Using Statistics to Quantify Motivation: A Month-Long Experiment Tracking 10 Daily Goals.

Photo by Glenn Carstens-Peters on Unsplash

Overview

In February, I embarked on a journey to quantify my productivity by setting and tracking 10 daily goals, inspired by the SMART (Specific, Measurable, Achievable, Relevant, Time-bound) framework. This experiment was designed to measure my progress towards larger, more abstract ambitions, such as academic success, career direction, and extracurricular achievements. I thought to myself, if it was any month to start doing this, I should choose the month with the fewest days!

My goals ranged from academic tasks like solving LeetCode problems and preparing for the GRE, to career-oriented activities such as contributing to Medium articles and planning society events. Each day, I meticulously (or tried to be) logged my achievements, enabling me to observe patterns, identify productivity peaks and troughs, and assess my overall progress.

The rest of this article serves as a summary of my findings over this month-long endeavour, and hopefully will also motivate you, the reader, into a habit of systematic goal-tracking over a more extended period. By actively tracking and reflecting on your goals, you can gain a deeper understanding of your personal work habits, identify areas for improvement, and foster a more disciplined approach to achieving your ambitions. This process not only enhances your productivity but also empowers you with the insight to make informed adjustments to your goals and strategies. Ultimately, the discipline of systematic goal-tracking is a powerful tool for personal development, offering a structured approach to realising your full potential.

Who am I?

I am Peter Zhang, a student at UCL (University College London) pursuing a degree in Computer Science. I have devised this method of goal tracking to balance my university obligations and extracurricular responsibilities. To any students reading this article, I hope that I can inspire similar systems of routine in you, as I found it to be enormously useful.

The 10 Goals

Before diving into the insights gained from this month-long journey of meticulous goal tracking, let’s introduce the 10 goals that were the cornerstones of this endeavour. They were carefully selected to span across different areas of my personal and professional development:

1. 1 LeetCode Question Practice
2. 10 GRE quant questions
3. 1 day of vocab mountain
4. 1 day of GregMat lessons
5. 1.5 hours of homework/pre-reading according to schedule
6. 30 minute contribution towards publishing articles on Medium (etc)
7. 15 minute contribution towards planning of society events and other responsibilities
8. 45 minute contribution towards Systems Engineering (a UCL Computer Science class)
9. 1 hour dedication to consolidating past information from the start of the term (to review concepts with a higher level knowledge)
10. Solve 1 Jane Street brainteaser

The set of goals (on the right) represent realisations of my other longer-term, more abstract goals:

• Success in academics at UCL
• Success in career in the direction of software engineering, consultancy, or quantitative finance/research
• Success in Graduate School admissions, through improvements in extracurriculars, standardised tests, GPA etc.
• Success in society events and extracurriculars, in hosting events and organising workshops

My Results

Number of daily goal completions

Insights: Patterns over the Month Long Experiment

The goal tracking has proved invaluable in recognising patterns in work behaviour.

1. Observed a progressive increase in the achievement of daily goals throughout February. This uptrend is likely a result of implementing goal-setting strategies, supporting that this exercise in goal tracking has been effective in the medium term.
2. Significant day-to-day variance in goal achievements noted. This variability may be linked to the varying number of tasks and unique daily conditions — namely academic obligations and lecture contact hours
3. Sundays consistently show lower numbers of goal completions. This trend could stem from an underlying perception of Sundays as days of rest, hence explaining the lower levels of goal completion.

Insights from my February goals tracking

Descriptive Statistics

I complete 4 goals a day on average and most often, for days where I don’t complete as many goals, I make up for it on other days.

Histogram of the Distribution of Daily Goals Achieved Throughout February

• The data indicates an average completion rate of approximately 4.32 goals per day.
• The median value of goals completed daily stands at 4, suggesting a consistent achievement rate.
• The mean and median being similar suggest symmetrical data — i.e. that for days with less completions I have equal number of days with more completions — e.g. with the median of 4 goals complete every day, if there was a day where I complete only 2 goals, there will be a corresponding day where I complete 6 goals
• The mode being 4 also confirms that the most frequent number of daily goals completed is 4.
• The standard deviation is 1.56, which points to a moderate variability in daily goal completion

Goal Oriented Insights — Which are the most and least completed goals?

Goal completion rates in percentage points for February

Insight

The completion of goals overall depended most 1) whether the goal was an immediate objective to complete and 2) whether the task was interesting to me

A flowchart depicting my order of precedence for goal completion propensities

My order of precedence for my propensity to complete tasks were linked to the following three buckets that tasks may fall into:

• Most Often: Goals with direct and pressing relevance, especially academic or university tasks, were completed more frequently. For instance, homework assignments (Goal #5) and LeetCode problems (Goal #1) were prioritised.
• Somewhat Often: Goals of moderate urgency but still of personal interest were completed with medium frequency. This includes society work (Goal #7) and reviewing past information to consolidate knowledge (Goal #9).
• Least Often: Goals with less immediate relevance or lower personal interest were completed the least. An example is solving Jane Street brainteasers (Goal #10).

In Summary

1. Goal tracking has significantly improved my motivation to complete work and self-control in focusing on the tasks at hand
2. There exists intra-week fluctuations of levels of work over the different days, with Sunday being the least productive likely attributed to subliminal associations with rest
3. I am able to make up for lost work by compensating with higher workloads (of similar delta) on other days, centred around 4 tasks a day
4. My propensity to complete tasks are largely based on their level of urgency and my interest in the work

Improvements

Conclusion

In conclusion, my month-long journey of tracking 10 daily goals has been a profound learning experience. The data-driven approach provided me with invaluable insights into my work patterns, discipline, and overall productivity. The visualisation of my progress emphasised the importance of consistency, adaptability, and the undeniable value of reflecting on one’s daily practices. As I move forward, these lessons will be integral in refining my strategies for personal and professional development.

I encourage you to embark on your own journey of goal quantification. Whether it’s for a month or longer, the clarity it brings could be the catalyst for remarkable growth and achievement. Remember, the journey to achieving great things begins with a single step — or in this case, a single goal tracked and accomplished day after day.