The Science behind LifeMapping 

We use a range of linear and non-linear analytics to ‘discover, mirror and reflect’ the patterns in people’s interactions and relationships that they might not otherwise be able to see. 

We are all driven to find and tell stories because they have the power to transform our lives and help us make sense of complex events and circumstances. Our research is built around finding and sharing insights as peoples' lives change.

• Exploratory and confirmatory factor analysis is used to build reliability into our LifeMap factor model. Regression analysis is used to quantify the relationships between variables of interest – identifying important drivers. Regression trees (CART) are used to handle non-linearity in the data as change does not always follow in a straight line! Structural equation modelling (SEM) allows us to establish causality (and not merely correlation) between certain variables.
• Text analysis is used to discover the deeper meaning behind the words in people's stories - to gain insights into how they really think and feel, their mental maps and models and what guides their actions.
• Changes in life are often described as a 'journey.' But up to now, accurate maps showing the complex interactions between changes, actions, resources and the outcomes people achieve have not been available. We build multi-dimensional maps from people’s experiences to help them more effectively navigate change. The application of advanced non-linear methods such as Self-Organising Maps (SOMs) to ‘people-change’ data is innovative and breaks new ground.

LifeMap Research has research collaborations with research partners, universities, government science groups and global consulting companies.