Overview
David Brooks coined the term Dataism in a 2013 New York Times post. In short, he argues that, in a world of increasing complexity, relying on data could reduce cognative biases and “illuminate patterns of behavior we haven’t noticed yet”.
In that same vain, I began tracking my ‘personal happiness index’ - a daily score and associated summaries - with the hopes that I would be able to identify factors that influence my mood, both positively and negatively. Once mood factors have been identified, rituals & habits can be altered for optimum mood.
Theory, Assumptions & Procedure
Theory:
- happiness is a function of environmental inputs and personal expectations
Assumptions:
- ratings are taken in the morning of the following day
- notes describe (what I thnk to be) key events that took place the day prior
- key events are determined / defined by memorable events
- it is assumed that memorable events are only memorable because they have emotion tied to them
- it is assumed that emotion determines mood
Potential Improvements:
- blind entry of data such that previous entries do not inflence current entry
- improved accessibility to prevent backdated records or PM entries
Results
2019-10-20 to 2019-11-27
PowerBI Word Cloud used for word frequency and value
Conclusion
Quick review of work importance, frequency and daily score leads me to believe work has a large impact on my general day-to-day happiness. This makes intuitive sense giving societal constructs, specifically those for males, in the United States. Additionally, while the pared word is not listed, “with” is a high volume word generally associated with a persons name. This leads me to believe time with family & friends also has a larage impact on mood. Worth noting, confirmation bias is probable given these conclusions are in line with my original theory.