Coming into this semester, I had big ambitions for getting my data analysis done early and having plenty of time after I finished that to work on the actual writing. What I did not realize was that projects with large data sets require lots of time spent organizing and cleaning the data in order to get it ready for analysis. This required me to learn the relevant functions in R and took a lot of back and forth with my advisor—it was almost two months into the semester before I got my initial results! I had fallen for the planning fallacy.
This is very common, and it happens for projects big and small. I’m sure you have had many assignments that you thought would take an hour but ended up taking five. Even people with lots of experience at a task can succumb to the planning fallacy. The Sydney Opera House was supposed to be completed in 1963, but the picture above shows the progress in 1966. It was eventually completed in 1973—and it took more than ten times the original budget!
After I organized my data and ran the initial analysis, I still found myself taking longer on parts of my thesis project than I intended. Too often, I submitted class assignments minutes before the deadline—or even a little late. All of this took away from some of the enjoyment of writing a thesis. Don’t get me wrong, I still found the process greatly rewarding, but I made it more stressful than I had to. I urge you to learn from my mistakes and be more proactive at managing your time.
Here are four tips to avoid the planning fallacy:
- Meet with your advisor early and often.
Ideally, start the semester before you take V499 and meet at least every other week. Meeting with my advisor on a regular basis helped me avoid getting stuck on one thing for an extended period of time. Often, when I did not know how to do something in R, my advisor would know the solution or be able to point me in the right direction.
The other big thing my advisor helped with was laying out the steps of the process. Not having done a research project before, there were several steps my advisor suggested that I had not considered—for example, creating an organized project directory.
- Set SMART goals and have accountability for meeting them.
Your advisor will help you get a handle on the major tasks necessary to complete your thesis, which will be medium-term goals (probably tasks to complete before the next meeting with your advisor). But also set more immediate goals. Make a plan for each writing session and set a specific goal for the day. It is easy to get sidetracked and work on less urgent tasks otherwise. Be sure you share your medium-term goals with your advisor (and your writing group) for accountability. If it helps, also share your daily goals with someone—and at least write them down for accountability with yourself.
- Set aside more time than you think you’ll need in your writing schedule and stick to it.
Chances are, you will be busier at the end of the semester than you are at the beginning, so set ambitions goals for yourself at the beginning of the semester—like getting your initial data analysis done a month into the semester. That way, when you run into roadblocks, you’ll have more time to adjust. And if you get your analysis done early, you can always run follow-up analysis to better understand your results.
- On the first draft, don’t edit your work—just get the ideas out.
Professor Baggetta introduced us to this idea early in the semester, and I wish I had done it more. I often found myself looking at the little details too much, rather than making sure I got all of the main ideas on the page. Even as I write this blog, I can’t stop rewriting sentences. Do your best to get out of this habit. Editing comes later.
I wish you the best in this process. Be grateful for the opportunity to do undergraduate research—and the professors that help make it happen! Also, enjoy getting to know your classmates and learning about the cool research they are doing. Seek to help them where you can—after all, it takes a community to write an honors thesis!
P.S. If you use R for your data analysis, I recommend the book R for Data Science, which is available for free online. Even before you have your own data, you can familiarize yourself with important chapters and do the practice exercises.
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