Welcome to ThoughtSpace.
ThoughtSpace is a Python-based toolbox for analysing experience sampling data via Principal Components Analysis (PCA) to identify common "patterns of thought".
This readme is a Beginners guide to using ThoughtSpace that assumes little-to-no prior knowledge of coding and GitHub.
It does assume you already have a GitHub account set up. So, if you don't, create one first!
The guide will take you through installation and your first analysis, with examples:
If you have the necessary interpreter and programming steps set up, you can get into preparing and running ThoughtSpace.
If you're new to GitHub, here's a guide to forking and cloning the ThoughtSpace repository:
Once you've set up a local (and forked) ThoughtSpace repository on your computer, you can set up a virtual environment for using ThoughtSpace:
After you've generated your components, it's important to get a sense of how robustly they represent your data and how well they generalize across types of situations (e.g., different sampling environments, different participant populations, etc.).
Usage of rhom primarily involves four functions that assess both component reliability and generalizability in a few different ways. Take a look at the guides below, organized by the questions each function targets, and be sure to look at the example script provided in the /examples folder!
How robustly do my components represent my data?
How similar are the components produced by different situations?
How representative are the components I get when I combine data from different situations?
Answering these questions requires a metric that provides some indication of the similarity between two components (e.g., generated from different halves of the same dataset, generated from separate datasets with the same measure, etc.). To do so, the rhom module leverages two metrics of component similarity: