Mind Mapping: Tracing the Evolution of Causal Mapping from Cognitive Psychology to Research

Causal mapping visually represents the relationships between various factors or variables and their effects on each other. Causal mapping has roots in cognitive psychology, where researchers began using diagrams and networks to represent mental models and causal relationships. These early efforts were inspired by the work of psychologist Jean Piaget, who emphasised the importance of understanding how individuals perceive cause-and-effect relationships in their environment. He proposed that individuals go through stages of cognitive development in which they progressively develop more sophisticated ways of thinking and understanding the world. 

A causal map is a model of causal relationships, in the form of a directed graph in which the items (nodes, elements ) are linked by arrows, together with translation rules that tell us how to interpret the arrows, namely that an item with one or more arrows pointing to it is in some sense causally influenced by the item(s) at the start of those arrows 

(Powell, S. 2019). 

As cognitive psychology evolved, so did the techniques and theories of causal mapping. Researchers began to use computer-based tools to create more sophisticated and dynamic models of causal relationships, allowing for more complex and nuanced analyses of how different factors interact with and influence each other.

Today, causal mapping has expanded beyond cognitive psychology and is widely used in various fields, including organisational development, strategic planning, and decision-making. Researchers and practitioners use causal mapping techniques to identify key drivers of behaviour, predict outcomes, and design interventions to improve performance. Causal mapping visually represents relationships between various factors or variables influencing a particular outcome or event. It involves identifying the causal links between different elements and depicting them in a structured manner, often using diagrams or graphs.

What is causal research design?

Causal research (1) is sometimes called an explanatory or analytical study. It delves into the fundamental cause-and-effect connections between two or more variables. Researchers typically observe how changes in one variable affect another related variable. Examining these relationships gives researchers valuable insights into the mechanisms that drive the phenomena they are investigating (2).

Capturing Strategic Data Using Causal Mapping

To capture strategic data using causal mapping in development projects, researchers can follow these steps:

1. Identify the key variables: Identify the key variables or factors relevant to the development project. These include economic, social, political, environmental, or technological factors likely to impact the project outcomes.
2. Define the relationships: Once the key variables have been identified, researchers can define the relationships between these variables. This entails determining how each variable affects or is influenced by other variables in the system.
3. Construct the causal map: Researchers can construct a causal map representing the relationships between the variables using a visual tool such as a causal loop diagram or a concept map. This can be done manually or with the help of software tools such as CmapTools or Kumu.
4. Validate the causal map: It is important to validate it with stakeholders or domain experts to ensure it accurately represents the system dynamics and interactions. This can also uncover any gaps or inconsistencies in the data.
5. Analyse and interpret the causal map: Once validated, researchers can analyse it to identify key patterns, feedback loops, and causal relationships within the system. This can help uncover underlying mechanisms driving the project outcomes and identify potential leverage points for intervention.

Overall, the steps of causal maps help researchers to visually map out complex relationships, identify key variables, and ultimately organise their thoughts to develop theories or hypotheses that can be tested and refined through empirical research.

Causal Mapping Tools