Theory of Change vs Logic Models: Which One Comes First?
In this complex world of program evaluation research, two approaches are critical and dependent on each other: the Theory of Change (ToC) and Logic Models. As tools of strategic planning and evaluation, both offer complementary frameworks for understanding interventions. However, their differences often lead to confusion, especially in deciding which to apply first. For evaluators who often find themselves in the crosshairs of this debate, this article delves deeper into the distinctions, applications, and intersections of both models, with an emphasis on short-term evaluation projects.
Understanding the Basics
Theory of Change (ToC): The Theory of Change is essentially a narrative or story, presenting a comprehensive depiction of problem identification to its intended or ultimate outcome. It draws a detailed picture right from the intervention's commencement to its eventual outcomes, highlighting the pathways and the underlying assumptions. The ToC can assume any shape, even going sideways and providing understanding about the broader context, interactions, and the process at play.
Logic Models: Logic Models are more schematic. As per the W.K. Kellogg Foundation (2004), Logic Models provide a simplified picture of a program activities, processes, and outcomes, showing the logical links among the resources that are invested, the activities that take place, and the benefits or changes that result. In essence, they encapsulate how inputs lead to processes, outputs, outcomes, and impacts.
Image of Theory of Change/Logic Model
Which Comes First? Understanding the Sequence
ToC and logic models are complementary in nature, though the strategic application of ToC and Logic Models depends significantly on the purpose and scope of study or project:
When embarking on a large or complex project, where a deep understanding of context, stakeholder dynamics, and the intricacy of an intervention is required, initiating with the Theory of Change is plausible. ToC aids in understanding the broader context and the various pathways of change. Following this, a Logic Model can be extrapolated to distill the line details. ToC is comprehensive, expounds the theory and clarifies the assumptions whereas logic model depicts a visual summary or a snapshot of the proposed theory.
When an existing programme or intervention requires needs narrow evaluation or is limited to few objectives, then starting with Logic Model is appropriate. Also, if the need is more towards its performance evaluation or feedback loops, then again starting with the Logic Model might be advantageous. Its bottom-to-top or left-right design offers clarity on perceived relationships among resources, activities, and outcomes. Given the constraints of time and resources in short-term projects, Logic Models aids in strategic decision-making. However, for a more complex or deep understanding of context, players, and assumptions, ToC provides richer insights, even if it demands background research or ground truth.
Both the Theory of Change and Logic Models stand as invaluable assets for evaluators, and both are applied in a complementary fashion. At times, evaluators or researchers develops the initial Logic Models and then followed by the development of theory of change narrative. In other instances, there is a need to identify ‘root causes’ and capture in-depth information. ToCs then becomes the core and more nuanced than Logic Models. As Breuer et al. (2016) noted, ToC helps in understanding uncertainties by using a backward mapping approach. On the other hand, Logic Models offer a linear way to indicate the linkage between inputs and outcomes.
Choosing between the two is a matter of relevance and applicability. As practitioners, our choices should hinge not just on project goals but also on the depth, breadth, and context we aim to explore and understand.
W.K. Kellogg Foundation, (2004). Logic model development guide. Battle Creek, MI.
Breuer, E., Lee, L., De Silva, M., & Lund, C., M. et al, (2016). Using theory of change to design and evaluate public health interventions: a systematic review. Implementation Science, 11(1), 63.