OVERVIEW OF METHOD
Like most complex analysis, causal loop diagramming is iterative. The steps outlined below are meant to serve as a high-level guide to the process rather than a strict sequencing.
Key steps in conducting a causal loop diagramming include:
1. DEFINE YOUR LEARNING QUESTION
The first step in developing a CLD is to establish what you are trying to better understand. Your goal may be to understand a system (e.g., healthcare in general) in its entirety or a sub-part of the system (e.g., prenatal healthcare). You may be trying to understand how a system/organization operates or characterize a context (i.e., problem space) to see how a phenomena or problem emerged and is sustained by related processes, stakeholder behaviors, and perceptions. Or, maybe the goal is to capture and convey a theory of change that underlies a new program or initiative. Whatever the goal may be, it should be established through consultations with key stakeholders before modeling efforts commence.
2. DEFINE CLD PARAMETERS
Since CLDs are often used to understand complex issues, modelling efforts can get overwhelming quickly unless proper parameters have been identified and agreed upon by key stakeholders. Some of the key parameters to consider include:
3. IDENTIFY STAKEHOLDERS
If the CLD incorporates information elicited from stakeholders, who will be included in the modeling effort needs to be determined. Once the high level categories of stakeholders (e.g., academics, practitioners, local farmers) associated with the issue of interest are determined, specific individuals who will represent each category need to be identified and contacted. One key consideration in this stage is to ensure that diverse stakeholders as well as diverse perspectives within each stakeholder category are represented during the data collection efforts.
4. COLLECT & MODEL DATA
During this step, a literature review is conducted to identify key variables and relationships relevant to what is being modelled. Literature reviews can include previous studies, program evaluations, government documents, statistics, newspaper articles, and any other documentation that relate to the identified learning question. A critical component of this literature review is to investigate behavior over time associated with the key variables identified. If data are to be collected from stakeholders or key informants, semi-structured interview questions and focus group questions should be prepared. Alternatively, stakeholders can be brought together in person for a real-time, facilitated discussion and participatory group modeling. If the CLD will be developed through participatory group modeling process, several sessions will be conducted to capture all relevant perspectives as well as emergent ideas and thoughts. During the first session, stakeholders can be presented with a simple, core feedback loop to kick-start discussions.
During the data collection process, some of the key questions considered include: What are the key variables, issues, forces, dynamics and outcomes essential to explain this system or problem? How do they relate to one another? What are some of the key cause-effect relationships, interactions and interdependencies? How can these relationships be reflected in terms of reinforcing (a series of relationships that appear to cause exponential growth or decline in a phenomena) and balancing loops (a series of relationships that appear to prevent change with a push in the opposite direction)? Which one of the effects are immediate and which ones are delayed? How do stakeholders perceive each other, their place in the system and key dynamics associated with the problem? What are some of the economic, social, political, and cultural norms and structures in place and how do they influence the operation of the system and key outcomes? Are there any real or perceived delays in cause-effect relationships identified?
Often data collection and CLD modelling happen simultaneously. As data accumulates and our understanding of key dynamics and forces evolve, mapping key variables and relationships begin.
Once a CLD is formed, its variables or relationships can be color coded to convey another layer of information. For example, variables associated with different domains (e.g., economic or social) can be colored differently; similarly, arrows (that represent relationships) can be color-coded to reflect different types of relationships (dependency, information flow, compliance, etc.).
CLD development is an iterative process. Before the analytic team and key stakeholders feel comfortable with the resulting CLD, the model will almost always go through several revisions and adjustments in light of new information and group learning. Similarly, once a baseline CLD is developed, it can periodically be updated to reflect the ways the system, local context, or problem may be changing as a result of program interventions or natural evolution.
5. ANALYZE CLD
Once a CLD is developed, it is time to take a step back and examine the model for original insights. Synthesis of different perspectives and information often reveals information that you cannot see by examining individual parts. A CLD lends itself to different types of qualitative assessment, including:
6. SHARE RESULTS
For the best results, CLDs should not be presented as a single page analytic product. Typically, a complex CLD can be presented in two ways: