you are pursuing driven by the theory.

In an adaptive program, your results chain (i.e.: activities and outputs) can change depending on the initial results you are observing and the reflections that are being undertaken about what should happen next. The ‘what comes next’ reflections need to be based on something, and that ‘something’ is the theory in your Theory of Change.

Depending on the type of program you are working on, if you are implementing an adaptive MEL framework, you may or may not need a results chain as well as a TOC (for example, results chains are not essential for systems change or advocacy programs which tend to go through frequent adaptations as change emerges and the system evolves). However, in order to be adaptive, a TOC is essential. Why? Theory of Change can play the following important roles in an adaptive MEL Framework:

1. It provides a clear framework for ‘the change we want to see.’ A TOC helps you to understand what change looks like – for beneficiaries, for other stakeholders and the program. For example, does change look like improved policy and regulatory coherence? Does it look like more inclusive health and education services? Does it look like evidence-based planning?

When the ‘change’ is clear, work backwards to determine the ‘pathways’ to change. These pathways are the strategic directions you believe that your program needs to take to effect the change you are pursuing and achieve your outcomes. The pathways act as ‘boundaries’ in which the program will implement activities, providing enough space to adjust and adapt to changing context and priorities in order to ensure continued relevance and the sustainability of the change you are pursing.

2. It provides the rational for what needs to be measured vis-a-vis change you want to see. When you break down the outcomes and the change pathways into variables, you are able to determine how to measure progress towards your outcomes, even when activities or outputs need to be adapted (or even dropped) due to changes in your implementation context (see below). The variables guide indicator development as well as the development of progress markers (for those programs that work on systems change and don’t set specific output or outcome targets).

For example, I developed a TOC which, among other changes, determined that the key variables to change around policy coherence were recognition of the multi-sectoral nature of the issue, the need for shared policy objectives and opportunities to lessen ‘trade-offs’ between policies. In a separate project, the TOC focused on community resilience to conflict and the variables were broken down into the supply and demand for inclusive service delivery (including dispute resolution and access to justice) and sustainable livelihoods opportunities. In both cases, the variables formed the basis for outcome level indicators. These variables also help to ensure that activity planning remains strategic, even when activities change, and within the ‘boundaries’ of the pathways by assessing whether the results of proposed activities are directly aligned to any one or combination of variables identified.

3. It provides space to adapt and adjust activities (and strategy) within change pathways. We have all faced the challenge of projects operating entirely on results chains (logframes, results frameworks) and then the project strategy or outputs change, and the data which you have collected (and all of the work that went into that) becomes redundant. In a results chain, progress is often measured at the output level and then aggregated up – making adapting in the program difficult. When you utilise TOC as the main reference point for outcome measurement, adapting and adjusting activities has less impact on the way we measure progress towards outcomes. Regardless of whether activities and outputs change, as long as the revised or new activities are guided by the variables in your TOC, you can continue to generate evidence for your outcome indicators and assess progress towards your outcomes.

The development evaluation space is constantly changing – with new approaches and tools being developed, and innovation on how we use evaluation (and the models and frameworks we use to implement it) emerging at a fast pace. Thus, the above insights on how to use Theory of Change for adaptive programs serve only as guidance – for every program, for every organisation, for every community and for every country, external and internal factors will influence how a program is designed and how Theory of Change is/can/should be used.